Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data,and a decision tree algorithm |
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| Institution: | 1. Departamento de Procesos y Tecnología, Universidad Autónoma Metropolitana Unidad Cuajimalpa, Mexico;2. Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA;3. NASA Astrobiology Institute, USA;4. Laboratorio de Pruebas Biológicas, Instituto de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, México City, Mexico;5. BGR Bundesansaltfür Geowissenschaften und Rohstoffe, Stilleweg 2, D-30655 Hannover, Germany;6. Direccion de Investigación y Posgrado, Instituto Mexicano del Petróleo, Mexico;7. Laboratorio de Resonancia Paramagnética Electrónica, Instituto de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, México City, Mexico |
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| Abstract: | In this paper, we propose a decision tree algorithm to characterize spatial extent and spectral features of invasive plant species (i.e., guava, Madagascar cardamom, and Molucca raspberry) in tropical rainforests by integrating datasets from passive and active remote sensing sensors. The decision tree algorithm is based on a number of input variables including matching score and infeasibility images from Mixture Tuned Matched Filtering (MTMF), land-cover maps, tree height information derived from high resolution stereo imagery, polarimetric feature images, Radar Forest Degradation Index (RFDI), polarimetric and InSAR coherence and phase difference images. Spatial distributions of the study organisms are mapped using pixel-based Winner-Takes-All (WTA) algorithm, object oriented feature extraction, spectral unmixing, and compared with the newly developed decision tree approach. Our results show that the InSAR phase difference and PolInSAR HH–VV coherence images of L-band PALSAR data are the most important variables following the MTMF outputs in mapping subcanopy invasive plant species in tropical rainforest. We also show that the three types of invasive plants alone occupy about 17.6% of the Betampona Nature Reserve (BNR) while mixed forest, shrubland and grassland areas are summed to 11.9% of the reserve. This work presents the first systematic attempt to evaluate forest degradation, habitat quality and invasive plant statistics in the BNR, and provides significant insights as to management strategies for the control of invasive plants and conversation in the reserve. |
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| Keywords: | Betampona Nature Reserve Guava Madagascar cardamom Molucca raspberry Invasive plants Remote sensing |
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