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Remote Sens. 2015, 7(5), 6257-6279; doi:10.3390/rs70506257

Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways

1
Center for Sustainability, Saint Louis University, St. Louis, MO 63108, USA
2
College of Resources and Environmental Sciences, Chongqing University, 174 Shazhengjie, Shapingba, Chongqing 400044, China
3
College of Management, Xinjiang Agricultural University, Urumqi 830052, China
4
Madagascar Fauna and Flora Group, Kalinka, Lochearnhead FK19 8NZ, UK
5
Madagascar Fauna and Flora Group, 4065 Flora Place, St. Louis, MO 63110, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Randolph Wynne and Prasad S. Thenkabail
Received: 15 February 2015 / Accepted: 14 May 2015 / Published: 20 May 2015
View Full-Text   |   Download PDF [15279 KB, uploaded 21 May 2015]   |  

Abstract

In this paper, grid cell based spatial statistics were used to quantify the drivers of land-cover and land-use change (LCLUC) and habitat degradation in a tropical rainforest in Madagascar. First, a spectral database of various land-cover and land-use information was compiled using multi-year field campaign data and photointerpretation of satellite images. Next, residential areas were extracted from IKONOS-2 and GeoEye-1 images using object oriented feature extraction (OBIA). Then, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to generate land-cover and land-use maps from 1990 to 2011, and LCLUC maps were developed with decadal intervals and converted to 100 m vector grid cells. Finally, the causal associations between LCLUC were quantified using ordinary least square regression analysis and Moran’s I, and a forest disturbance index derived from the time series Landsat data were used to further confirm LCLUC drivers. The results showed that (1) local spatial statistical approaches were most effective at quantifying the drivers of LCLUC, and (2) the combined threats of habitat degradation in and around the reserve and increasing encroachment of invasive plant species lead to the expansion of shrubland and mixed forest within the former primary forest, which was echoed by the forest disturbance index derived from the Landsat data. View Full-Text
Keywords: tropical rainforest; Betampona Nature Reserve; land-cover/land-use change (LCLUC); remote sensing tropical rainforest; Betampona Nature Reserve; land-cover/land-use change (LCLUC); remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ghulam, A.; Ghulam, O.; Maimaitijiang, M.; Freeman, K.; Porton, I.; Maimaitiyiming, M. Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways. Remote Sens. 2015, 7, 6257-6279.

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