PMID- 28988063
OWN - NLM
STAT- MEDLINE
DCOM- 20181029
LR  - 20181029
IS  - 1095-8630 (Electronic)
IS  - 0301-4797 (Linking)
VI  - 206
DP  - 2018 Jan 15
TI  - Spatial landscape model to characterize biological diversity using R statistical 
      computing environment.
PG  - 1211-1223
LID - S0301-4797(17)30924-6 [pii]
LID - 10.1016/j.jenvman.2017.09.055 [doi]
AB  - Due to urbanization and population growth, the degradation of natural forests and 
      associated biodiversity are now widely recognized as a global environmental 
      concern. Hence, there is an urgent need for rapid assessment and monitoring of 
      biodiversity on priority using state-of-art tools and technologies. The main 
      purpose of this research article is to develop and implement a new methodological 
      approach to characterize biological diversity using spatial model developed 
      during the study viz. Spatial Biodiversity Model (SBM). The developed model is 
      scale, resolution and location independent solution for spatial biodiversity 
      richness modelling. The platform-independent computation model is based on 
      parallel computation. The biodiversity model based on open-source software has 
      been implemented on R statistical computing platform. It provides information on 
      high disturbance and high biological richness areas through different landscape 
      indices and site specific information (e.g. forest fragmentation (FR), 
      disturbance index (DI) etc.). The model has been developed based on the case 
      study of Indian landscape; however it can be implemented in any part of the 
      world. As a case study, SBM has been tested for Uttarakhand state in India. 
      Inputs for landscape ecology are derived through multi-criteria decision making 
      (MCDM) techniques in an interactive command line environment. MCDM with 
      sensitivity analysis in spatial domain has been carried out to illustrate the 
      model stability and robustness. Furthermore, spatial regression analysis has been 
      made for the validation of the output.
CI  - Copyright (c) 2017 Elsevier Ltd. All rights reserved.
FAU - Singh, Hariom
AU  - Singh H
AD  - Geomatics Engineering Group, Civil Engineering Department, Indian Institute of 
      Technology Roorkee, Roorkee, India.
FAU - Garg, R D
AU  - Garg RD
AD  - Geomatics Engineering Group, Civil Engineering Department, Indian Institute of 
      Technology Roorkee, Roorkee, India.
FAU - Karnatak, Harish C
AU  - Karnatak HC
AD  - Indian Institute of Remote Sensing, ISRO, Dehradun, India. Electronic address: 
      harish@iirs.gov.in.
FAU - Roy, Arijit
AU  - Roy A
AD  - Indian Institute of Remote Sensing, ISRO, Dehradun, India.
LA  - eng
PT  - Journal Article
DEP - 20171004
PL  - England
TA  - J Environ Manage
JT  - Journal of environmental management
JID - 0401664
SB  - IM
MH  - *Biodiversity
MH  - *Conservation of Natural Resources
MH  - Forests
MH  - India
MH  - Mathematical Computing
OTO - NOTNLM
OT  - Biodiversity
OT  - Biological richness
OT  - Disturbance index map
OT  - MCDM
OT  - Parallel computation
OT  - SBM
EDAT- 2017/10/11 06:00
MHDA- 2018/10/30 06:00
CRDT- 2017/10/09 06:00
PHST- 2017/04/25 00:00 [received]
PHST- 2017/09/18 00:00 [revised]
PHST- 2017/09/19 00:00 [accepted]
PHST- 2017/10/11 06:00 [pubmed]
PHST- 2018/10/30 06:00 [medline]
PHST- 2017/10/09 06:00 [entrez]
AID - S0301-4797(17)30924-6 [pii]
AID - 10.1016/j.jenvman.2017.09.055 [doi]
PST - ppublish
SO  - J Environ Manage. 2018 Jan 15;206:1211-1223. doi: 10.1016/j.jenvman.2017.09.055. 
      Epub 2017 Oct 4.