TECHNICAL GUIDE FOR CHANGE DETECTIONUsing Multi-temporal Landsat TM Data:Ca River Basin Forest Cover Analysis

Bill SalasInstitute for the Study of Earth, Ocean, and SpaceUniversity of New HampshireEmail: bill.salas@unh.edu

September 23, 1998

Jake BrunnerWorld Resources InstituteEmail: jakeb@wri.org

IntroductionThis report was prepared as a contribution to an environmental assessment of the Ca river basinunderway by the Center for Natural Resources and Environmental Studies (CRES) at HanoiUniversity, Vinh University, the National University of Laos, and provincial authorities. The assess-ment is a component of the Resources Policy Support Initiaitive (REPSI), a 5-year project aimed atimproving the management of natural resources in the uplands of mainland Southeast Asia. TheWorld Resources Institute (WRI), an environmental policy research center based in Washington,D.C., is collaborating with local organizations to strengthen the supply, quality, and demand forpolicy analysis in support of this goal.

The Ca is a major river in northern Vietnam that flows from the highlands of northern Laos intothe Gulf of Tonkin near Vinh. The Ca river basin covers 27,000 km2 and is home to 2,672,000people. Two-thirds of the basin lies in Vietnam and one-third in Laos. Staff from the collaboratingorganizations are carrying out household-level research to assess environmental and developmenttrends in the basin. The forest cover analysis described in this report is intended to complement theresults of the field work by providing detailed information on forest cover change over the whole ofthe basin.

The Ca river basin is covered by the following Landsat TM images (see map):

Path/row127/47 April 4, 1989 November 20, 1991 October 8, 1992 To be acquired128/46 March 2, 1989 November 29, 1992 December 8, 1995 November 27, 1997128/47 March 2, 1989 November 29, 1992 December 8, 1995 November 27, 1997

128/46-47 cover most of Vientiane, Xieng Khouang, and Houa Phan Provinces in Laos. 127/47covers most of Nghe An Province in Vietnam and Bolikhamxai Province in Laos. The study area,excluding areas that were cloudy or water at any date, covers 71,000 km2, or about one-eighth of thecombined area of the two countries.

Landsat Thematic Mapper (TM) is a sevenchannel, multi-spectral scanner that acquires datain the visible, near-infrared, mid-infrared, andthermal infrared portions of the electromagneticspectrum. The data are acquired at a resolution of30 meters, so each individual picture element(pixel) of an image represents a surface area of 30by 30 meters. Bands 1, 2, and 3 are in the blue,green, and red regions of the visible portion ofthe spectrum. Band 4 is in the near-infrared,bands 5 and 7 are in the mid-infrared, and band 6is in the thermal infrared. True and false colorcomposites can be created by displaying three ofthe seven TM bands in the red, green, and bluecolor components (RGB) of the display.

The topography in the Ca river basin is extreme insome areas. Landsat data are acquired during themorning when the sun illuminates the surfacefrom the east. Mountain ridges and valleys can betherefore be identified by the relative location ofshadows and brighter areas. It is important toaccount for topographic influences when analyz-ing Landsat data.

Figures 1, 2, and 3 show three commonly usedcolor combinations of TM data.

Figure 1: True color composite where bands 3, 2,1 are displayed as RGB. In this color combination,forests appear light to dark green, while non-forested areas appear bright. Areas with more and/or darker vegetation look dark green. Areas withhaze or thin smoke in the valley have a blueish tint.

Figure 2: False color composite where bands 4, 3,and 2 are displayed as RGB. In this color combina-tion, forests appear reddish and the non-forestareas appear white to blueish.

Figure 3: False color composite where bands 5, 4,and 3 are displayed as RGB. In this color combina-tion, forests appear green, with the darker greenareas containing darker canopies or more inter-canopy shadowing. Non-forest areas look purple.Note that the haze in the valley is less visiblebecause of the haze-penetrating properties of thelonger wavelength bands used in this colorcomposite.

Interpretation of Landsat TM Data

FIGURE 1 Landsat TM true color (3,2,1)

FIGURE 2 Landsat TM false color (4,3,2)

FIGURE 3 Landsat TM false color (5,4,3)

1

Image scale: 1:100,000

The image processing techniques used for thisstudy were developed by NASA’s Landsat Path-finder project for analyzing large areas of forestcover in Southeast Asia using Landsat data. Thetechniques are relatively simple in order tofacilitate rapid processing and to take advantage ofuser experience in classifying forest cover change.

Figure 1 shows a small portion of a TM image.Reddish areas are forest and scrub fallow areas.Blueish and tan areas are cleared areas with non-photosynthetic vergetation and/or soils. Moun-tain shadows are black.

Processing Steps

1. Register the base image to UTM coordinates usingsatellite ephemeris data (i.e., data on the attitude ofthe TM sensor at the time of image acquisition).

2. Register the remaining images to base imageusing 25-35 ground control points to calculatefirst order transformation. Resample withnearest neighbor resampling.

3. Create a vegetation index layer using the ratioof near-infrared (band 4) and red (band 3). Theratio image suppresses the effects of shadowing.

4. Classify band 4 and the vegetation index imageusing an unsupervised, iterative function(ISODATA) that groups the pixels into 45 clustersof similar spectral statistics (figure 2).

5. Evaluate and label the clusters into five the-matic classes (forest, non-forest, water, cloud,and cloud shadow).

6. Identify mixed clusters. Mask the image andrecluster by repeating steps 3 and 4 for maskedareas. Repeat for all mixed clusters.

7. Recode the clusters into the five thematic classes.

8. Filter to remove single pixels caused by edgeeffects and mixed pixels.

9. Although the masking and re-clustering steps areperformed to prevent mixed classes, there willstill be some areas that are misclassified afterseveral iterations of masking and reclustering.These problem areas tend to be due to cloudedges, vegetation phenology, mountain shadows,or fire scars. Visual interpretation with handediting (digitizing) is required to remove remain-ing mislabeled areas. Figure 3 is an example ofthe final classification.

Landsat TM Processing2

FIGURE 1 Landsat TM image

FIGURE 2 45 clusters

FIGURE 3 Recoded thematic classification

Forest

Non-forest

Image scale: 1:100,000

Change detection is performed using “post-classification change detection.” Since the imagesare precisely co-registered, it is possible to trackchanges in land cover on a pixel-by-pixel basis bycombining the individual forest and non-forestclassifications.

Figures 1 and 2 show portions of the final 1995and 1997 classifications. Green areas are forest andred areas are non-forest. By overlaying these twoclassifications, we can map changes in forest coverbetween 1995 and 1997.

Figure 3 shows these changes, color-coded bytransition. Dark green areas were classified asforest in both 1995 and 1997 (transition of FF);tan areas were non-forest in both 1995 and 1997(NN); light green areas were non-forest in 1995and forest in 1997 (NF); red areas were forest in1995 and non-forest in 1997 (FN). Light greentherefore represents forest fallow/regrowth areasin 1997, while red represents new 1997 clearings.

The change detection image provides informationon both gross and net changes in forest cover, aswell as the spatial patterns of land use. To removechange artifacts due to misregstration, single pixelchanges are removed from the analysis.

Land Cover Change Detection 3

Image scale: 1:100,000

FIGURE 1 1995 classification

FIGURE 2 1997 classification

FIGURE 3 Changes 1995 to 1997

1995 1997 Transitionforest forest FFforest non FNnon forest NNnon forest NF

In areas of deciduous forest, the image acquisitiondate is critical. Scenes acquired late in the dry season(e.g., March or April) can be problematic forclassifying the extent of forest and non-forestbecause of confusion between non-forest areas anddeciduous forests with leaf-off. Once deciduous treeshave dropped their leaves, the undergrowth and soilconditions dominate the reflectance. As a result,areas of leaf drop cannot necessarily be discrimi-nated from non-forest areas. Figures 1, 2, and 3show TM data for an area of deciduous forest inLaos. Figures 1 and 3 were acquired prior to leafdrop (November 1992 and December 1995, respec-tively). The 1989 image (figure 2) was acquired inMarch, after leaf drop. The northern (upper) area ofthe image contains the deciduous forest and a fewsmall clearings. Discrimination between the leaf-offand cleared areas is not possible with this image.

Another problem in interpreting late dry seasonimages is the presence of large burn scars caused bythe annual burning of undergrowth and driedleaves in deciduous forests. Burning of undergrowthis carried out to promote the emergence of herba-ceous vegetation for pasture. It is also caused by theaccidental expansion of burning from field mainte-nance. The undergrowth burn scars have the samespectral properties as fields where the vegetation hasbeen burned in preparation for shifting cultivation(slash and burn). Distinguishing undergrowth burnscars from shifting cultivation fields is importantbecause they occur in intact forest and in recentlycleared forest, respectively.

When selecting Landsat data for an analysis offorest cover, it is important to select imagesacquired near the beginning of the dry seasonbefore leaf drop. But because of persistant cloudcover in the tropics, this is not always possible, andvisual interpretation may be needed to identifyareas that are deciduous forests that have dropedtheir leaves at the time of image acquisition.

Characteristics of leaf-off areas that can beobserved with Landsat TM data:

1. Large areas.

2. Occur in lower to mid-elevations.

3. Areas with an irregular shape that followelevation contours and/or soil conditions.

4. Appear forested in images acquired prior to leafdrop (see figures 1, 2, and 3).

Processing Issues:Phenology of Deciduous Forests4

Image scale: 1:100,000

FIGURE 2 1992 TM data with leaf on (November)

FIGURE 1 1989 TM data with leaf off (March)

FIGURE 3 1995 TM data with leaf on (December)

Regions of Southeast Asia with deciduous andevergreen forests and moderate to steep topogra-phy present a challenge for mapping forest coverwith single date Landsat TM data. Areas ofprimary forest, secondary forest, and scrub falloware difficult to separate. The arrows in figures 1and 2 show areas that were deforested in 1992 andregrew between 1992 and 1995, highlighting howyoung regrowth areas are difficult to separate fromthe surrounding forest.

For post-classification change detection studies,low classification accuracies are compounded andgreatly reduce the reliability of the final changedetection analysis. To maintain the accuracy of thefinal analysis, high accuracies of the input classifi-cations are needed.

WHY ARE THESE FOREST STATESDIFFICULT TO SEPARATE?

? Forest succession in the tropics can be rapid.Young stands can have complete canopy closurewith dense green biomass within a year of aban-donment.

? Near-infrared and visible reflectances saturaterapidly because of the greenness of the regrowthand density of the vegetation canopy.

? Differences in slope and aspect change thesolar illumination angle and local viewing angleof the TM. This causes variable reflectance thatconfounds the classification.

IMPACT

? Broad classification scheme is used in forestchange studies in the uplands of Southeast Asia toincrease the accuracy of the change detection.

CLASSIFICATION SCHEME

? Forest class contains areas of primary forest,secondary forest, and scrub fallow.

? Non-forest class contains areas of barrenland, grassland, rock, and agriculture.

RESULT

Areas of new forest fallow and secondary growthare classified as forest in the change detectionprocess. Areas that were classified as non-forest inan earlier image, but as forest in a later image, areknown to be areas of regrowth/forest fallow.

Processing Issues:Landsat TM Spectral Separation of Primary Forest,Secondary Forest, and Scrub Fallow

5

Image scale: 1:100,000

FIGURE 1 1992 TM data of cleared areas FIGURE 2 1995 TM data with regrowth areas

In the uplands of Southeast Asia there are twomain land uses that cause loss of forest: loggingand shifting cultivation. With Landsat TM data,these two land uses can be separated by the spatialand temporal characteristics of the clearings.Shifting cultivation areas will have numeroussmall clearings that undergo rapid recycling inand out of fallow. The clearings tend to be smallerthan 4 hectares. Areas that are fallow tend to bebrought back into cultivation after a few years andare not permanently abandoned.

Figures 1, 2, and 3 show an area under shiftingcultivation. Reddish areas in figures 1, 2, and 3are primary forest, secondary forest, and areas ofdense forest fallow. The darker the red and coarserthe texture are, the more mature the forest. Youngstands of regrowth have a smooth texture and arebright red. The bright blue, green, and whitishareas are cleared areas without trees. Note theshifting of cleared areas across the landscape.

Characteristics of shifting cultivation that can beobserved with Landsat TM data:

1. Small clearings (smaller than 4 hectares).

2. Patches of fallow in various stages of regrowth.

3. Occurs in higher elevations.

4. Rapid rotation of clearing, abandonment, andreclearing of fallow areas.

Shifting Cultivation6

Image scale: 1:100,000

FIGURE 1 1992 TM data

FIGURE 2 1995 TM data

FIGURE 3 1997 TM data

FIGURE 4 Shifting cultivation

Photo from

Luang Prabang, Laos

Logging and shifting cultivation can be separatedon the basis of their spectral, spatial, and temporalcharacteristics in TM data. Logged areas tend tohave larger clearings. They will typically belocated within stands of primary forest or verymature secondary forest (darker red areas withrougher texture). After logging, these areas willregrow quickly and tend not to be recleared.

Figures 1 and 2 show areas that appear to havebeen logged (areas within yellow rectanges). Infigure 3 these areas appear to have regrownrapidly (look reddish) after only a 5 year period.Note that a road is visible in the 1997 image, but isless evident in the 1995 image.

Characteristics of logged areas that can beobserved with TM data:

1. Larger clearings than shifting cultivation.

2. Clearings occur in mature forest.

3. Abandonment after clearing.

4. Roads are present, although they may bedifficult to detect.

In many areas, selective logging may be morewidespread than clear cutting. However, due tothe 30 meter resolution of Landsat TM, selectivelogging is very difficult to observe directly.

Clear-Cut Logging 7

Image scale: 1:100,000

Photo from

Landsat Pathfinder Slide Set

FIGURE 4 Logging in the tropics

FIGURE 1 1992 TM data

FIGURE 2 1995 TM data

FIGURE 3 1997 TM data

The rate of forest recovery depends on the type ofdisturbance (logging, shifting cultivation),intensity of distrurbance (period of cultivation,number of cultivation/fallow cycles, length offallow), soil type, and climate (seasonal precipita-tion and temperature). Large areas in the uplandsof Southeast Asia that were once forested can nolonger support forest succession and are replacedby grasses (e.g., elephant grass). This ecologicalstate is known as arrested succession. Figure 4shows an area of arrested succession in northernThailand.

Multi-temporal Landsat data can be used toidentify areas in arrested succession. These areascan be identified by the following characteristics:

1. Areas in upper elevations that were onceforested (yellow circle in figures 1, 2, and 3).

2. Areas that have the spectral properties ofgrassland (i.e., low reflectance in near- andmid-infrared relative to forest, scrub, and activeagriculture).

3. Areas where forest succession has stopped.These areas have spectral properties that do notchange over time in contrast to the rapidlychanging spectral properties of secondarygrowth and fallow areas.

Arrested Succession8

Image scale: 1:100,000

FIGURE 4 Arrested succession, northern Thailand

Photo from

Mae W

ang Watershed, T

hailand

FIGURE 1 1992 TM data

FIGURE 2 1995 TM data

FIGURE 3 1997 TM data