CHAPTER 2 The Concept of Intensive Land Use345749/S... · 2019. 10. 9. · Laura Sonter Chapter 2: The Concept of Intensive Land Use 11 Figure 2.1: Simplified conceptual model of

Laura Sonter Chapter 2: The Concept of Intensive Land Use 9

CHAPTER 2

The Concept of Intensive Land Use

Objective 1: To propose and develop the concept of ‘intensive land use’, using mining as an

example.

Preface: The purpose of this Chapter was to conduct a review of the Land Change literature.

Results illustrate a major knowledge gap exists within this research field – small-scale land uses are

given little attention in studies seeking to understand processes of land use change. The major

contribution to the thesis is a conceptualization of the pathways through which small-scale land uses

operate ‘intensively’, to drive more extensive land use change at the regional scale: (1) modifying

spatial landscape attributes and (2) altering underlying forces driving the expansion of other land

uses. These conceptual pathways are discussed in detail in Chapter 7; the purpose Chapter 2 was to

show these pathways exist in multiple forms in QF. I illustrate the significance of intensive land

uses, using the case study of mining in the Iron Quadrangle. Following this, Chapters 3 builds on

these ideas by quantifying the spatial extent of regional land use change driven by global demand

for steel.

Acknowledgements: The ideas contained within this paper were initially presented at the AAG

meeting in Los Angeles in March 2013 and were subsequently published in the Journal of Land Use

Science. I would like to thank AAG participants for discussion of these ideas, and the manuscript

reviewers commenting on a previous draft. I would also like to thank Greg Keir and James Morrell

for commenting on written material.


A Land System Science meta-analysis suggests we underestimate intensive land

uses in land use change dynamics1

Abstract: A meta-analysis of the Land System Science literature identified that small-scale land

uses currently receive little attention in studies seeking to understand land use change dynamics.

We conceptualized two ways in which small-scale land uses operate to indirectly drive more

extensive land use change: 1) through modifying spatial landscape attributes and 2) through altering

underlying forces driving the expansion of other land uses. We then propose the concept of

‘intensive land uses’, those that occupy a small proportion of the landscape but indirectly drive land

use change dynamics through their operation. Our discussion highlights that, with the exception of

roads, we currently underestimate the importance of intensive land uses in the literature and we

illustrate this with a case study of a commonly disregarded intensive land use: mining. We conclude

that the inclusion or exclusion of land uses from analyses should extend beyond quantifying their

land use area and instead incorporate an understanding of how land uses operate within their

regional context. Finally we present three future research opportunities to incorporate intensive land

uses into analyses and models of land use change dynamics.

Keywords: Land Change Science, LUC, Leximancer, mining, roads, text analysis

2.1 Introduction

Understanding land use change dynamics is essential to develop and implement land management

plans and policies capable of achieving long-term resource management goals (Turner et al. 2007).

In theory, these dynamics can be conceptualized using simplified conceptual frameworks or models

of driving forces, actors, land uses and system feedbacks (Geist et al. 2006; Hersperger et al. 2010),

as shown in Figure 2.1. Such models can then be calibrated with real-world information to allow

projections of future trajectories under different policy options (Alcamo et al. 2006; Reid et al.

2006).

1 Sonter et al. (2013) A Land System Science meta-analysis suggests we underestimate intensive land uses in land use

change dynamics. Journal of Land Use Science DOI: 10.1080/1747423X.2013.871356

http://dx.doi.org/10.1080/1747423X.2013.871356


Figure 2.1: Simplified conceptual model of land use change dynamics

Obtaining the information to comprehensively populate these models, however, is often a

complicated task. This is because land use change is driven by a large number of socio-economic

and environmental forces, which interact and operate over variable spatial and temporal scales

(Geist & Lambin 2002). These influence the decision making of many actors and groups of actors,

who use and modify landscapes in different, often competing ways. The impacts of these decisions

can then feed back to alter future dynamics through non-linear and complex pathways (Verburg et

al. 2006).

In this regard, the challenge is to reduce land use change dynamics to a sufficiently simple state, so

as to avoid becoming lost in (potentially) unimportant detail, yet capture enough information about

these dynamics to develop policies capable of achieving management goals (Rounsevell et al.

2012). The process of simplification involves identifying the most influential components (driving

forces, actors, land uses and feedback mechanism in land use change dynamics at a defined spatial

and temporal scale. Doing this relies on the assumption that incorporating a small number of key

components into an analysis will sufficiently capture a region’s land use change dynamics.

In this context, the purpose of this paper is to investigate how land use change dynamics have been

simplified and studied in the past. Previous studies have put forward methods to determine the most

influential driving forces (Hersperger & Burgi 2009), actors and their behavior (Parker et al. 2003;

Robinson et al. 2007); however, little justification is often given to the inclusion or exclusion of

specific land uses from the analysis extent. Although incorporating the most influential land uses


into an analysis may seem trivial (since land use is the result of driving forces and actors and can be

directly observed), capturing only a limited subset of uses also limits the spatial and temporal

feedbacks (as illustrated in Figure 2.1) that can be incorporated into, and explored throughout, the

analysis. Additionally, without incorporating these feedbacks, simulations of long-term trajectories

of change may rapidly become inaccurate.

Therefore, the specific objectives here were to determine: 1) which land uses have been included in

studies of land use change dynamics in the past; 2) why these land uses were selected over others;

and 3) what the assumptions and potential implications of these decisions were. To do this, we

conducted a meta-analysis of papers published in the field of Land System Science.

2.2 Meta-analysis methods

The analyzed literature was collected in October 2012 from Web of Knowledge databases using the

search terms: ‘driving force*’ AND either ‘land use change’ OR ‘land cover change’. Using these

search terms (specifically the incorporation of ‘driving force*’) captured studies seeking to

understand land use change dynamics, while excluding those that only quantified land use change.

The search results were further refined to papers published in peer-reviewed journals since 2002.

This produced 388 abstracts, which were downloaded and imported into Leximancer for analysis.

Leximancer (Version 4; www.leximancer.com) is a content analysis program used to quantify and

analyze trends in a body of text (Cretchley et al. 2010; Smith & Humphreys 2006). In this study,

Leximancer was used to identify frequently (re)occurring concepts in the defined literature.

Concepts represent a collection of words (referred to as the ‘thesaurus’) that travel together

throughout text and aggregate to have meaning. To identify concepts, Leximancer uses seed words

(frequently occurring words) to define an initial concept list. This list is then updated through an

iterative learning process. To improve the model output, the list of seed words was edited prior to

analysis. Frequently occurring but irrelevant seeds were removed and seeds with similar meanings

were merged to avoid duplication of concepts, as shown in Table 2.1.

Leximancer was then used as a platform to investigate the meaning and the significance of these

concepts, by tracing concepts back to the papers from which they emerged.

http://www.leximancer.com/


Table 2.1: Seed words that were removed or merged prior to the concept analysis

Removed seed words Merged seed words

Analysis, based, data, different,

during, important, major, paper,

research, reserved, results,

rights, study, used

(area + areas), (change + changes), (driving + forces +

factors), (increase + increased), (images + remote +

sensing), (land-use + land + landscape + land-cover + use

+ cover), (model + models)

2.3 Meta-analysis results

The meta-analysis identified more than 25 meaningful concepts from the Land System Science

literature. Table 2.2 shows the ten most frequently occurring concepts and their corresponding

thesaurus. It is expected that the concepts of ‘land’ and ‘driving’ would emerge at the top of this

list, since these terms were used to define the input literature. Analyzing the remaining eight

concepts, however, led us to the conclusion that, over the past decade, preference has been given to

studying land uses that occur at extensive spatial scales.

Table 2.2: Ten most frequently occurring concepts that emerged from the text meta-analysis

Concept Count Thesaurus (in order of importance to the concept)

land 1789 land, change, use, changes, forces, cover, development, landscape,

land-use

driving 735 driving, forces, factors, drivers, driven, mechanism, actors, control,

proximate, roles, known, regression

area 556 area, areas, occupied

agriculture 530 agricultural, cultivated, cropland, agriculture, farmland, farming,

arable, converted, crop, crops, farm, China

forest 297 forest, forests, fragmentation, forestland, rain, regrowth, shrubland

model 259 Model, models, CA, automata, Clue-S, simulates, agent-based,

automation, explanation, cellular, neighborhood, validated

urban 257 urban, metropolitan, motorway, highways, industrial, sprawl

patterns 237 patterns, pattern, heterogeneous, explaining, quantifying

images 235 images, sensing, remote, classification, Landsat, TM, ETM, image,

imagery, classified, sensed, remotely, supervised, digital, MSS, remote,

satellite

spatial 223 spatial, exhibited, broadly, embedded, geography, grid, predictions,

1km, cellular

This result is illustrated by the three land uses that formed concepts in Table . : ‘Agriculture’,

‘Forest’ and ‘Urban’. The concept of agriculture included both cropping and grazing activities,


which together occupy more than 40% of the global land surface area (Foley et al., 2005). Almost

the entire analyzed literature focused on understanding the dynamics of agricultural land uses. This

focus was primarily justified by reporting the spatial extent of these land uses and describing the

projected expansion of agricultural practices driven by changing global demographics. Following

this, justification often involved identifying the environmental impacts of agricultural practices,

such as deforestation. Forests occupy approximately 30% of the land surface at the global scale

where deforestation is also an extensive process that occurs at about 7–8 Mha per year (FAO 2012).

Within the literature, the major focus of deforestation research was undertaken on tropical forests,

because these were the most threatened by processes of extensive agricultural expansion (Lambin et

al. 2003). Studies on urbanization and urban sprawl were mostly done in the context of

development in China with their rapid rural to urban migration, which has entailed a rapid and

extensive expansion of their spatial urban footprint (for example, some highly cited papers include:

(Ma 2002; Weng 2002; Zhou et al. 2004).

This focus on extensive land uses suggests that the ‘area of impact’ is frequently used to identify the

most influential land uses in land use change dynamics. The importance of, and focus on, ‘area’ is

illustrated by the remaining concepts in Table 2.2. The third-most frequent concept of ‘Area’

illustrates that most studies initially quantify the area impacted by land uses, then emphasize the

importance of this value in deciding which land uses to include in analyses. Similarly, the

thesauruses of the remaining concepts (‘Model’, ‘Patterns’, ‘Images’ and ‘Spatial’) illustrate the

relative importance of spatial land use change dynamics, rather than an understanding of how non-

area related factors, such as resource production and consumption, may be important. Additionally,

the concepts of ‘images’ and ‘spatial’ illustrate the reliance on remote sensing data to quantify and

analyze land use change, rather than incorporating other statistical information, such as land

management practices, to aid interpretation of land use operation.

In comparison, land uses that occupied a relatively small proportion of the landscape received

relatively less attention as influential components of land use change dynamics. For example, in the

literature analyzed, mine expansion was almost always ignored; only 8 of 388 analyzed abstracts

explicitly referred to mining land uses. In studies that took place in regions containing mining

operations, the decision to exclude this land use was generally justified with several similar

statements. Firstly, remote-sensing images were considered unable to detect the disturbance

associated with land uses at such a small spatial scales, especially when the spatial extent of the

analysis was broad (achieving a broad analysis extent was repeatedly seen as an important aspect of

a successful case study). Secondly, the direct impacts of mining were often considered to be

‘localized’ since they did not aggregate to a meaningful or significant national or global scale. Other


small-scale land uses, such as piggeries (referred to in 3 abstracts), feedlots (5 abstracts),

manufacturing (3 abstracts) and aquiculture (1 abstract) were also rarely included in studies of land

use change dynamics.

2.4 Discussion

Our meta-analysis shows that, in simplifying land use change dynamics, spatially extensive land

uses have received the most attention in the literature while smaller land uses are often filtered from

analyses or aggregated into other less specific categories. This result is reflective of other keystone

papers in the literature on land use change. For example, Geist and Lambin (2002) categories land

uses (in their paper referred to as proximate causes of land use change) into three themes:

agricultural expansion, wood extraction and infrastructure extension. The theme of infrastructure

extension incorporates all other land uses that occupy small land areas (transportation networks,

markets and processing facilities, settlements, public services and private company infrastructure

such as hydro-power generation, mining and fossil fuel exploration), yet disregards other attributes

that distinguish these land uses. This finding suggests that an assumption is commonly made

whereby a land use’s aggregate area can indicate its importance in regional land use change

dynamics. While this may be true for some land uses in some regional contexts, it overlooks the

area-independent interactions that occur between land uses through their operation.

It is important to remember that ‘land use’ is not synonymous with ‘land cover’ (Comber, 8).

While the impacts of land cover may be considered in accordance with their direct spatial

occupation of the landscape, the role of land use cannot. This is because land use has an operational

component that often extends beyond the onsite use of land. For example, land use operation can

involve the consumption of other (non-land) resources, the development of off-site social capital

and manufactured infrastructure and the production of economic wealth. These alterations all have

implications for broader land use change dynamics as they alter the opportunities and constraints of

a landscape, which by definition, alters the decision making of actors to implement future land use

change (Lambin & Meyfroidt 2010). In this way, a land use can be considered to interact with other

land uses to potentially indirectly drive their dynamics (e.g. alter their future transition

probabilities), irrespective of their spatial extent.

Conceptually, land uses can interact in two ways: 1) either the operation of a land use can modify

the spatial characteristics of the landscape that are positively or negatively associated with the

operation or expansion of other land uses, or alternatively 2) the operation of a land use can modify

the underlying forces driving production demands for other land-based commodities. Through these


interactions it is therefore possible for the operation of smaller-scale land uses to drive the dynamics

(e.g. the expansion) of other, potentially more extensive, land uses. The following section builds on

these ideas of land use operation and interactions and examples are given to illustrate the

implications of overlooking land uses that occur at smaller spatial scales.

Land use operation modifies landscape attributes

The location of land use transitions is dependent on spatially explicit landscape attributes (Soares-

Filho et al. 2002). These attributes can be associated with natural landscape features, for example,

the probability of agricultural expansion is higher in regions of low elevation and flat topography

(Maeda et al. 2010). However, they can also be associated with socio-economic factors of the

landscape, such as proximity to land use. For example the distance to pre-occurring deforestation

and established agricultural areas is an important spatial factor governing agricultural frontiers

throughout the tropics (Etter et al. 2006). Another example is the distance to markets and urban

centers, which has long been recognized as an important factor in determining agricultural

opportunities and the probability for future production (Lambin et al. 2003; Nelson & Hellerstein

1997).

Proximity to land use has little to do with the aggregated area of that particular land use. Instead it is

heavily associated with the heterogeneity that a land use contributes to the landscape. This idea that

spatial heterogeneity is a more important factor governing land use change dynamics than land use

spatial extent is rapidly gaining prominence in the literature (Rounsevell et al. 2012; Smart et al.

2012). In this regard, land uses that occupy only small area in aggregate can be as important, if not

more important, than more extensive land uses; this renders ‘area’ a poor indicator of land use

importance. The most obvious example of this, and potentially the only exception to overlooked

small-scale land uses within the Land System Science literature is the influence of paved roads on

the tropical deforestation (Geist & Lambin 2002). Roads and highways, although spatially

insignificant in aggregate, have important implications for land use change dynamics. Through their

operation (i.e. their ability to increase access for other land users to otherwise ‘unavailable’ land)

they have the potential to significantly increase the probability of nearby deforestation (Soares-

Filho et al. 2004). The cause and effect, however, of roads on deforestation is not always straight

forward and is generally dependent on a region’s landscape context, such as development history

and the occurrence of other regional land uses. Regardless, the relationship between road paving

and land use change has received a lot of attention in tropical landscapes and has become a widely

recognized important land use in the field Land System Science (Freitas et al. 2010; Nelson &

Hellerstein 1997; Wilkie et al. 2000).


Land use operation alters underlying driving forces

While the spatial distribution of land uses and their expansion probabilities is associated with

landscape attributes, the rate at which a land use expands throughout a landscape is determined by

its underlying driving forces (Hersperger & Burgi 2009). Ultimately, driving forces culminate to

represent the demand for goods and services and the opportunities available for actors to meet such

demands through changes in land use (Lambin et al. 2003). To date, the majority of research on

driving forces is focused on how land uses respond to increasing demands (e.g. food and shelter)

from a growing population. It is important to recognize, however, that although changes in land use

is often in response to changing demands, the demands for products are also altered through

changes in land use. This is because land use operation does not just involve production, but also

consumption, and that these consumed resources also have embodied land requirements.

The importance of land use operation in consuming other land-produced resources has begun to

emerge in the context of agricultural intensification (Rounsevell & Arneth 2011). Recent advances

suggest that changes in land management, rather than an expansion in area of a particular land use,

may have at least as important—if not more important—consequences for regional to global-scale

land use change dynamics (Ellis & Ramankutty 2008; Verburg et al. 2011). This is because a

modification in land use operation, such as management intensification, modifies the requirements

of the land system itself, such as increased demand for water resources and fertilizers. Similarly,

interactions can also emerge through changes in the demand for non-natural resources, such as

social, human and economic capital. For example, intensification of irrigated agriculture in Western

Australia’s wheat belt led to a decline in the required labor force, which had severe implications for

maintaining the viability of surrounding rural communities; subsequently rural to urban migration

took place with younger generations (Tonts 1996). Interactions between land uses is also illustrated

with the emerging literature on ‘indirect’ land use change (Lapola et al. 1 ) and ‘teleconnections’

(Haberl et al. 2009; Seto et al. 2012), where a change in the demand and production of one resource

in one geographical location affects the production opportunities for other resources elsewhere.

Outside of the literature on agricultural production systems, however, it remains largely unknown

how small-scale changes in land use area may have operational impacts that modify the dynamics

of other more extensive land uses. This is not to say, however, that this evidence does not exist

elsewhere. For example, the literature on ecological footprints is capable of quantifying the

extensive embodied land requirements for the operation of smaller-scale industrial land uses (Rees

& Wackernagel 1996). However, while approaches like these can identify and quantify how land

use decisions can indirectly have more extensive repercussions for the landscape, they cannot


provide insight into the spatially explicit nature of these dynamics. Without this understanding, the

usefulness of embodied land calculations in policy development and land use change management

is limited.

Intensive land uses: an overlooked aspect in Land System Science

The above discussion identifies an overlooked category of land uses, here defined as ‘intensive land

uses’. These land uses occupy a relatively small spatial extent but can indirectly drive land use

change dynamics through their operation. As indicated in the previous section, one exception is the

attention given in the literature to the influence of roads on tropical deforestation. With the concepts

proposed in the previous section and the case study evidence presented in the literature on roads, it

becomes evident that other land uses which occupy smaller spatial scales may also operate

‘intensively’ to have far more extensive implications for land use change dynamics. Such examples

may include: mining and mineral extraction, piggeries and feedlots, manufacturing industries,

aquiculture, and energy generation. For these examples, spatial extent does not provide much

information about their importance in land use change dynamics. In the following section, we

present a case study from an iron ore production region in Brazil to demonstrate how the intensive

land use of mining interacts with other land uses to drive land use change dynamics.

2.5 Case study: Mining, an overlooked intensive land use

Mining is an example of an intensive land use that is overlooked in the literature as an influential

component of land use change dynamics. Although current mining operations occupy less than 1%

of the global land surface area (Bridge 2004), emerging evidence suggests that the operation of this

industry can have far more extensive consequences for land use change dynamics than is indicated

by the area directly occupied by their operations (Schueler et al. 2011).

The Iron Quadrangle

Our case study site is the Iron Quadrangle, in south-eastern Brazil. The region is the largest iron ore

production hub in South America and third largest in the world (InfoMine 2012). Mines in the

region impact about 200 km2 (or less than 1% of the total land area) and expand at a rate of

approximately 4 km2 per year (Sonter et al. 1 b). In addition to mining, the region’s native

Atlantic Forests are threatened by the expansion of other more extensive productive land uses, such

as pasture, urbanization and Eucalyptus plantations. Analyzing the operation of mines identified

three examples of how this industry indirectly drives the land use change dynamics within this

region.


An unintentional ‘deforestation buffer’

The first example of how mining indirectly drives land use change dynamics was that mines created

a ‘deforestation buffer’, where deforestation for the expansion of pastures was less likely to occur

closer to mining leases than far from them (Figure 2.2). This unintentional conservation effect was

caused by mining companies purchasing the land around the peripheries of their lease boundaries.

This was done for several reasons, one of which was to avoid the negative visual effects associated

with mines; an impact that is found in many other mining regions of the world (Moran & Brereton

2013). The consequences of this decision, however, produced a 300 m wide conservation buffer

around each mining lease. At the regional scale, this buffered area aggregated to approximately

450 km2, incorporating approximately 6% of the native forests in the region (as defined by the

native vegetation map of the Atlantic Forest biome; SEMAD 2010).

Figure 2.2: The association between occurrence of deforestation (W+, vertical axis) and distance to a mine lease

(horizontal axis), showing that when forests exist close to a mining lease they are less likely to be cleared than forests

further from mining leases. W+ methods are described in detail in Section 3.2 – Spatial land-use change model.

Legal requirements to establish conservation reserves

The second example of how the mining industry influenced land use change dynamics was through

the legal requirement to compensate (or offset) impacts on deforestation. The Brazilian Forest Code

requires companies to establish conservation areas when they clear forests to expand existing mines

or establish new operations (Brazil, 2006). Currently these policies are not heavily enforced in the

region, where the current extent of conservation areas established by mining companies to offset

deforestation equals only approximately 20 km2 (IBAMA 2010); an area that represents less than


10% of deforestation caused by mining companies since 1990. When these protected areas are

established, however, they contain native vegetation with a much lower probability of deforestation

compared with unprotected areas (Sonter et al. 2013b). Over the next decade, both mining

companies and government officials suggest that enforcement of these policies will increase given

the growing pressure from society to improve environmental performance. This has been the case in

Brazil and around the world (ICMM 2005; ten Kate et al. 2004). As a result, an additional 230 km2

of conservation areas could be established in the region, considering the current rate of

deforestation for mine expansion and the national plan to double iron ore production by 2020

(MME 2011). Such conservation reserves could protect at least % of the region’s native forest

from deforestation.

Increasing regional demand for charcoal

The third example that illustrates how mine operation drives regional land use change dynamics is

through the processing of mined iron ore. Approximately one half of the iron ore that is produced in

Brazil is converted into steel or iron alloys, which is a process that demands a carbon source. Thirty

percent of the steel produced in the region currently uses biomass charcoal for this purpose (EPE

2012). In Minas Gerais, charcoal is predominantly produced from short-rotation Eucalyptus

monocultures, which have a large land requirement (Piketty et al. 2009). About 1100 km2 of the

region is currently occupied by plantations, which represents approximately 6% of the total

landscape (Sonter et al. 2013b). In the future, production of charcoal for steel production is

expected to increase significantly, given the increasing demand for steel and the growing financial

incentives to grow plantations for charcoal production in order to mitigate the impacts of climate

change (IPCC 2012).

Implications for land use change dynamics

Although the direct area of mining in the Quadrilátero Ferrífero is relatively small, the three

examples presented here illustrate that the operation of the industry had far more extensive impacts

on land use change dynamics. Given the case study context (e.g. history, location and culture), the

relationships between mining and forest dynamics were good examples to illustrate the concept of

intensive land uses. While in other mining regions of the world, these specific examples may not

occur, others might. For example, there is often an interaction that occurs between mine expansion

and regional urbanization. Such is evident in the Carajás mining region in Brazil’s Amazonian state

of Pará, where broad-scale industrial mining initiated a rapid influx of people to the region’s

‘boomtowns’ (Roberts 199 ). Similarly, when mines close down, surrounding towns and


communities often suffer to the point where their viability becomes questioned (Petkova-Rimmer et

al. 2009).

Ignoring the operation of mines changes our understanding of land use change dynamics in this

context. This has consequences for the development and implication of management plans and

policies that aim to achieve long-term sustainable development goals because the land use change

driven by mining would not be captured by these plans. Similarly, our case study evidence

illustrates potentially positive impacts that mines could have in terms of forest conservation. The

potential benefits that the mining industry can have in conservation and revegetation have also been

found in other studies (Sonter et al. 2013c). Neglecting the impact of mines on land use change

dynamics ignores (and potentially forgoes) the potential this industry has to contribute, in positive

ways, to their regional landscapes.

2.6 Conclusions and future research directions

Our findings suggest that including or excluding land uses based solely on their spatial extent may

fail to sufficiently capture the complexities of land use change dynamics. For this reason, we

propose a shift in the assumption that a land use’s spatial extent is a sufficient indicator of its

importance in land use change dynamics as it overlooks the importance of intensive land uses. To

capture these understudied land uses, we suggest that the decision to include or exclude land uses

from analyses requires an understanding of how land uses operate (i.e. their production and

consumption of other resources and capital) and, through their operation, how they interact with

other land uses and users in their regional context. This approach would capture intensive land uses.

Incorporating intensive land uses into land use change models will then require two additional

forms of information: 1) spatially explicit land use maps at high spatial resolution and 2) production

and consumption statistics for land uses and their operation. Producing high resolution land use

maps to allow identification of small-scale land uses is often considered limited by the spatial

resolution of satellite imagery (Rounsevell et al. 2012; Schmit et al. 2006; Verburg et al. 2011).

Although high-resolution imagery is now available, classification of these images to land use

categories at regional to global scales remains a time consuming task. Opportunities exist, however,

to combine GIS information with satellite images to produce land use maps containing information

at multiple scales. This has been successfully done in studies on roads and deforestation trajectories,

since the road network is a feature not easily detected with remote sensing images. Mining

operations, for example, could also be incorporated into land use maps using a similar technique

when the resolution of land use maps is too coarse to capture the expansion of this industry.


Incorporating information on land use production and consumption requires additional information

to that detected from satellite imagery. Often these datasets are collected through surveys and

censuses and aggregated to regional or national government boundaries. Spatial disaggregation of

these statistics by land uses then becomes an important next step. Some statistical methods currently

exist to do this, mainly in the context of agricultural production (Monfreda et al. 2008; van Asselen

& Verburg 2012); however, significant opportunity remains to build on these methods to capture

the production and consumption of other, more intensive, land uses.

The alternative to spatial disaggregation of production/consumption statistics would be to take a

systems approach to modelling land use change dynamics. We feel significant opportunity remains

to enhance techniques of coupling spatially explicit land use change models with other system and

hierarchical models capable of representing land use operation. This is important because of the

need to incorporate the dynamic interactions that occur between land uses in their resource

requirements and the interdependencies that exist within the land use system. One example of this

was presented by Lee et al. (2009). In this study the authors coupled a spatially explicit land use

change model with a model of socio-economic metabolism (essentially a regional-scale production

and consumption model). The results illustrated how the regional flow of materials, the

accumulation of assets and the generation of waste from the operation of multiple land uses drove

regional land use change dynamics and trajectories in China’s Taipei Metropolitan Region. These

systems level approaches that enable inclusion of industry interactions in land use change dynamics

will be integral to furthering our understanding of intensive land uses in the field of Land System

(or Change) Science.

Laura Sonter Chapter 3: Spatially Extensive Land Use Change 23

CHAPTER 3

Spatially Extensive Land Use Change

Objective 2: To quantify the land use change (onsite and offsite), and associated impacts on native

vegetation, driven by global demand for steel.

Preface: The purpose of this Chapter was to quantify the spatial extent of land use change driven

by global demand for steel. I quantified twenty years of land use change, and developed and

calibrated a spatially-explicit land use change model capable of simulating the effects of increasing

steel production. Results illustrate that mine expansion for iron ore extraction was relatively small-

scale; however demand for steel also drives spatially-extensive plantation expansion for charcoal

production. Further, land use changes impacted native vegetation through direct and indirect

pathways, which influenced the majority of land in the study region. The major contribution of this

paper to the thesis was to provide evidence that global driving forces of small-scale land uses can

have spatially extensive effects on processes of regional land use change, and cause significant

environmental impacts. Following this, Chapters 4 and 5 investigate the effectiveness of

management strategies to mitigate these impacts.

Future work opportunities: The results of this paper suggest that an additional indirect impact on

deforestation rates may be caused by plantation expansion of cleared land. To investigate this

impact would require a detailed analysis of all driving forces of deforestation in QF. This further

analysis was beyond the scope of this paper; however, presents an opportunity for future work.

Acknowledgements: This paper was published in the journal Global Environmental Change. I

would like to thank Allaoua Saadi for his enthusiastic involvement in field trips and discussions

throughout the early project development phase. I am also grateful to Mr. Fábio Nogueira de Avelar

Marques for providing insight into the plantation charcoal industry in Brazil.


Global demand for steel drives extensive land-use change

in Brazil’s Iron Quadrangle2

Abstract: Global demand for minerals is often considered an insignificant driver of land-use

change because mines are small. We (1) investigated evidence supporting a link between global

demand for steel and land-use change in Brazil’s Iron Quadrangle, and ( ) quantified the extent of

land-use change and associated impacts on native vegetation. Historic land-use change was

quantified using Landsat TM, relationships between demand for steel and land-use change were

investigated using a simple linear model, and future scenarios were simulated using a calibrated

land-use change model. Results support our hypothesis that global demand for steel drives extensive

land-use change in the Iron Quadrangle, where increased steel production was correlated with

increased iron ore production and mine expansion, and with increased charcoal production and

plantation expansion. The direct impacts of mining on native vegetation were disproportionate to

their relatively small spatial extent, while direct impacts of plantations were spatially extensive, as

were their impacts on surrounding native vegetation. Additionally, evidence of two indirect impacts

emerged during 1990–2010. Plantation expansion decreased native forest regrowth, while

competition for land between mining companies and urban developers increased deforestation

pressures. In combination, global demand for steel affected the majority of land in the Iron

Quadrangle; however, many impacts were poorly captured by current land management approaches.

Similar processes may operate in other mining regions, where global demand for minerals drives

production of multiple resources (non-renewable and renewable) and thus extensive land-use

change.

Keywords: Atlantic Forest, charcoal, deforestation, mining, plantations, regrowth

3.1 Introduction

Global demand for minerals is often considered an insignificant driver of extensive land-use change

because the area impacted by mining is small (Sonter et al. 2013a). Instead, most attention is given

to drivers of agricultural production, since agricultural land uses are spatially extensive (e.g.

McAlpine et al. 2009; Schmitz et al. 2012). However, we hypothesize that global demand for

minerals drives more extensive land-use change than is currently recognized, given society’s global

reliance on mineral resources (Prior et al. 2012; Wellmer & Becker-Platen 2002) and considering

the embodied land requirements of mineral supply chains (Hubacek & Giljum 2003). In this paper,

2 Sonter et al. (2014) Global demand for steel drives extensive land use change in Brazil’s Iron Quadrangle. Global

Environmental Change 26:63–72 DOI: 10.1016/j.jclepro.2014.03.084.

http://dx.doi.org/10.1016/j.jclepro.2014.03.084


we focus specifically on global demand for steel driving extensive land-use change in Brazil.

Hypothesized causal pathways are conceptualized in Figure 3.1 and described in detail below.

Figure 3.1: Hypothesized causal pathways between increasing global demand for steel and regional land-use change in

Brazil’s Iron Quadrangle (QF). Broken arrows represent underlying driving forces and solid arrows represent proximate

causes of land-use change. Inset illustrates the location of Minas Gerais in Brazil (top) and the distribution of 2010

mines in QF (bottom).

Global demand for steel has accelerated over the past century, fueled by industrialization of Asian

countries (Goldewijk et al. 2011; Worldsteel 2014). The minerals required to manufacture steel,

however, are produced by a small number of supplier countries. Brazil is currently the world’s

leading producer of key steel feedstocks (Gurmendi 2013), exporting 66% of their mined iron ore

(258.8 Mt of 391.1 Mt) and 23% of their pig iron and steel (8 Mt of 35.2 Mt) to international

markets each year (MME 2012).


Converting mined iron ore into pig iron and steel requires a carbon source. While most countries

utilize coking coal for this purpose, the Brazilian steel industry has long substituted coal with

charcoal (AMS 1 ). Using charcoal removes the country’s dependence on foreign energy

supplies (Gurmendi 2013) and increases the quality of steel produced (Nogueira & Coelho 2009). In

2011, more than one third of Brazilian pig iron and steel was produced using charcoal (EPE 2012).

Brazilian charcoal has been historically produced from native forests; however, mounting

international pressure to reduce deforestation and mitigate GHG emissions has caused a recent shift

to ‘carbon-neutral’ plantation charcoal production (Gouvello et al. 1 ) (Figure . ). Plantation

charcoal production is land intensive (Fearnside 1995); to produce 10 Mt of hot metal requires at

least 1.3 Mha of highly productive (40 m3ha

-1yr

-1) Eucalyptus plantations (Piketty et al. 2009). As a

result, demand for charcoal continues to outweigh plantation production capacity. In 2012, almost

half the charcoal used in Brazilian pig iron production was derived from native forests (AMS 2012).

Figure 3.2: Production of charcoal from native forests (native charcoal) and plantations (plantation charcoal) in Minas

Gerais. Source: IBGE, 2012.

Multiple factors have limited plantation expansion to meet charcoal demands for steelmaking in the

past, including high production costs (relative to coal or native charcoal production), lack of

financial credit to establish plantations, and weak institutional governance for environmental

licensing (ABRAF 2011; Piketty et al. 2009; van Kooten et al. 2002). Over the past decade,

multiple factors have been introduced to increase plantation production. Carbon credits became

available under the World Bank’s Clean Development Mechanism (CDM) to support industrial-

scale production (The World Bank ) and Brazil’s Low Carbon Emission Agriculture Program

(Programa ABC) began to offer low-interest loans for landholders to establish farm-scale

production (Soares-Filho et al. 1 ). Additionally, government policies in Minas Gerais (Brazil’s


largest charcoal-producing state) now stipulate that native charcoal use in steelmaking be limited to

5% of all charcoal used by 2017 (PROFOR 2012).

In the future, global demand for steel is projected to increase; Hatayama et al. (2010) estimate a six-

fold increase from Asia by 2025. In response, the Brazilian government and steelmaking industry

have committed to triple production by (MME 11). Brazil’s forestry sector is also expected

to overcome the current plantation charcoal deficit during this time (AMS 2009, 2012) given

increasing enforcement of Brazil’s deforestation legislation (Nepstad et al. 11) and increasing

financial incentives to produce plantation charcoal.

Despite these production commitments, consequences for regional land-use change and associated

impacts on native vegetation have yet to be analyzed. Therefore, our objectives were to: (1)

investigate evidence supporting a link between increasing global demand for steel and land-use

change in Brazil’s Iron Quadrangle; and ( ) quantify land-use change and associated impacts on

native vegetation. We focused on Brazil’s Iron Quadrangle, since it produces approximately 65% of

Brazilian iron and steel (Gurmendi 2013) and is an increasingly important region for plantation

charcoal production.

3.2 Methods

Study region

The Iron Quadrangle (Quadrilátero Ferrífero; QF) occupies % of Brazil’s south-eastern state of

Minas Gerais (Figure .1) and is located in the Atlantic Forest biome, one of the world’s most

threatened yet bio-diverse ecosystems (Myers et al. 2000; Ribeiro et al. 2009). Remnant vegetation

in QF is ecologically and socially significant (Jacobi & do Carmo 2008; Sonter et al. 2014a), as

indicated by its ‘APA’ status (a federally protected unit permitting sustainable land use; IBAMA

1 ) and its UNESCO ‘geopark’ classification (Anastasia et al. 1 ). Iron ore mining in QF has

had a long and important history; QF currently produces 6 % of Brazil’s iron ore (InfoMine 1 )

and contains 75% of its economically viable reserve (Rosiere et al. 2008).

The QF is also an important charcoal production region. Sixty percent of Brazilian-produced

charcoal originates from the state of Minas Gerais (AMS 2012), 90% of which is used in pig iron

and steel production (EPE 2012). Charcoal in Minas Gerais is largely produced from short-rotation

Eucalyptus plantations (E. grandis x urophylla; IBGE 2006; Figure 3.2), which cover 1.4 Mha (or

2.5%) of the state (ABRAF 2011). These plantations exist at two scales: industrial and farm-scale

operations. Industrial operations are defined as non-family farms of 20–450 ha (Kroger 2012) and


occur outside QF where they utilize CDM credits to support high-yield plantations (The World

Bank 2002). It is unlikely industrial operations will occur inside the QF boundary in the near future,

given a lack of large contiguous tracts of available land. In QF, farm-scale operations are dominant.

These are smaller and more fragmented than industrial operations and are less likely to be certified

or monitored in terms of their social and environmental practices (May 2006). Approximately half

the plantations in QF produce charcoal, the rest produce cellulose for paper production, or other

wood materials (IBGE 2012).

In addition to iron mining and plantations, QF has a large urban area, incorporating the state’s

capital, Belo Horizonte (IBGE 2010). Urbanization in Belo Horizonte is driven by population

growth and a rural to urban migration, which is caused by demand for higher living standards

(IBGE 2010). The QF also contains an extensive cattle-grazing legacy, which is now in decline due

to increasing land value, low pasture productivity and high labor costs (IBGE 2006). Remaining

pastures in QF are predominantly family-owned, low-density cattle grazing properties.

Land-use change data

Four land cover maps were produced by classifying Landsat TM data (30 m resolution). Two scenes

cover QF (217 064 and 218 064) and four ortho-rectified image pairs (one from each scene) were

acquired (from USGS Glovis) over a 20 year period. Image pairs were dated: 24 Jun 1993/25 Jul

1990, 08 Jul 2001/26 Jun 2000, 24 Jul 2004/31 Jul 2004, 07 Aug 2009/01 Aug 2010, for scenes 217

064 and 218 064 respectively. Images were converted to exo-atmospheric reflectance using

published post-launch gain and offset values (NASA Goddard Space Flight Center 2011). Image

pairs were geographically joined and clipped to the study region, which was defined by intersecting

local municipalities (IBGE 2005) (Appendix A) with QF geographical boundary (CODEMIG

2010).

The 2010 image was classified into six land cover classes (forest, grass, mine, plantation, urban and

water) using a supervised classification and image processing software (ENVI version 4.8; Exelis

Visual Information Solutions, 2010). The classification scheme utilized the Spectral Angle Mapper

algorithm with bands 1–7 (excluding thermal band 6), NDVI and Tasseled Cap. Training pixels for

each class were selected based on field knowledge and higher-resolution Quickbird imagery. Each

training class had a distinct spectral signature (Jeffries-Matusita and Transformed Divergence

separability statistics >1.9 for all comparisons; Richards 1999).

Pre-2010 images (1990, 2000, 2004) were classified using an image differencing and thresholding

method (applied to NDVI) to identify pixels that had undergone land cover change (Jensen 2005).


This method required: (1) the classified 1 land cover map, ( ) a ‘change image’ (e.g. NDVI2004

subtracted from NDVI2010), and ( ) a threshold value to identify ‘change’ and ‘no change’ pixels (a

10% change threshold in NDVI difference was applied). Pixels in the binary mask (of change

regions) were classified using end members based on the spectral signatures of training pixels

collected from the 1 image. ‘No change’ pixels were excluded from classification. This

approach reduces omission and commission errors (Jensen 2005); however, accuracy depends on

the threshold value and its ability to detect land cover change.

An accuracy assessment was performed to detect omission and commission errors. A stratified

random sampling protocol was used (Foody 2011; Stehman 2009) and sample locations were

generated in ENVI. Ground truth information was collected from higher-resolution imagery in

which land cover classes were clearly visible. Due to data limitations, only the 2010 and 1990 land

cover maps were assessed using Quickbird imagery (2010) and an orthorectified digital photograph

(199 ). For the 199 map, ‘change regions’ were sampled for accuracy assessment to avoid

resampling stable pixels. Classification accuracy was above 90% for all land cover classes

(Appendix B).

Land cover classes were converted to land use classes, including native forest (unmanaged forests,

including native forest regrowth), native grass (unmanaged grassy vegetation), field (productive

grassy landscapes, generally managed for cattle grazing purposes), mine (current and abandoned

mining operations), plantation (planted and managed tree crops, usually Eucalyptus monocultures),

urban and water bodies. Land cover to land use conversions were performed using decision rules

developed from the classification time series (Appendix C). Additionally, a native vegetation map

(SEMAD 2010) was used to separate native grass (Campo, Canga and Cerrado) from field, which

was typically either low-density cattle pastures, abandoned cattle pastures or cleared land in

transition to urban.

Spatial land-use change model

The land-use change simulation model was developed using the modeling platform Dinamica EGO

(Soares-Filho et al. 2013). The model consisted of two parts: a spatially explicit model of landscape

dynamics, which allocated land use transitions in the landscape, and a scenario generation model,

which projected future land use transition rates.

The spatial model was calibrated by calculating conditional probabilities between spatially

distributed landscape attributes and land use transitions. The Bayesian Weights of Evidence (WofE)

(Bonham-Carter 1994) was used to calculate WofE coefficients for five land use transitions (Figure


3.3): deforestation (native forest to field), regrowth (field to native forest), plantation expansion

(field to plantation), mine expansion (native forest to mine, native grass to mine, field to mine) and

urbanization (native forest to urban, native grass to urban, field to urban). Seven landscape

attributes were used: distribution of protected areas (IBAMA 2010), distribution of mining leases

(CODEMIG 2010), slope (calculated from Shuttle Radar Topography Mission [SRTM] digital

elevation model; USGS 2006) and distance to fields, native forests, urban and mines (calculated

from classified land use maps). Spatial autocorrelation between landscape attributes was not

significant (Crammer coefficient < 0.5; Bonham-Carter 1994).

The spatial model was calibrated using 1990–2000 WofE coefficients, however all time periods

produced similar trends (Appendix D). The calibrated model produced a spatial probability map for

each land use transition. These maps were validated by simulating land-use change from 2000 to

2010 (using 2000–2010 transition rates) and comparing the 2010 simulated land use map with the

2010 classified map, using fuzzy logic and an exponential decay function at multiple increasing

windows (Soares-Filho et al. 2013). The calibrated model performed significantly better for all

transitions than a null model, which was calibrated with 2000–2010 transition rates but evenly

distributed spatial probably (Appendix E). Nevertheless, regrowth had relatively lower predictive

capability than other land use transitions due to its relatively rare occurrence (5% minimum

similarity between the simulated and observed 2010 land use maps at 15 ha resolution), as did

plantation expansion (14% minimum similarity at 15 ha resolution) since many closely suitable

sites were available.

Figure 3.3: Land use transitions observed during 1990–2010. Arrow weight depicts relative land use transition rates

(ha yr-1

).


Scenario generation model

Future land use transitions rates were calculated for three scenarios: business as usual (BAU),

increasing demand for steel (DEMAND), and overcoming the plantation charcoal deficit

(PLANTCHAR). BAU assumed land use transition rates remained constant through time and thus

meet current annual demands for steel, DEMAND aimed to meet Brazil’s increasing annual steel

production targets but maintained the current plantation charcoal deficit (equal to the 1.7 Mt of

native charcoal currently used in Brazilian steelmaking; IEF 2008), and PLANTCHAR aimed to

both meet increasing production targets and overcome the deficit by 2030. All scenarios used 2004–

2010 transition rates for deforestation, regrowth and urbanization. BAU also used 2004–2010

transition rates for mine expansion and plantation expansion, while DEMAND and PLANTCHAR

used projected rates for these transitions.

Projected rates were obtained from a simple linear model, based on historic production trends and

pig iron projections made by the Brazilian government (MME 2011) (Figure 3.4). Linear

relationships were established between pig iron production and iron ore production (R2=0.83;

n=20), iron ore production and mine extent (R2=0.99; n=4), pig iron production and plantation

charcoal production (R2=0.49; n=20), and plantation charcoal production and plantation extent

(R2=0.91; n=4). National-scale statistics were used for pig iron and iron ore production (USGS,

2012) and state-scale data for plantation charcoal production, since these sources represented

production trends in QF (IBGE 2012; InfoMine 2012). Mine and plantation extent were quantified

from classified land use maps. For DEMAND, mines and plantations were extrapolated according

to future pig iron production (MME 2011) to 2030 (Figure 3.4). For PLANTCHAR, 88 Kt yr-1

of

additional plantation charcoal was added to the model before extrapolating plantation expansion so

as to overcome plantation charcoal deficit by 2030 (Figure 3.4). Linear extrapolation assumes

historical trends remain constant in the future.

Extrapolated mines were divided among the three initial land uses for this transition (native forest,

native grass and field), according to 2004–2010 proportions: 65% of mine expansion occurred in

native forests, 21% in native grasses, and 14% in fields. Aligning with current legislation in QF and

historic trends to converse native forest and grasses, all extrapolated plantations were allocated to

fields. Mine and plantation expansion were converted to annual transition rates (Figure 3.5A, B)

and these were used to simulate future scenarios. All land use transitions were allocated to the

landscape at 1 ha spatial resolution at annual iterations from 2010 to 2030. Simulated land use maps

were compared with the 2010 land use map to quantify land-use change and impacts on native

vegetation.


Figure 3.4: Simple linear model showing relationships between: A) Brazilian pig iron production and iron ore production (R2=0.83; n=20; observed annual data from 1990–2010);

B) Brazilian iron ore production and QF mine area (R2=0.99; n=4; observed annual data from 1990, 2000, 2004, 2010); C) Brazilian pig iron production and Minas Gerais plantation

charcoal production (R2=0.49; n=20; observed annual data from 1990–2010); D) Minas Gerais plantation charcoal production and QF plantation area (R

2=0.91; n=4; observed annual

data from 1990, 2000, 2004, 2010). Projections are extrapolations of historic linear trends under future pig iron production projections obtained from PNM (MME 2013) for 2015,

2022 and 2030. Confidence intervals are the 95% confidence level for model predictions.


Figure 3.5: Observed and projected land use transition rates for: (A) plantation expansion (field to plantation), (B) mine expansion (native forest to mine, native grass to mine, field

to mine), (C) deforestation (native forest to field), and (D) regrowth (field to native forest). Error bars on observed rates and BAU projected rates represent transition detection errors

quantified from accuracy assessment of land use change observations. Error bars on projected DAMAND and PLANTCHAR rates are calculated from 95% confidence intervals

shown in Figures 3.4B and Figure 3.4D.


3.3 Results

Observed land-use change during 1990–2010

QF underwent extensive land-use change during 1990-2010 (Figure 3.6). More than 9,500 ha

(0.5%) of available land transitioned to mining and 41,000 ha (9%) transitioned to plantations.

Urban and field extent grew by 30% and 5%, respectively. Multiple land use transitions impacted

native vegetation (Figure 3.3). In total 63,000 ha (7%) of native forests and 2,700 ha (4%) of native

grasses were cleared, which caused native vegetation extent (45% of land in 2010) to fall below the

combined extent of mines, urban areas, plantations and fields (55%) by 2010 (Figure 3.6).

Native forest loss was caused by the expansion of multiple land uses (Figure 3.3). Deforestation

(native forest to field) caused the greatest loss during 1990–2010, at an average rate of 2,500 ha yr-

1; however, rates significantly declined from 3020 ha yr

-1 to 2,360 ha yr

-1 (Figure 3.5C). More than

3.6% of native forests were also cleared by mine expansion, and another 3.6% by urbanization.

During 1990–2010, the annual rate of mine expansion significantly increased over time (Figure

3.5B), while the annual rate of urbanization remained constant. During 1990–2010, plantation

expansion occurred at an average rate of 1,500 ha yr-1

; however, rates significantly increased from

590 ha yr-1

to 2,500 ha yr-1

(Figure 3.5A). Regrowth (field to native forest) equaled 9,000 ha during

1990–2010; however, rates significantly declined from 630 ha yr-1

to 250 ha yr-1

(Figure 3.5D).


Figure 3.6: Regional land use and land-use change. (A) Percent of QF occupied by land use classes in 1990, (B) observed land-use change since 1990, and (C) projected land-use

change since 1990. Note change in y axis scale between B and C.


Spatial distribution of land use transitions

WofE coefficients represent the influence of landscape attributes on the spatial distribution of land

use transitions (Appendix D). Deforestation (native forest to field) occurred close to fields, far from

mines, outside protected areas and on flat slopes. Regrowth (field to native forest) occurred close to

native forests and on steep slopes, as did plantation expansion (field to plantation) (Figure 3.7).

Urbanization (native forest to urban, native grass to urban, field to urban) occurred close to urban

areas and outside protected areas. Mine expansion (native forest to mine, native grass to mine, field

to mine) occurred where iron ore existed, close to current mining operations and outside protected

areas.

Simulated land-use change during 2010–2030

All projected transitions were successfully allocated to the landscape (i.e. global constraints on the

simulation model were met), illustrating sufficient land was available to meet projected iron ore and

plantation charcoal production by 2030. Under BAU, mines expanded by 13,000 ha (to occupy 2%

of QF) and plantations expanded by 52,000 ha (to occupy 8%) by 2030. Under DEMAND, mines

expanded to 4% of QF and plantations to 14% (Figure 3.8), while under PLANTCHAR plantations

reached 17%.

Sixty-five percent of mine expansion occurred in native forests and 21% in native grasses during

2004–2010. Holding this proportion constant and simulating land-use change under BAU, mines

cleared 8,600 ha of native forests and 2,700 ha of native grasses during 2010–2030. Under

DEMAND, native forest loss by mine expansion increased to 36,800 ha and native grass loss

increased to 11,600 ha. Alternatively, plantation expansion was allocated exclusively to fields,

causing a regional decline in fields of 52,000 ha, 168,500 ha and 213,000 ha under BAU,

DEMAND and PLANTCHAR, respectively (Table 3.1).


Figure 3.7: Weights of Evidence coefficients (W+) for landscape attributes associated with land use transitions of

regrowth (field to native forest) and plantation expansion (field to plantation): (A) influence of distance to native forest

on regrowth, (B) influence of slope on regrowth, (C) influence of distance to native forest on plantation expansion, and

(D) influence of slope on plantation expansion. Positive W+ indicates a positive influence and represents increased

probability of transitions in pixels with this attribute. Negative W+ indicates a negative association (or a repelling

effect) and decreased probability in pixels with this attribute. Circles represent 1990–2000 data, squares 2000–2004 and

crosses 2004–2010.


Figure 3.8: Land use maps for observed data (A: 1990 and B: 2010) data and simulated data (C: 2030 under DEMAND) in the central region of QF. Black dashed line shows the QF

urban-mining interface.


Table 3.1: Observed and projected land use area (1000 ha) during 1990–2030 at 5 yearly intervals.

Land use Observed BAU DEMAND PLANTCHAR

1990 2010 2015 2020 2025 2030 2015 2020 2025 2030 2015 2020 2025 2030

Field 868 869 864 858 852 847 839 811 769 724 828 789 736 680

Urban 43 57 60 63 66 68 60 62 65 68 60 62 65 67

Mine 8 17 21 24 27 31 31 43 58 74 31 43 58 74

Native forest 921 858 845 832 820 807 839 820 800 780 839 820 800 779

Plantation 69 110 123 136 149 162 147 180 228 279 158 203 261 323

Native grass 71 69 68 67 66 65 66 63 60 56 66 63 60 56


3.4 Discussion

Results support our hypothesis that global demand for steel drives extensive land-use change in

Brazil’s Iron Quadrangle. Using a simple linear model, we found increasing pig iron production was

correlated with mine expansion and plantation expansion. Remote sensing observations indicate

direct impacts of mining on native vegetation were disproportionate to their small spatial extent,

while those of plantations were spatially extensive, as were their impacts on surrounding native

vegetation. Evidence of two indirect impacts also emerged during 1990–2010. Plantation expansion

decreased native forest regrowth, while mining-urban interactions increased deforestation pressures.

These findings suggest demand for steel affects the majority of land in QF and simulated scenarios

suggest these impacts could intensify during 2010–2030.

Increased pig iron production was correlated with increased iron ore production and mine expansion

(Figure 3.4A, B). Mine expansion in QF cleared native forests and native grasses (Figure 3.3),

thereby impacting the biodiversity of these ecosystems. Mining-related impacts on biodiversity are

often disregarded since they are typically small-scale and localized compared to those caused by

other land uses (Sonter et al. 2013a). This was true in QF where 7.5 times more native vegetation

was cleared by non-mining land uses during 1990–2010. Mine expansion, however, was dependent

on underlying geology and thus associated impacts were confined to ecosystems with certain

lithologies. In QF, iron ore deposits occur along high-relief rocky outcrops, where vegetation is

dominated by Campo Rupestre, an endemic, bio-diverse and highly threatened rocky grassland

ecosystem (Jacobi and do Carmo, 2008). Model simulations suggest mine expansion will clear 35%

of rocky grasslands between 2010 and 2030 under DEMAND (Figure 3.9). Therefore, while the

impacts of mine expansion on native vegetation were relatively small in area, impacts on this

specific ecosystem were disproportionately large. Spatially disproportionate impacts on biodiversity

can have important conservation implications when not identified or managed correctly. For

example, Brazilian legislation requires compensation for vegetation clearing (offsetting; Sonter et

al., 2014) at a rate proportional to the area impacted. Offsetting, however, ignores risks to

ecosystems associated with historical impacts (caused by other mines or land users) and thus

disregards their remaining extent.


Figure 3.9: Native vegetation cleared by mine expansion under DEMAND since 1990. (A) Area of vegetation cleared,

(B) percent of vegetation cleared.

Increasing pig iron production was also correlated with increased plantation charcoal production

and plantation expansion (Figure 3.4C, D). Unlike mine expansion, plantation expansion was

spatially extensive, occupying 6% of QF in 2010 and potentially 12% by 2030 under DEMAND

(Figure 3.6). Extensive plantation expansion was possible due to the extent of field availability;

20% of 2010 fields were converted to plantations by 2030 under DEMAND, and a further 5% under

PLANTCHAR. This finding suggests sufficient land is available in QF to overcome its share of

Brazil’s plantation charcoal deficit, which, if achieved, would reduce pressures on native forests

outside QF to supply this resource. It also supports broader findings on land availability in South

America to establish plantations for climate change mitigation (Piketty et al. 2009; Zomer et al.

2008) and, since plantations were allocated exclusively to fields, simulated results suggest

plantation expansion can be achieved without competition with urban and mining land uses

(Meyfroidt & Lambin 2008; Piketty et al. 2009).

Plantation expansion is also expected to affect surrounding native vegetation. Ecological benefits,

such as enhancing soil stability and increasing conservation value are possible (Paquette & Messier

2010; Ribeiro et al. 2009). However, plantations often have negative impacts on surrounding

biodiversity and this is especially true for Eucalyptus monocultures since they support few native

species (Hall et al. 2012). The specific nature of these impacts will be dependent on both the

landscape matrix and forestry management practices (Barlow et al. 2007; Lamb et al. 2005). In this

regard, a mix of farm-scale plantations with other land uses may be more favorable to maintain

landscape diversity than industrial operations found outside QF. Nevertheless, in QF, plantation

certification is rare and best-practice management is uncommon (May 2006). Additionally, fast-

growing exotics species can have negative impacts on ecosystem services, such as reduced water


availability (Jackson et al. 2005). While quantifying these tradeoffs with other ecosystem services is

beyond our scope, it is important to recognize that current environmental licensing does not require

any tradeoff management.

Several indirect impacts of land-use change on native vegetation also emerged during 1990–2010.

One indirect impact was caused by plantation expansion slowing native forest regrowth rates. In

QF, a small amount of regrowth occurred during 1990–2010 (Figure 3.3), as it did in other parts of

the Atlantic Forest (Lira et al. 2012; Teixeira et al. 2009). In QF, however, regrowth rates declined

significantly over time (Figure 3.5D). Two observations suggest this was indirectly caused by

extensive plantation expansion. First, the land area available for regrowth (i.e. fields) declined; four

percent of fields in 1990 were used by plantations in 2010 and simulations suggest 25% of fields

would be used by plantations in 2030 under DEMAND. Second, the most suitable land for regrowth

also declined. In QF, regrowth was most likely to occur close to rivers and on steep hill slopes,

which were sites also favored for plantation expansion (Figure 3.7), possibly due to low opportunity

costs (Piketty et al. 2009). These observations suggest a tradeoff emerged in QF between

plantations and regrowth, where plantation expansion increased on marginal agricultural land that

would have otherwise regrown with native forest species.

Tradeoffs between plantation expansion and native forest regrowth have been found elsewhere,

caused by emerging demands for land use (Diaz et al. 2011; Meyfroidt & Lambin 2008). In QF, it is

also possible this tradeoff was enhanced by recent revisions to Brazil’s Forest Code. Under this

policy, protected vegetation on private property must remain intact and, if cleared, revegetation with

native forest species is required (Brazil 2006). In the Atlantic Forest biome, protected vegetation

includes Areas of Permanent Preservation (APP; native forests on steep slopes, hilltops and riparian

zones) and 20% of native forests on privately owned land. Changes to the Forest Code now permit

small-scale farmers to partially reforest APP with agro-forestry or small-scale tree lots for

sustainable wood (including charcoal) production (Soares-Filho et al. 2014). These changes—and

their perception prior to formal declaration—help explain the observed extent and location of

plantation expansion in QF (Figure 3.6, Figure 3.7) and the decline in regrowth rates (Figure 3.5D).

Providing financial incentives for regrowth to land holders, such as the new Bolsa Verde program,

may enhance regrowth opportunities in QF; however, our results suggest that to be effective,

incentives must exceed both the opportunity costs of plantations and the financial incentives

currently encouraging their establishment in Brazil.

Evidence suggests deforestation pressures in QF also increased during 1990–2010, given that native

forest extent declined (Figure 3.6) yet high deforestation rates persisted (Figure 3.5C). Evidence


also suggests one potential indirect impact that increased deforestation pressures emerged from

mining-urban interactions. In QF, urban extent tripled during 1990–2010 (Figure 3.6) and cleared

.6% of the region’s native vegetation (Figure . ). Urbanization in mining regions is often driven

by increasing socio-economic opportunities (e.g. employment) associated with a growing mining

industry (e.g. rapid urbanization followed expansion of the Carajás iron ore mine in Pará; Roberts

1992). Belo Horizonte, however, is already a well established city, capable of sustaining itself

independent of the mining industry and, as such, it is unlikely mine expansion directly drives

urbanization in QF. Rather, urbanization has been indirectly affected by competition for land

between mining companies and urban developers, which is particularly intense along the

southeastern boundary of Belo Horizonte where both land availability and distance between mines

and urban areas have declined (Figure 3.8). This competition has escalated land value in these

locations and caused rapid land acquisition and development. Land use change simulations suggest

competition will intensify during 2010–2030 as the distance between mines and urban areas

continues to decline (Figure 3.8); future model refinement should capture this indirect impact into

model simulations.

Another indirect impact increasing deforestation pressures was potentially caused by plantation

expansion. During 1990–2010, the average rate of plantation expansion (field to plantation; Figure

3.5A) was of the same order of magnitude as deforestation (native forest to field; Figure 3.5C). This

finding suggests three potential processes are operating. Native forests are converted to fields before

transitioning to plantations, to avoid the illegal nature of a native forest to plantation transition.

However, no direct evidence of native forest to field to plantation was observed at the pixel-scale

during 1990–2010, suggesting either environmental legislation has been effectively enforced in QF

or a lag-time greater than years exists. Alternatively, fields are ‘displaced’ by plantation

expansion into native forests. This process would require an active driver maintaining field extent;

however, given the mountainous and hilly terrain of QF, it is unsuitable for agribusiness, farming

and ranching. Therefore it is possible that the deforestation transition captures fields in transition to

urban, rather land in transition to plantations or newly established cattle grazing pastures. To

conclude this with certainty would require a detailed analysis of forces driving deforestation in QF.

Results suggest global demand for steel drives iron ore production, mine expansion, plantation

charcoal production and plantation expansion (Figure 3.1). In QF, these relationships were

approximately linear during 1990–2010 (Figure 3.4) and thus simulated scenarios utilized linear

projections. However, increasing QF productive capacity through infrastructure extensions may

accelerate iron ore production relative to increasing demand for steel (Gurmendi 2013). Similarly

changes in production efficiencies may accelerate mine expansion relative to increasing iron ore


production if ore grades decline (Mudd 2007; Prior et al. 2012), or slow mine expansion rates if

mineral extraction technologies improve (InfoMine 2012). Changes in external factors may also be

influential, for example climate change mitigation incentives may accelerate plantation charcoal

production and plantation expansion rates relative to increasing demand for steel (The World Bank

2002). These factors indicate the diversity of forces potentially influencing the link between global

demand for steel, projection trajectories and regional land-use change in QF in the future.

While this study focused on demand for steel driving land-use change in QF, similar processes may

operate elsewhere. In other steel producing regions, mine expansion can be expected to have

spatially disproportionate impacts on geologically dependent ecosystems (Erskine et al. 2012),

landforms (Palmer et al. 2010) and livelihoods (Sonter et al. 2013c). Additionally, extensive

plantation expansion for charcoal production can be expected to become more common as steel

producing countries and companies begin to investigate opportunities to reduce greenhouse gas

emissions (Weldegiorgis & Franks 2014). Demand for other minerals is also expected to drive

extensive land-use change in these mining regions, where mineral resource extraction intersects

with the production of other resources and land uses (Moran et al. 2013).

3.5 Conclusion

Global demand for minerals is often considered an insignificant driver of land-use change because

the area impacted by mining is small. However, results from Brazil’s Iron Quadrangle suggest

increasing demand for steel drives extensive land-use change and has far-reaching impacts on

native vegetation. The direct impacts of mining were disproportionate to their spatial extent since

mine expansion caused a decline in specific, highly threatened, endemic ecosystems.

Comparatively, plantation expansion was spatially extensive, as were the impacts of plantations on

surrounding native vegetation. Evidence of two indirect impacts on native vegetation also emerged

during 1990–2010. Plantation expansion decreased native forest regrowth by rapidly consuming

available fields; while competition for land between mining companies and urban developers

increased deforestation pressures. In combination, these impacts affected the majority of land in

QF; however, many were poorly captured by current land management approaches, indicating

implications for achieving long-term sustainable development goals. In the future, these processes

may intensify in QF as global demand for steel increases; although production trajectories and land-

use transition rates will be dependent on other driving forces operating at multiple scales. In other

mining regions, similar processes may operate where global demand for minerals drives the

production of multiple resources (non-renewable and renewable) and is expressed through extensive

land-use change.

Laura Sonter Chapter 4: Offsetting the Impacts of Mining 45

CHAPTER 4 & CHAPTER 5

Objective 3: To investigate the influence of environmental management strategies on land use

change trajectories.

CHAPTER 4

Offsetting the Impacts of Mining

Objective 3a: To determine the effectiveness of biodiversity offsetting activities to mitigate

vegetation loss caused by mine expansion.

Preface: The purpose of this Chapter was to quantify the effectiveness of biodiversity offsetting

policies to mitigate the impacts on native vegetation caused by mine expansion. I built and

calibrated a spatially explicit land use change model to simulate future mine expansion and

biodiversity offsets, and I quantified the impacts of these conservation activities on future

deforestation trajectories. Results showed that Brazil’s biodiversity offset policy fails to mitigate

impacts on biodiversity because offsets do not avert sufficient vegetation loss. This paper makes

three major contributions to the thesis: (1) biodiversity offsetting represents an additional offsite

land requirement of global demand for steel; (2) information on land use change probability is

essential to avert deforestation; and (3) the offsite and indirect impacts of steel production were not

captured by biodiversity offsets, and therefore were also not mitigated. Note – the offset

investigated here seek only to mitigate the direct impacts of mining.

Future work opportunities: This paper illustrates many areas for further investigation. Two include:

(1) Investigate relationships between native vegetation and biodiversity to determine if offsets areas

are indeed achieving ‘like-for-like’ objectives. ( ) Determine if alternative offsetting approaches

can potentially achieve no net loss in QF, such may include landscape-scale conservation or

payments for ecosystem services. There is also opportunity to incorporate statistical matching

methods to identify appropriate offsite sites, with similar biodiversity value to mined land.

Acknowledgements: This paper has been published in the journal Conservation Biology. I would

like to thank Allaoua Saadi and Jackson Campos for discussions on biodiversity offsetting practices

in Brazil and Letícia Santos de Lima for translating preliminary interviews with industry

representatives. I am also grateful to Geordan Graetz and Chris Moran for commenting on a

previous draft.

Laura Sonter Chapter 4: Offsetting the Impacts of Mining 46

Offsetting the impacts of mining to achieve no net loss of native vegetation3

Abstract: Offsets are a novel conservation tool, yet using them to achieve no net loss of

biodiversity is challenging. This is especially true when using conservation offsets (i.e. protected

areas) because achieving no net loss requires avoiding equivalent loss. Our objective was to

determine if offsetting the impacts of mining achieves no net loss of native vegetation in Brazil’s

largest iron mining region. We used a land-use change model to simulate deforestation by mining to

2020; developed a model to allocate conservation offsets to the landscape under three scenarios

(baseline, no new offsets; current practice, like-for-like [by vegetation type] conservation offsetting

near the impact site; and threat scenario, like-for-like conservation offsetting of highly threatened

vegetation); and simulated non-mining deforestation

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CHAPTER 2 The Concept of Intensive Land Use345749/S... · 2019. 10. 9. · Laura Sonter Chapter 2: The Concept of Intensive Land Use 11 Figure 2.1: Simplified conceptual model of