Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-1

DETECTION AND ANALYSIS OF DEFORESTATION IN CLOUD-CONTAMINATED LANDSAT IMAGES: A CASE OF TWO PHILIPPINE PROVINCES WITH HISTORY

OF FOREST RESOURCE UTILIZATION

Meriam Meralles Makinano Faculty, College of Engineering and Information Technology, Caraga State University,

Ampayon, Butuan City 8600, Philippines; Tel: +63-85-3412296 E-mail: meriam.makinano@gmail.com

Jojene Rendon Santillan, Enrico Camero Paringit

Research Laboratory for Applied Geodesy and Space Technology, Department of Geodetic Engineering and Training Center for Applied Geodesy and Photogrammetry, University of the

Philippines, Diliman, Quezon City 1101, Philippines

KEY WORDS: Deforestation, Cloud Contamination, Remote Sensing, GIS. ABSTRACT: This paper presents an approach in extracting multi-temporal land-cover information (years 1976-2001) from cloud-contaminated remotely-sensed images acquired by the Landsat Multi-Spectral Scanner (MSS) and Enhanced Thematic Mapper plus (ETM+) sensors in order to detect and analyze deforestation and other types of land-cover change (LCC) in two forest resource-rich provinces of Agusan del Norte (ADN) and Agusan del Sur (ADS) in Mindanao, Philippines. The cloud contamination problem was addressed by developing a cloud-and-shadow masking algorithm comprising of image segmentation and Maximum Likelihood classification to remove clouds and shadows. Forest-cover change from 1976-2001 were then detected using a Support Vector Machine classifier, and a post-classification change detection algorithm in portions of the land-cover maps un-contaminated by clouds and shadows. Geographic Information System (GIS) and logistic regression analyses were employed to characterize deforestation in ADN and ADS and to determine the significance and magnitude of the relationship between the detected deforestation and bio-physical and socio-economic factors. Major results showed that deforestation and other types of LCC can be detected, characterized and analyzed from cloud-contaminated Landsat images using the integrated image cloud masking, SVM classification, GIS and logistic regression approach. This study is a significant contribution to LCC research by providing a series of techniques to understand deforestation based on cloud-contaminated Landsat images. 1 INTRODUCTION In the Philippines, deforestation and forest degradation are the most prevailing land-cover change (LCC) processes, and have been the major reasons behind flooding, acute water shortages, rapid soil erosion, siltation, and mudslides that have proved to take its toll not only on the environment and properties but also in human lives (Moya and Malayang III, 2004). Hence, detecting deforestation and other types of LCC and determining factors contributing to it are important, as this could be a first step in controlling forest loss and is necessary in comprehensive forest management planning and formulation of appropriate forest policy (Grainger, 1993). Conversely, analyzing forest cover change and identifying its major drivers are important from a planning perspective because they provide a means to create and evaluate strategies that attempt to mitigate its negative effects (Wilson and Lindsey, 2005). While the roles of remote sensing (RS) and Geographic Information System (GIS) have become significant in LCC researches, studying LCC in tropical areas like the Philippines is hampered by lack of good RS images due to the presence of clouds and cloud shadows (Tseng et al., 2008). Cloud cover contamination of remotely-sensed images acquired by optical passive satellite sensors has severely restricted the regular exploitation of these images in various application fields such as in land-cover mapping. Clouds and their shadows often mask large parts of the images, thus reducing the amount of land-cover information and preventing its

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-2

Figure 1. Map showing the provinces of Agusan del Norte and Agusan del Sur.

contiguity in time and in space. Hence, the utilization of RS and GIS technologies to understand the process of land-cover change especially deforestation at a finer scale are limited in this stituation (Melgani, 2006). Synthetic Aperture Radar (SAR) images overcome the cloud contamination problem but the availability of such images and their long-term and multi-temporal capabilities are often inadequate for studies that require immediate images. The objective of this paper is to present an approach in extracting multi-temporal land-cover information (years 1976-2001) from cloud-contaminated remotely-sensed images acquired by the Landsat MSS and ETM+ sensors in order to detect and analyze deforestation and other types of LCC in two forest resource-rich provinces of Agusan del Norte (ADN) and Agusan del Sur (ADS) in Mindanao, Philippines. 2 THE STUDY AREA The provinces of ADN and ADS (Figure 1) belong to the so-called “Eastern Mindanao Corridor” where 75% of the country’s timber resources are extracted (CEPF, 2005). The two provinces have utilized their forest resources extensively resulting from the establishment of logging and timber industries way back in the 1950s (Paler et al., 1998) that continue to operate until this time by way of forest license agreements issued by the Philippine government to private corporations and non-government organizations. These industries have contributed greatly to the economy of both provinces and to the Philippines as a whole (Rapera et al., 1987); however, they are often blamed for decades of rampant upland forest destruction and significant changes in land-cover whose ecological aftermath continues to unfold in the valleys below. While the logging industries may have direct connection to deforestation and other types of LCC in the Agusan provinces, the contributions of other equally relevant factors associated with deforestation such as agricultural expansion, wood extraction, expansion of infrastructure, population growth, economic and technological factors, policy/institutional factor, land characteristics, bio-physical environment, and government policy failures, among others (Geist and Lambin, 2002) maybe overlooked. Hence, there arises a necessity to ascertain what were the factors associated with deforestation in these two provinces. 3 METHODS The methodology is divided into three phases: (i.) Landsat image analysis to derive multi-temporal land-cover and change maps, (ii.) GIS analysis of detected forest cover change, and (iii.) statistical analysis to determine the degree of association of bio-physical and socio-economical factors with deforestation. 3.1 Landsat Image Analysis Image Preprocessing: Landsat 2 MSS image acquired on April 17, 1976 covering the study area was downloaded free-of-charge from the University of Maryland - Global Land Cover

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-3

Facility (GLCF) website (http://glcf.umiacs.umd.edu), while Landsat 7 ETM+ image acquired on May 22, 2001 was also obtained free-of-charge from the U.S. Geological Survey (http://edcsns17.cr.usgs.gov/EarthExplorer/). These 8-bit images, with pixel resolutions of 57 and 30 meters, respectively, were already orthorectified. Comparisons with ground control points selected from topographic maps as well as image-to-image comparisons showed that the images were geometrically correct (with total root mean square error of less than half a pixel for each image) and are co-registered. This eliminates the error due to image misregistration in the change detection and analysis. The images were then radiometrically corrected to at-sensor radiance and reflectance using the standard Landsat calibration formulas and constants to eliminate errors due to differences in sun illumination during image acquisitions. A dark-object subtraction was also implemented to correct the images from atmospheric effects. Normalized Difference Vegetation Index (NDVI) images and, in the case of Landsat MSS, synthetic reflectance bands were also created to supplement the limited number of bands of each image dataset. This was found necessary in order to increase the number of R-G-B band combinations that could aid proper interpretation during the course of image classification. These steps as well as subsequent image analyses were done using Environment for Visualizing Images (ENVI) Version 4.4. Cloud and Shadow Masking: A cloud and shadow detection technique was developed to eliminate the error and confusion that cloud cover and shadows may introduce to the extraction of land-cover information during the image classification process. The technique is generally composed of two steps: (i.) manual segmentation of cloud and shadow contaminated regions of the image, (ii.) application of Maximum Likelihood supervised classification to label pixels contaminated and not contaminated with clouds and shadows in the segmented regions. Land-cover Classification and Change Detection: The cloud-and-shadow free, radiometrically calibrated and atmospherically corrected Landsat images (as well as other by-products such as NDVI and synthetic bands) were subjected to supervised classification to derive the 1976 and 2001 land-cover maps. Eight (8) land-cover classes namely, Forest, Rangeland, Built-up, Palm Trees, Cropland, Bare Soil, Exposed Rocks and Water, were identified from the images through visual interpretation using existing land-cover maps, topographic maps and Google Earth images as references. Representative samples of each class were collected from the images for supervised image classification and accuracy assessment. The classification algorithms included traditional classifiers such as Minimum Distance, Mahalanobis Distance and Maximum Likelihood and the recently developed Support Vector Machines (SVM) classifier. SVM was implemented as a non-linear classifier using the Radial Basis Functions (RBF) kernel available in the ENVI 4.4 image analysis software. For both the 1976 and 2001 image dataset, each classifier was implemented using various combinations of input bands. The use of 4 classifiers and various combinations of image bands and by-products was done to generate several classified images and selecting from these outputs the best classified image in terms of classification accuracy. A digital elevation model (DEM) from the Shuttle Radar Topography Mission (SRTM) was also included as additional band during image classification. The two land-cover maps derived were then subjected to post-classification comparison change detection analysis (Coppin et al., 2004) to examine the location, extent and distribution of land-cover change in the study area. The 2001 land-cover map was first re-sampled to 57-m resolution using nearest neighbor method prior to change detection. Because of cloud and shadows present in the images used (“No Data” in the LC maps), only portions of the LC maps that both have data in 1976 and 2001 were subjected to change detection analysis. Deforestation and land-cover change statistics were also computed. 3.2 GIS Forest Cover Change Analysis The detected changes in forest cover, in the form of a “change-no change in forest cover” map, were visualized and analyzed in a GIS. The analysis (using Arcview GIS 3.2) involved characterization and visualization of the detected changes vis-à-vis sets of georeferenced bio-physical and socio-economic factors hypothesized to be associated with deforestation (Table 1).

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-4

Table 1. Definitions of geo-referenced bio-physical and socio-economic factors. Factor Description

Bio-

physical

ELEV Elevation of the Agusan provinces.

SLOPE Slope of the Agusan provinces.

DISTRIV Distance to major river networks in the Agusan provinces.

Socio-

economical

DISTNEWRD Distance to the new roads since 1976 to 2001.

DISTNEWBUILT Distance to the new built-up areas since 1976 to 2001.

POPDENCHANGE Change in population density of the Agusan provinces from 1976-2001.

DIST_TLA-IFMA Distance to the combined land parcels subjected to Timber License Agreements and

Integrated Forest Management Agreement.

DIST_CBFMA-

CBRM

Distance to the combined land parcels subjected to Community Based Forest

Management Agreement (CBFMA) and Community-Based Resource

(CBRM)Management Agreement.

Point layers of “changed” and “no change” in forest cover (hereafter referred to as “FCOVER”) were made from the forest-cover change map. Overlay analysis was then performed to populate

the attribute of the FCOVER pixels with their corresponding socio-economic and bio-physical factor values. The resulting tabular data was exported to a spreadsheet file and further analyzed. The mean factor values of ‘changed’ and ‘no change’ samples were computed and were displayed graphically for both qualitative and quantitative analyses. The analyses of the mean factor values was made in order to gain insights on the possible similarities or differences in trends between the two provinces’ forest cover in relation to the identified bio-physical and socio-economical factors. 3.3 Logistic Regression Analysis Logistic regression analysis was conducted for each province to explain forest cover change in ADN and ADS vis-à-vis bio-physical and socio-economic factors. The multivariate logistic regression equation used in the analysis is of the form (Millington et al., 2007):

1

( ) , 1

z n

i izi

ex z x

eπ α β

=

= = ++

∑ (1)

where π(x) is the probability that the dependent factor (FCOVER) equals 1, α is the equation constant, βi is the coefficient of predictor factor xi, and i= 1,2,3…n number of independent factors. Each of the regression coefficients describes the size of the contribution of that factor to the outcome. In this study, the modeled “outcome” is change in forest cover (i.e., deforestation). The logistic regression coefficient, β was primarily used in explaining the relative influence of the factors to the deforestation in the area. A positive regression coefficient implies that if the factor value is large, the probability of deforestation is high; if the factor value is small, the probability of deforestation is low. For factors with negative regression coefficient and have large values, the probability of deforestation is low; if the factor value is small, the probability of deforestation is high. 4 RESULTS AND DISCUSSION 4.1 Land-cover Maps of ADN and ADS The land-cover maps of ADN and ADS for 1976 and 2001, showing areas with data common to both years (i.e., cloud covered areas in 1976 and 2001 were excluded), are depicted in Figure 2. These land-cover maps have overall classification accuracies of 94.99% and 98.25% for 1976 and 2001, respectively. The 1976 land-cover map was the result of SVM-RBF classification of band combinations of Landsat MSS surface reflectance bands (Bands 4, 5 6 and 7), NDVI and DEM. This result was the highest among 32 classifications that utilized 6 various combinations of inputs bands subjected to four classifications algorithms. On the other hand, the 2001 land-cover map was the result of SVM-RBF classification of Landsat ETM+ surface reflectance & temperature bands (Bands 1-7) and DEM; this has the highest classification accuracy among 8

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-5

(a.) (b.)

Figure 2. Land-cover maps of ADN and ADS for 1976 (a) and 2001 (b). “No Data” represents clouds and shadows detected in the Landsat images used.

classifications that utilized 2 various combinations of inputs bands subjected to 4 classification algorithms. The cloud-and-shadow masking algorithm used was able to detect 1,258,494 pixels of 57-m resolution in the 1976 Landsat MSS image. This accounts for 36.70% of the combined area of ADN and ADS of 11,141.747 sq. km. For the 2001 Landsat ETM+ image, a total of 1,747,105 cloud and shadow pixels of 30-m resolution were detected which represents 14.11% of the study area. The portions with land-cover data common to both years (i.e., excluding all cloud-and-shadow contaminated areas in 1976 and 2001) used in the change analysis amounts to 6,178.49 sq. km. or 55.45% of the study area.

4.2 Land-cover Change in ADN and ADS The most pronounced changes in land-cover in ADN are the conversions of forest and rangeland (Figure 3). Quantitative assessments through change detection show significant decrease in forest cover by 32% (or about 255.30 sq. km.) while rangeland areas increased by 92% (about 327.86 sq. km.) during the 25-year period. Forest to rangeland is the major land-cover change in ADN from 1976 to 2001 (Figure 4). Although deforestation due to increase in rangeland is significantly evident, “re-forestation” of rangeland areas from 1976 to 2001 was also present. In the case of ADS, increase in cropland and decrease in forest cover is the most significant land-cover change in terms of change in land area. Quantitatively, these translate to 156% increase in cropland (or about 198.47 sq. km.) and about 6% decrease in forest cover (or 113.42 sq. km.). The two most prominent land-cover change types from 1976 to 2001 in this province are the conversions of rangeland to forest and forest to palm trees. Considering errors in classifications, these two land-cover change types are almost identical in magnitude. A third major type of change is that of conversion of forest to rangeland. It is very apparent that the type and magnitude of changes in land-cover between the years 1976-2001 are different for each province based on the top 10 land-cover change types. In ADN, the major land-cover change type is “forest to rangeland”. A decrease in forest area in this province was found to be due to the conversion of forest area in 1976 to rangeland areas in 2001. In the case of ADS, an opposite

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-6

Mean Factor Values of Deforested and Retained Forest Locations

0

2000

4000

6000

8000

10000

12000

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

Ret.For.

Def.

ELEV(*10)

(m)

SLOPE(*10)

(%)

DISTRIV

(m)

DIST

NEWRD

(m)

DIST

NEWBUILT

(m)

DIST

TLA-IFMA

(m)

DIST_CBFMA

CBRM

(m)

POP

DENCH(*10)

(per/sq.km)

Factors

Facto M

ean Values

Agusan del Norte

Agusan del Sur

Figure 5. Mean factor values of deforested (Def.) and retained/unchanged forest pixels (Ret. For.).

type of major change was found which is “rangeland to forest”. The question on why there were such kinds of conversions that drove deforestations may be answered by taking into account the

interplay between various bio-physical and socio-economical factors in both provinces. 4.3 GIS-based Characterization of Deforestation Results of GIS-based characterization (Figure 5), indicates that deforestation in ADN is located in areas with higher elevation and steeper slope compared to ADS. Deforestation occurrence in ADN is located much farther from the river (approx. 3km.) compared to ADS where deforestation can be found in areas less than 1km away from the river. It was observed that deforestation in ADN is located in areas nearer to new built-ups and new roads compared to ADS. Deforestation in ADN also occurred in areas with higher population density compared to ADS. This may be

0

50

100

150

200

250

300

Forest to Rangeland

Palm Trees to Cropland

Rangeland to Forest

Palm Trees to Rangeland

Bare soil to Rangeland

Forest to Palm Trees

Cropland to Palm Trees

Cropland to Rangeland

Rangeland to Palm Trees

Bare soil to Palm Trees

Land-cover change type

Area of change, in sq. km.

0

50

100

150

200

250

300

350

Rangeland to Forest

Forest to Palm Trees

Forest to Rangeland

Palm Trees to Cropland

Palm Trees to Rangeland

Rangeland to Palm Trees

Palm Trees to Forest

Cropland to Palm Trees

Bare soil to Palm Trees

Bare soil to Forest

Land-cover change type

Area of change, in sq. km.

Figure 4. Top 10 land-cover change types

in ADN and ADS.

Figure 3. 1976-2001 land-cover statistics of ADN and ADS.

0

100

200

300

400

500

600

700

800

900

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

Land-cover Type

Area, in sq. km.

1976 Land-cover area

2001 Land-cover area

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

Bare Soil

Built-up

Cropland

Exposed Rocks

Forest

Palm Trees

Rangeland

Water

Land-cover Type

Area, in sq. km.

1976 Land-cover area

2001 Land-cover area

(a.) ADN (a.) ADN

(b.) ADS

(b.) ADS

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-7

due to the fact that population density, built-ups and roads in ADN are denser than in ADS. With regards to the occurrence of deforestation in ADN with respect to the TLA-IFMA and CBFMA-CBRM parcels, it is located in areas farther from TLA-IFMA but nearer to CBFMA-CBRM parcels compared to ADS. This scenario can be attributed to the fact that the largest and the earliest TLA-IFMA concessions in the Agusan province were located in ADN. In general, unchanged forest areas (Ret. For.) have higher mean factor values than deforested areas in both provinces of ADN and ADS indicating that remaining forests in the study area are located in less accessible areas (e.g., in highly elevated areas with steep slopes and far from roads). 4.4 Results of Logistic Regression Analysis The results of logistic regression based on combined bio-physical and socio-economic factors provided significant results as to what has influenced deforestation in the Agusan provinces (Figure 6). For ADN, the bio-physical factor DISTRIV was found to be positively related to deforestation, followed by socio-economic factors POPDENCHANGE and DIST_CBFMA-CBRM. Compared to DISTRIV, the contributions of these two socio-economic factors are minimal. The bio-physical factors ELEV and SLOPE, and the socio-economic factors DISTNEWRD and DIST_TLA-IFMA, were all found to be negatively related to deforestation, thus, the probability of deforestation decreases as the values of these factors increases. Although the socio-economic factor DISTNEWBUILT was found to be a contributor to deforestation, its effect is minimal. In the case of ADS, DISTNEWBUILT, DIST_TLA-IFMA, POPDENCHANGE, and DISTRIV are positively related to deforestation (i.e. when their value increases, the probability of deforestation increases). Although the socio-economic factor DISTNEWRD was found to be positively related to deforestation, its effect is insignificant. ELEV, SLOPE and DIST_CBFMA-CBRM were the factors that decrease the probability of deforestation in this province. 5 CONCLUSIONS This study has provided an analysis of deforestation and other types of land-cover change in the Agusan provinces by using RS and GIS methodology. A series of techniques were provided to relate deforestation with bio-physical and socio-economic factors using RS images with severe cloud cover. The results imply that it is possible to analyze deforestation using cloud contaminated RS images. Results of this study exemplified the fact that although the two Agusan provinces have history of forest resource industry, the presence of these industries is not the most prominent factor that influenced deforestation. The factors associated with deforestation vary in ADN and ADS. Thus, it is concluded that the factors influencing deforestation in one area may not be the same factors

Figure 6. Computed logistic regression coefficients of bio-physical and socio-economic factors indicating the

association of these factors to deforestation.

Proceedings of the 31st Asian Conference on Remote Sensing (ACRS 2010): “Remote Sensing for Global Change and Sustainable

Development”, November 1-5, 2010, National Convention Center, Hanoi, Vietnam

Technical Session – TS 02 (Forest Resources)

pp. TS 02 -02-8

that can influence deforestation in another area. Deforestation is indeed an interplay between several bio-physical and socio-economic factors. This study demonstrated the usefulness of RS and GIS not only in obtaining accurate information on the location, extent and type of land-cover change (including deforestation) but also in characterizing the relationships of the detected changes with bio-physical and socio-economic factors. With statistical analysis, the information and the characterizations can be expounded further leading to a more comprehensive analysis of the deforestation phenomenon. ACKNOWLEDGEMENT The Philippine Council for Advanced Science and Technology Research and Development of the Department of Science and Technology (PCASTRD-DOST) financially supported this research through a graduate scholarship grant in M.S. Remote Sensing provided to M.M. Makinano. REFERENCES CEPF (Critical Ecosystems Partnership Fund), 2005. An Overview of CEPF’s Portfolio in the

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Geist, H.J. and E.F. Lambin, 2002. Proximate causes and underlying driving forces of tropical deforestation, Bioscience, 52, pp. 143–150.

Grainger, A., 1993. Controlling Tropical Deforestation, Earthscan Publications. Melgani, F., 2006. Contextual reconstruction of cloud-contaminated multitemporal multispectral

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Moya, T.B. and B.S. Malayang III, 2004. Climate Variability and Deforestation–Reforestation Dynamics in the Philippines, Environment, Development and Sustainability, 6, pp. 261-277.

Paler, D., Shinohara, T., del Castillo, R. and I. Nomura, 1998. Development of industrial tree plantation in the Administrative Region No. 10 in Mindanao Island, Philippines: with two case studies, Journal of Forest Research, 3, pp. 193-197.

Rapera, R., Fernandez, V., Villaflor, A.A., Corpuz, E., Villanueva, M., Bacani,F., Caraan,P., and S. Garnace, 1987. Rationalizing the Wood Industry, Forest Development Center, College of Forestry, University of the Philippines, Los Banos, Laguna.

Tseng, D., Tseng, H. and C. Chien, 2008. Automatic cloud removal from multi-temporal SPOT images, Applied Mathematics and Computation, 205, pp. 584-600.

Wilson, J. and G. Lindsey, 2005. Socioeconomic correlates and environmental impacts of urban development in a Central Indiana landscape, Journal of Urban Planning and Development, 3, pp. 159-169.