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International Congress on EnvironmentalModelling and Software

6th International Congress on EnvironmentalModelling and Software - Leipzig, Germany - July

2012

Jul 1st, 12:00 AM

Impacts of Human Settlements on a CoastalLandscape: The Case of Rio Verde Basin, MexicoCuitlahuac Hernandez-Santiago

Martin Volk

Joerg A. Priess

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Hernandez-Santiago, Cuitlahuac; Volk, Martin; and Priess, Joerg A., "Impacts of Human Settlements on a Coastal Landscape: TheCase of Rio Verde Basin, Mexico" (2012). International Congress on Environmental Modelling and Software. 256.https://scholarsarchive.byu.edu/iemssconference/2012/Stream-B/256

International Environmental Modelling and Software Society (iEMSs) 2012 International Congress on Environmental Modelling and Software

Managing Resources of a Limited Planet, Sixth Biennial Meeting, Leipzig, Germany R. Seppelt, A.A. Voinov, S. Lange, D. Bankamp (Eds.)

http://www.iemss.org/society/index.php/iemss-2012-proceedings

Impacts of Human Settlements on a Coastal Landscape:

The Case of Rio Verde Basin, Mexico

Cuitlahuac Hernandez-Santiago1,2

, Martin Volk1, Jörg Priess

1

1Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany. 2Universidad del Mar, Oaxaca, Mexico (cuitlahuac.hernandez@ufz.de)

Abstract: Human settlements require natural resources to cover their needs for shelter, food, water, etc., which creates environmental impacts. This is particularly true for new rural settlements which obtain these resources from their nearby surroundings. Current analysis of land-use change has taken inhabited settlements as subject of study where population dynamics or socio-economic changes can be assessed. In the present study we analyse extent and patterns of land-use change found around four different types of settlements (permanent, intermittent, new and abandoned) in a coastal river basin in Mexico. The methodological steps of our investigation include analyses of a) multi-temporal land-use change, b) human population/settlements dynamics, c) correlation analysis for land-use change and four types of settlements and assessment of changes. The environmental conditions of these areas seem to be heavily impaired by overexploitation of natural resources. Preliminary results show differential loss of natural vegetation areas depending on settlement type and land cover. These impacts can also induce changes in hydrological flows and water related impacts in the lower basin. The results of our analysis show that there is an urgent need for preventive measures in view of potential new future settlements for example caused by domestic or international migration. Keywords: settlement, watershed management; land-use change, Rio Verde; Mexico. 1 INTRODUCTION

The loss of tropical forest in Mexico and the rest of Latin America has been a major concern that remains unsolved in natural resource management as described for instance by Tinker et al. [1996], Carr et al. [2005], Ferraz et al. [2009], Diaz-Gallegos et al. [2010] and Lambin et al. [2010]. According to the FAO [2010], Mexico´s annual forest change rate is around -0.52% for the decade between 1990 and 2000 and -0.3% for the decade of 2000 and 2010 which represent a slight decrease of the negative trend compared to the rest of Latin America (-0.47 %) and the Caribbean (-0.46 %). Nevertheless, in Mexico´s southern rural areas, subsistence farming and illegal wood extraction are the main direct drivers of forest clearance as stated by Flamenco-Sandoval et al. [2007]. Forest clearance creates indirect impacts because of the negative border effect that can reach from hundreds of meters up to kilometers as described by Skole et al. [1993] and Laurance et al. [1997], where Laurance [2000] and Ochoa-Gaona et al. [2007] indicates that negative collateral effects on ecosystem services take place. On the other hand Grau et al. [2003], Hecht et al. [2006] and Izquierdo et al. [2009] indicate that recovery of forest due to agricultural land abandonment has also been observed in neotropical ecosystems.

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Previous studies by Allen et al. [1985], Houghton [1991] and Mather et al. [1998] showed that land-use change analysis has used population dynamics as one of the main direct drivers of global deforestation. Geist et al. [2001] and Wibowo et al. [1999] describe that population dynamics has used indicators such as population growth, immigration-outmigration, population density, spatial distribution and life cycle to compare with deforestation. Geist et al. [2002] reviewed 152 study cases of deforestation in the tropics. The results showed that direct or indirect drivers such as infrastructure, agriculture, forestry, demographics, economics or technological, institutional and cultural factors can explain individually parts of land-use change. Multifactorial analysis identified synergies with more explanatory power rather than single factor causation. This added complexity also requires highly detailed data that is not always available. Regarding the mentioned problems, it is not just the growing population but also the rapidly growing number of settlements in the study area. Due to the existing climate change scenarios, these areas will be exposed to the impacts of increasing floods, droughts and high temperatures - and could lead to what was defined as “Environment refugees” by Westing [1992]. This situation can be aggravated by current phenomena of internal displacement for economical reason, which has been pointed out by Aide et al. [2004] and López et al. [2006]. Schubert et al. [2007] showed that the impact of migrant population can even threaten public security. Nevertheless, none of the studies showed the number of settlements as an explanatory variable for land-use change. Therefore, the objective of this work is to identify the relationship between human settlements and changes in land use. In particular, the main interest is to investigate how the changing number of settlement impacted land-use change. 2 DATA AND METHODS 2.1 Study area The study area is the Rio Verde Basin, which covers an area of 2,586 km². The basin is located southwest of the Oaxaca state in Mexico (see http://g.co/maps/v3x28). The terrain conditions are mountainous, with a steep gradient ranging from 0 to 2,536 meters above sea level. In the south-eastern region of study area, an important coastal section includes the Chacahua Lagoons National Park. This area is also known as Terrestrial Priority Region for Conservation. It shares its border with a Marine Priority Region for Conservation, both established by Biodiversity National Commission (CONABIO). The basin comprehends the territory of 15 municipalities. In the year 2010, the census, which was carried out by National Institute of Statistics and Geography-INEGI [2011b], registered 230 localities and 85,971 inhabitants. In this territory, the main land uses and production sectors are irrigated and rain fed agriculture, forestry, urban development as well as fishery and tourism. Highly productive coastal lands have been converted to pasture and crops, and the construction of a diverter dam during the 1980´s has promoted irrigated agriculture. This conversion has also impaired biodiversity and resulted in ecosystem loss, agrochemical pollution, illegal logging and poaching. The data used for this analysis include official records of censuses from Historical Archive of Localities by INEGI [2011a] for 1980, 1990, 1995, 2000 and 2005; vector layers of landscape features at a scale of 1:50,000 from INEGI [2000]; land cover layers derived from LANDSAT TM images for years 1986, 1993, 1997, 2000, 2003 and 2006 provided by Electricity Federal Commission (CFE). All dataset were in UTM Zone 14 North projection system. The study area counted 85,971 inhabitants and 230 localities in 2010, having the largest settlement 10,107 inhabitants and rural localities an average of 240 inhabitants.

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2.2 Methodology The analytical approach to identify the relationship between land-use change and human settlement dynamics includes three components: population dynamics, land-use change analysis and correlation and spatial analysis. Figure 1 shows an overview of the procedure.

Figure 1. Methodological procedure for correlation analysis of settlement and land-use change.

The land-use change analysis required a minimum set of functional classes that allowed the identification of the change in the surroundings of the settlements. Therefore a basic classification of six classes: Settlements, Agriculture, Disturbed Vegetation, Natural Vegetation, Water bodies and Other was used. Analysis to calculate differences between time steps were performed in IDRISI Taiga - Land Change Modeler by Eastman [2009] and change rate calculated using the geometrical equation (1).

[

]

(1)

Where: r = growth rate, Yt+n = value of variable Y in time n, Yt = initial value of variable Y, and n = number of years in period. Population and settlement dynamics included an analysis of population´s growth rate using equation (1) as recommended by INEGI [2010] and Palacio-Prieto et al. [2004]. Identification of settlement types using the number of settlements recorded in the period and the inhabitants for these settlements. Correlation of population/settlements and land-use change as main environmental impact was evaluated using regression analysis (α=0.05) to test the following Null hypothesis: The number of localities and the loss in natural vegetation are mutually independent. Where the alternative hypothesis is: The number of localities

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and the loss of natural vegetation are mutually dependent. To define type of land-use change and to asses affected area by settlement type, analysis were carried out in ArcGIS™ to establish spatial relationship of gains/losses in land use classes for every settlement using Thiessen polygons to define the neighbourhood area. 3 RESULTS 3.1 Multi-temporal land-use change The maximum increase observed in the land use annual change rates was 8.5% for the settlements area between 1993 and 1997. In contrast, the maximum decreasing annual change rate was -14% for natural vegetation between the years 1997 and 2000. Percentage changes for 1986-2006 for land-use class showed that Settlement areas had major increase (60.2%), Disturbed vegetation (39%) and Agriculture (28%) showed also significant increases. In contrast, the greatest decreasing rates can be observed with natural vegetation areas (-28.7%) and other (-12%). Evaluation of interclass exchange for 1986-2006 showed that Settlement areas increased by 37.6%, mainly obtained from Agriculture (17%), Disturbed Vegetation (16%) and Natural vegetation (4%) classes. 3.2 Population/settlement dynamics and projections In the study area, 283 localities were identified in the historical census data provided by INEGI [2011a] for the years of 1980, 1990, 1995, 2000, 2005 and 2010. The population analysis for 1980-2010 showed four patterns in the settlements:

a) 94 Permanent settlements (PermSet) that persisted along the period; b) 115 New settlements (NewSet) that appear as new records in the

censuses and persists in the period; c) 19 Intermittent settlements (IntermSet) which had at least one census with

zero inhabitants in the intermediate census of analysed period; and d) 55 Abandoned settlements (AbandSet) that showed no inhabitants in 2010

census. The number of localities in the study area showed an increase of 103% for the period between 1980 and 2010. The number increased from 112 to 228 settlements, although in 2010 283 settlement records were observed. The difference is represented by AbandSet. The change rate of the number of localities was calculated for every census period except for PermSet because the number of localities did not change. The NewSet change rate reached a maximum value of 14.2% during 1990 and 1995, and IntermSet showed a reduction (-10.4%) for the time between 1995 and 2000. But a major increase (17.4%) was observed for the period of 2005 and 2010. For AbandSet, minor variations were observed during this period with a major descend at the final period (-11.6%). Average change rates were calculated for the number of localities, obtaining 6.3% for NewSet, 3.3% for IntermSet and 2.1% for AbandSet. The population in the study area showed an increase of 93.7% during 1980-2010. The calculated population growth rate for the settlement types for every census period showed that PermSet reduced its change rate from 3.0% to 1.5% at the end of the period. The IntermSet population growth varied inconsistently from -13.1% to 20.5% and AbandSet also varied, but at the end it lost all inhabitants. The average change rate was calculated as 1.7% for PermSet, 4.3% for NewSet and -1.7% for IntermSet. 3.3 Correlation analysis for 4 types of settlements and land-use change To establish a correlation between land use change and number of settlements, the same time steps should be used. In our case, population census cover a broader range of time, therefore the time steps needed to match the years of the

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classified Landsat images were obtained by interpolation. As a first result, higher correlations were obtained between land-use classes related to vegetation loss (settlements, agriculture, disturbed and natural vegetation) and accumulated settlements (r

2=0.81-0.87, F=0.01), than the correlation of the same classes and

inhabited settlements (r2=0.70-0.78, F=0.02-0.04). This result allowed to reject the

null hypothesis of mutually independent variables with significance of α =0.05 for accumulated settlements but not for inhabited settlements. Figure 2 shows the percentual change differences on land use classes according to settlement type. Major changes ranking from 76% to 46% were observed in PermSet proximities, moderate changes (33% to 25%) were registered in NewSet vicinities, changes as low as 3% to 17% were related to AbandSet and minor changes (<3%) were obtained in IntermSet.

Figure 2. Percentage of land-use change for every class by settlement type

4 DISCUSSION AND CONCLUSIONS The main object of this work was to evaluate the pertinence of using settlements as a valuable variable to be considered in land-use change analysis. To achieve this, a comparative analysis was performed using census data to obtain population data and four types of settlements that was compared with land-use change observed in classified Landsat imagery. Spatial analyses were conducted using IDRISI and ArcGIS, and Microsoft Excel for statistical analysis. In the review of multiple studies by Geist et al. [2002], other authors have used single or multiple variables as explanatory proxies of land-use change. It is clear that complexity increases the possibility of obtaining a plausible model to reproduce or predict changes. Nevertheless due to less accessibility of data this kind of analysis is not always possible. In the meantime it is possible to explore basic variables such as settlements that is not yet included in literature. However, this work showed that accumulative counting of settlements had high correlation values and significances to changes in natural vegetation and disturbed areas. According to Carr et al. [2005], population as explanatory variable just helps to explain around 50% of observed deforestation. In our case the coefficient of determination indicate that population explains 77% of variation for the four classes. In comparison, total amount of settlements - including abandoned ones - explains 83% (r

2=91) of land use changes with a significance F of 0.01 (α=0.05).

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Geist et al. [2001] suggest that spontaneous transmigration (equivalent to New settlements in our case) are important because they are new entities that will create more fragmentation of the landscape and represent 14% of the total land-use change in reviewed cases. The same author also pointed out that natural expansion of permanent settlements are related to the 27% of changes, meanwhile in our case, this kind of settlements were related to 48% of change in natural vegetation in the proximities (Figure 3). Increasing number of new settlements could have the same meaning as the case described by Carr [2004] where immigrants looked for areas to develop subsistence agriculture and pasture fields. Further analysis is required to back this hypothesis. For intermittent settlements, Vanclay [1993] pointed out that arriving people try to cover their main needs such as shelter, food, firewood and use the resources available in the surrounding. But is also possible that their presence is related to labor markets in the nearby urban settlements and may be the reason why the impact observed over land cover classes is low.

Figure 3. Study area and river basin showing losses of natural vegetation and settlement types. Major changes were observed in the neighborhood of permanent

settlements. Abandoned settlements is a variable that needs further evaluation. Our results show that it is related to around 16-18% of changes in agricultural areas and areas of disturbed and natural vegetation. These results could indicate that people migrate again, leaving disturbed areas and loss in natural vegetation behind. This analysis also showed that adding the number of abandoned settlements to the number of settlements for every time step to perform the correlation analysis with land use raised the Significance F of the explanatory variable.

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Increasing population in certain areas is not just the result of settlement growth. The results of the studies of Aide et al. [2004] and Kniveton et al. [2008] indicate that migration from rural localities to urban areas will be result of expulsion due to natural disasters, loss of productivity, armed conflicts, international migration and climate change effects. In this context and the expected 214 million of migrants estimated by OIM [2012], the question arises: Would the cities be prepared to accommodate all these immigrants? Or will they be rejected to peripherals – which would lead to more disturbed surrounding areas? Therefore, the analysis of settlement types and impacts over land use needs assessment to make recommendations for land managers in rural municipalities. Finally, this exploratory work is the basis for further analysis to characterize rural settlement, degree of impact in their surroundings, geomorphological differences of sites, demographics, migration dynamics, agricultural practices, land tenure and correlation to socioeconomic processes in urban settlements. It is also important to highlight that environmental characteristics of the study area should be evaluated to identify possible synergies between explanatory variables of land-use change processes. ACKNOWLEDGMENTS This work was supported by a SEP-PROMEP grant to CHS and the Helmholtz Impulse and Networking Fund through Helmholtz Impulse and Networking Fund through Helmholtz Interdisciplinary Graduate School for Environmental Research (HIGRADE) Bissinger et al. [2008]. We thank Sven Lautenbach for analytical orientation and the Comisión Federal de Electricidad for land-use layers and Landsat data provided. REFERENCES Aide, T. M. and H. R. Grau, Globalization, Migration, and Latin American

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