Next Article in Journal
An Improved Endmember Selection Method Based on Vector Length for MODIS Reflectance Channels
Previous Article in Journal
Improving Remote Sensing of Aerosol Optical Depth over Land by Polarimetric Measurements at 1640 nm: Airborne Test in North China
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(5), 6257-6279;

Remote Sensing Based Spatial Statistics to Document Tropical Rainforest Transition Pathways

Center for Sustainability, Saint Louis University, St. Louis, MO 63108, USA
College of Resources and Environmental Sciences, Chongqing University, 174 Shazhengjie, Shapingba, Chongqing 400044, China
College of Management, Xinjiang Agricultural University, Urumqi 830052, China
Madagascar Fauna and Flora Group, Kalinka, Lochearnhead FK19 8NZ, UK
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
Full-Text   |   PDF [15279 KB, uploaded 21 May 2015]   |  


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

Figure 1

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).

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top