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Characterizing the Spatial Distribution of Giant Pandas in China Using MODIS Data and Landscape Metrics

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Characterizing the Spatial Distribution of Giant Pandas in China Using MODIS Data and Landscape Metrics Characterizing the Spatial Distribution of Giant Pandas in China using MODIS Data and Landscape Metrics Xinping Ye March, 2008 Course Title: Geo-Information Science and Earth Observation for Environmental Modelling and Management Level: Master of Science (Msc) Course Duration: September 2006 - March 2008 Consortium partners: University of Southampton (UK) Lund University (Sweden) University of Warsaw (Poland) International Institute for Geo-Information Science and Earth Observation (ITC) (The Netherlands) GEM thesis number: 2006-30 Characterizing the Spatial Distribution of Giant Pandas in China using MODIS Data and Landscape Metrics by Xinping Ye Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation for Environmental Modelling and Management Thesis Assessment Board Name Examiner 1 (Chair) Dr. J. (Jan) de Leeuw (ITC) Name Examiner 2 Dr. Ir. T.A. (Thomas) Groen (ITC) External Examiner Prof. Terry Dawson (University of Southampton) Primary Supervisor Prof. Dr. A.K. (Andrew) Skidmore (ITC) Secondary Supervisor Dr. A.G. (Bert) Toxopeus (ITC) International Institute for Geo-Information Science and Earth Observation Enschede, The Netherlands Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract Although forest fragmentation has been recognized as one of major threats to wild panda population, little is known about the relationship between panda distribution and forest fragmentation. This study is unique as it presents a fruit attempt at understanding the role of forest fragmentation on panda distribution for the entire wild panda population. A remote sensing based approach was proposed for characterizing the distribution of giant panda habitat using the MODIS EVI time series and landscape metrics. A five-class land cover map was generated from a complete year of uninterrupted MODIS 250m EVI data for 2001 by combining ISODATA and a Neural Net classifier. Fifty-two class-level landscape metrics were calculated (for two forest classes) in FRAGSTAT 3.3 using a 3km×3km moving window; and eight metrics that best captured the variation in forest configuration among landscapes were selected as representative metrics by a landscape metrics reduction procedure. These eight metrics measure three aspects of the forests: patch area/density/edge, patch proximity/connectivity, and patch contagion/interspersion. All representative metrics were significantly different (P < 0.05) between panda presence and absence; and the spatial configurations of the forests among five mountain regions were also heterogeneous. The forest I class occupied by the giant panda in Qinling were less fragmented, while the forests occupied by the giant panda in Xiangling and Liangshan were most fragmented among five regions. Using a forward stepwise logistic regression procedure, four landscape metrics were significant at P < 0.01 and included into the final logistic regression model, i.e. largest patch index, edge density, clumpy index of the forest I class, and mean patch area of the forest II class. These metrics indicate that the giant panda appear sensitive to patch size and isolation effects associated with forest fragmentation, and the forest I plays an important role in distribution of giant pandas. The performance of logistic regression model was significantly improved by applying a knowledge- based control to the modelling (P < 0.05). However, a low R-square value (0.451) of the model indicated that the landscape metrics alone can not effectively explain the distribution of the giant panda. The findings of this study have implications for the design of effective conservation plans for wild panda population limited by forest fragmentation. The presented approach may be applied to routine habitat monitoring and habitat evaluation for the giant panda. Keywords: Ailuropoda melanoleuca, giant panda, spatial distribution, forest spatial configuration, MODIS 250 m EVI, landscape metrics, logistic regression. i Acknowledgements I would like to express my sincere appreciation to all those who helped and supported me in one or another way during this course of the study. Special Thanks to the Erasmus Mundus GEM-MSc Consortium (University of Southampton, UK; Lund University, Sweden; Warsaw University, Poland; and ITC, The Netherlands) and the European Commission for fully sponsoring my MSc studies. It has been a wonderful and rewarding experience to study in four European countries. I would like to thank my supervisors, Prof. A. K. Skidmore (Natural Resources Department, ITC) and Dr. A.G. Toxopeus (Natural Resources Department, ITC), for their time, patience, criticisms, and suggestions. I would have been lost without their guidance. I wish to also thank Mr. Tiejun Wang (PhD candidate, ITC) and his wife Ms. Wen Xue (Foping Nature Reserve, China), for their invaluable knowledge sharing and suggestions, concern and encouragement while pursuing this study. I would also like to express my sincere appreciation to: Dr. Changqing Yu (Tsinghua University, China) for constructive suggestions and technical supporting; Mr. Xuelin Jin (Forestry Department of Shaanxi Province, China) for technical supporting and knowledge sharing; Dr. Lars Eklundh (Lund University) for helping me to manage the TIMESAT program; Dr. Xuehua Liu (Tsinghua University, China) for suggestions and encouragement; Mr. Zhanqiang Wen (State Forestry Administration of China) and Mr. Yange Yong (Foping Nature Reserve, China) for providing helps during the fieldwork in China. Thanks also go to Prof. Peter Atkinson, Prof. Petter Pilesjo, Prof. Katarzyna Dabrowska, Andre Kooiman, Stef Webb, Karin Larsson, and Jorien Terlouw, for their support during the course of the study. To my family and friends, thanks for your love, supports and encouragement in all my endeavors. To my fellow GEM students, thanks for the wonderful time together. ii Table of contents 1. Introduction ............................................................................................ 1 1.1. Background and Significance.................................................................. 1 1.1.1. The giant panda .................................................................................. 1 1.2. Research problem.................................................................................... 4 1.3. Research objectives ................................................................................. 5 1.3.1. General objective................................................................................ 5 1.3.2. Specific objectives.............................................................................. 5 1.4. Research questions .................................................................................. 5 1.5. Hypotheses .............................................................................................. 5 1.6. Research approach................................................................................... 6 2. Materials and methods............................................................................ 7 2.1. Study area................................................................................................ 7 2.2. Data description....................................................................................... 8 2.2.1. Time-series MODIS 250m EVI data .................................................. 8 2.2.2. Giant panda distribution data.............................................................. 9 2.2.3. Ancillary data ..................................................................................... 9 2.3. Reconstruction of cleaned MODIS EVI time series.............................. 10 2.4. Land cover classification....................................................................... 11 2.4.1. Land cover categories....................................................................... 12 2.4.2. Reference data extraction.................................................................. 13 2.4.3. Principal component transformation of EVI time series................... 13 2.4.4. Classification procedure ................................................................... 14 2.4.5. Accuracy assessment ........................................................................ 16 2.5. Quantifing the spatial configuration of the forests ................................ 16 2.5.1. Landscape metrics computation........................................................ 16 2.5.2. Landscape metrics extraction............................................................ 18 2.5.3. Metric reduction analysis.................................................................. 18 2.6. Linking the forest spatial configuration to the giant panda ................... 19 2.6.1. Extracting presence-absence data for the giant panda ...................... 19 2.6.2. Significance testing........................................................................... 21 2.6.3. Mapping the presence-absence of giant pandas................................ 21 3. Results .................................................................................................. 24 3.1. Land cover classification......................................................................
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