International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-5/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W3, 121–128, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W3-121-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W3, 121–128, 2019
https://doi.org/10.5194/isprs-archives-XLII-5-W3-121-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Dec 2019

05 Dec 2019

SPATIAL BIODIVERSITY MODEL TO CHARACTERIZE BIOLOGICAL DIVERSITY USING R STATISTICAL COMPUTING ENVIRONMENT FOR NEPAL HIMALAYA

R. Silwal1,2, A. Roy2, H. Karnatak2, and R. B. Thapa1 R. Silwal et al.
  • 1International Centre for Integrated Mountain Development, Khumaltar, Lalitpur, 44700, Nepal
  • 2Indian Institute of Remote Sensing, 4 Kalidas Road, Dehradun, 248001, Uttarakhand, India

Keywords: Multi-criteria Decision Analysis, Spatial Biodiversity Model, Biodiversity Characterization

Abstract. Biodiversity characters of the landscape provide basis of prioritizing the sites in conservation effort. There is an urgent need for rapid assessment of existing biodiversity using state-of-art tools and technologies at large scale. The purpose of the study is to model and prioritize biological richness based on multi-criteria decision analysis (MCDA) for conservation priority and management planning. Vegetation type map for year 2017 was developed for generation of various landscape indices e.g. fragmentation, patchiness, porosity, juxtaposition etc. The Spatial Biodiversity Model (SBM) prepared for similar landscape of Uttarakhanda, India which is scale, resolution and location independent for spatial biodiversity richness modelling was executed in R programming platform. Satellite data, non-spatial data and ancillary data were used to generate Biological Richness (BR) map which is categorized into 4 classes as low, moderate, high and very high (biodiversity rich) including non-forest area to quantify BR area. The result shows that largest area is under very high biological richness class followed by high, moderate and low BR area. Overall accuracy and Kappa Statistics of LULC/vegetation type classification is 82.61% and 0.8013 respectively. The spatial regression analysis for final output validation has been made with ground based species diversity data where R2 value for Shannon-Wiener index and Margalef’s diversity index are 0.64 and 0.56 respectively. The results also re-emphasize the role of geospatial techniques in the quick appraisal of predicting biological richness. The study result is applicable in systematic inventory of biological resources, land use planning, conservation prioritization and policy support.