Assessing spatio-temporal health of forest cover using forest canopy density model and forest fragmentation approach in Sundarban reserve forest, India

We used forest canopy density model for examining spatial–temporal variation in canopy closure in Sundarban Forest in India and validated the health with fragmentation model. Statistics derived through forest canopy model revealed that most of the changes in forest canopy density occurred in 60–80 % class during 1990–2011. Areas having >80 % and 40–60 % canopy density registered decrease in density while the remained classes 20–40 % and <20 % gained the proportion of decreased density from upper density classes. Forest fragmentation model classified the forested areas into four categories of disturbance-core, perforated, edge and patch based on 200 m edge width. Fragmentation model revealed that the perforated and edge areas have decreased while patch area has increased. Overall core area has increased due to decline in perforated area and consequently experienced decrease in canopy closure. The study demonstrated usefulness of forest canopy density and fragmentation models for assessing the health of the forests.

[1]  J. Townshend,et al.  Global discrimination of land cover types from metrics derived from AVHRR pathfinder data , 1995 .

[2]  A. Rikimaru,et al.  Development of Forest Canopy Density Mapping and Monitoring Model using Indices of Vegetation, Bare soil and Shadow , 1997 .

[3]  Teresa Pinto-Correia,et al.  A remote sensing-based approach to estimating montado canopy density using the FCD model: a contribution to identifying HNV farmlands in southern Portugal , 2014, Agroforestry Systems.

[4]  S. H. Raza,et al.  Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia , 2010, Environmental monitoring and assessment.

[5]  R. Noss,et al.  Conservation Biology and Forest Certification: Working Together toward Ecological Sustainability , 2001, Journal of Forestry.

[6]  Forest canopy density stratification using biophysical modeling , 2003 .

[7]  A. Mukhopadhyay,et al.  A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique , 2014, Journal of Coastal Conservation.

[8]  Zhongwu Wang,et al.  AN INTEGRATED METHOD FOR FOREST CANOPY COVER MAPPING USING LANDSAT ETM+ IMAGERY , 2009 .

[9]  K. P. Sharma,et al.  Stratification of density in dry deciduous forest using satellite remote sensing digital data—An approach based on spectral indices , 1996, Journal of Biosciences.

[10]  F. S. Gilbert,et al.  The Fragmented Forest: Island Biogeography and the Preservation of Biotic Diversity. , 1985 .

[11]  P. Roy,et al.  Tropical forest cover density mapping , 2002 .

[12]  S. Nugroho Method for Detecting of Forest Degradation Method Using Landsat Satellite Images To Support MRV REDD in Halimun Salak National Park , 2012 .

[13]  B. Gopal,et al.  Biodiversity and its conservation in the Sundarban Mangrove Ecosystem , 2006, Aquatic Sciences.

[14]  Chengquan Huang,et al.  Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA , 2009 .

[15]  F. Blasco,et al.  The mangroves of India. , 1980 .

[16]  Y. Haila Maintaining Biodiversity in Forest Ecosystems: Islands and fragments , 1999 .

[17]  Peter Vogt,et al.  Mapping Spatial Patterns with Morphological Image Processing , 2007, Landscape Ecology.

[18]  A. Paul,et al.  APPLICATION OF SUPERVISED ENHANCEMENT TECHNIQUE IN MONITORING THE MANGROVE FOREST COVER DYNAMICS - A CASE STUDY ON AJMALMARI RESERVE FOREST, SUNDARBAN, , 2013 .

[19]  L. Chapungu,et al.  A multi-method analysis of forest fragmentation and loss: The case of ward 11, Chiredzi District of Zimbabwe , 2014 .

[20]  Dazhong Wen Land Mosaics: The Ecology of Landscapes and Regions , 1997 .

[21]  Mohd Hasmadi Ismail,et al.  Remote sensing for mapping RAMSAR heritage site at Sungai Pulai Mangrove Forest Reserve, Johor, Malaysia. , 2011 .

[22]  P. Roy,et al.  Forest Canopy Density Stratification: How Relevant is Biophysical Spectral Response Modelling Approach? , 2005 .

[23]  Z. azizia,et al.  FOREST CANOPY DENSITY ESTIMATING , USING SATELLITE IMAGES , 2008 .

[24]  Md. Jinnahtul Islam,et al.  Remote sensing for change detection in the Sunderbands, Bangladesh , 1997 .

[25]  S. García-Gigorro,et al.  Forest Fragmentation Estimated from Remotely Sensed Data: Is Comparison Across Scales Possible? , 2005, Forest Science.

[26]  Tsuyoshi Kajisa,et al.  Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches , 2012 .

[27]  Om Prakash Tripathi,et al.  Implementation of Forest Canopy Density Model to Monitor Tropical Deforestation , 2013, Journal of the Indian Society of Remote Sensing.

[28]  T. Kajisa,et al.  Monitoring Deforestation and Forest Degradation in the Bago Mountain Area, Myanmar using FCD Mapper , 2010 .

[29]  R. Hobbs,et al.  Biological Consequences of Ecosystem Fragmentation: A Review , 1991 .