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

Agarwal, S.; Rocchini, D.; Marathe, A.; Nagendra, H. (2016). Exploring the relationship between remotely-sensed spectral variables and attributes of tropical forest vegetation under the influence of local forest institutions. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 5 (7): 117. doi: 10.3390/ijgi5070117 handle: http://hdl.handle.net/10449/34340

Exploring the relationship between remotely-sensed spectral variables and attributes of tropical forest vegetation under the influence of local forest institutions

Rocchini, Duccio;
2016-01-01

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
Biodiversity
Quantile regression
Remote sensing
Tree biodiversity
Settore BIO/07 - ECOLOGIA
2016
Agarwal, S.; Rocchini, D.; Marathe, A.; Nagendra, H. (2016). Exploring the relationship between remotely-sensed spectral variables and attributes of tropical forest vegetation under the influence of local forest institutions. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 5 (7): 117. doi: 10.3390/ijgi5070117 handle: http://hdl.handle.net/10449/34340
File in questo prodotto:
File Dimensione Formato  
IJGI_2016.pdf

accesso aperto

Licenza: Creative commons
Dimensione 3.21 MB
Formato Adobe PDF
3.21 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/34340
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 5
social impact