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ISPRS Int. J. Geo-Inf. 2016, 5(7), 117; doi:10.3390/ijgi5070117

Exploring the Relationship between Remotely-Sensed Spectral Variables and Attributes of Tropical Forest Vegetation under the Influence of Local Forest Institutions

1
Ashoka Trust for Research in Ecology and the Environment, Royal Enclave, Srirampura, Jakkur, Bangalore 560064, India
2
Manipal University, Manipal, Udupi 576 104, India
3
Fondazione Edmund Mach, Research and Innovation Centre, Department Biodiversity and Molecular Ecology, 38010 S. Michele All’adige (Trento), Italy
4
Azim Premji University, PES Institute of Technology Campus, Pixel Park, B Block, Electronics City, Beside Nice Road, Hosur Road, Bengaluru 560 100, India
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 25 April 2016 / Revised: 3 July 2016 / Accepted: 11 July 2016 / Published: 14 July 2016
(This article belongs to the Special Issue Spatial Ecology)
View Full-Text   |   Download PDF [3208 KB, uploaded 18 July 2016]   |  

Abstract

Conservation of forests outside protected areas is essential for maintaining forest connectivity, which largely depends on the effectiveness of local institutions. In this study, we use Landsat data to explore the relationship between vegetation structure and forest management institutions, in order to assess the efficacy of local institutions in management of forests outside protected areas. These forests form part of an important tiger corridor in Eastern Maharashtra, India. We assessed forest condition using 450 randomly placed 10 m radius circular plots in forest patches of villages with and without local institutions, to understand the impact of these institutions on forest vegetation. Tree density and species richness were significantly different between villages with and without local forest institutions, but there was no difference in tree biomass. We also found a significant difference in the relationship between tree density and NDVI between villages with and without local forest institutions. However, the relationship between species richness and NDVI did not differ significantly. The methods proposed by this study evaluate the status of forest management in a forest corridor using remotely sensed data and could be effectively used to identify the extent of vegetation health and management status. View Full-Text
Keywords: biodiversity; quantile regression; remote sensing; tree biodiversity biodiversity; quantile regression; remote sensing; tree biodiversity
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Agarwal, S.; Rocchini, D.; Marathe, A.; Nagendra, H. Exploring the Relationship between Remotely-Sensed Spectral Variables and Attributes of Tropical Forest Vegetation under the Influence of Local Forest Institutions. ISPRS Int. J. Geo-Inf. 2016, 5, 117.

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