Status and Opportunities for Forest Resources Management Using Geospatial Technologies in Northeast India

2020 ◽  
pp. 206-224
Author(s):  
Kasturi Chakraborty ◽  
Thota Sivasankar ◽  
Junaid Mushtaq Lone ◽  
K. K. Sarma ◽  
P. L. N. Raju
Keyword(s):  
Remote Sensing ◽  
Forest Degradation ◽  
Resource Assessment ◽  
Economic Benefits ◽  
Remote Sensing And Gis ◽  
Forest Resource ◽  
Forest Density ◽  
North East ◽  
Endemic Medicinal Plant ◽  
Forest Resources Management

The forest resource of North East Region (NER) of India is a store house of several unique, endangered, endemic, medicinal plant, bamboo, etc. species in diverse forest type and high forest density. Several authors and organizations have contributed to the study of the richness and diversity distributed in different forest types and forest density. This chapter attempts to highlight the uniqueness of the forest of NER and the role of geospatial technology and presents various interesting studies pertaining to the region as an input to forest resource assessment. Remote sensing and GIS have an important role in NER forest resource assessment, management, and conservation. Various studies carried out with the help of remote sensing and GIS technology have highlighted the ongoing forest degradation and deforestation taking place in this region due to developmental activity and economic benefits. There is continuous improvement in the forest estimates from coarse resolution satellite data to unmanned aerial vehicles (UAV) in the recent times.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

Satellite remote sensing of forest resources: three decades of research development

2005 ◽  
Vol 29 (1) ◽  
pp. 1-26 ◽  
Author(s):  
D. S. Boyd ◽  
F. M. Danson
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Forest Type ◽  
Resource Assessment ◽  
Biochemical Properties ◽  
Forest Resources ◽  
Forest Resource ◽  
Comprehensive Review ◽  
Satellite Sensor

Three decades have passed since the launch of the first international satellite sensor programme designed for monitoring Earth’s resources. Over this period, forest resources have come under increasing pressure, thus their management and use should be underpinned by information on their properties at a number of levels. This paper provides a comprehensive review of how satellite remote sensing has been used in forest resource assessment since the launch of the first Earth resources satellite sensor (ERTS) in 1972. The use of remote sensing in forest resource assessment provides three levels of information; namely (1) the spatial extent of forest cover, which can be used to assess the spatial dynamics of forest cover; (2) forest type and (3) biophysical and biochemical properties of forests. The assessment of forest information over time enables the comprehensive monitoring of forest resources. This paper provides a comprehensive review of how satellite remote sensing has been used to date and, building on these experiences, the future potential of satellite remote sensing of forest resources is highlighted.

scholarly journals A comparative study to assess surface water's anthropic and natural vulnerability using the DKPR and RUSLE approaches. Case of the watershed of the Joumouaa dam (Coastal basin of Ghiss, North East Morocco)

2021 ◽  
Vol 298 ◽  
pp. 05005
Author(s):  
Soukaina Mansour ◽  
Taoufiq Kouz ◽  
Abdeldjalil Belkendil ◽  
Hinde Cherkaoui Dekkaki
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Comparative Study ◽  
Remote Sensing And Gis ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
North East ◽  
Resource Managers ◽  
Water Vulnerability ◽  
Gis Environment

The salinization of surface water in a coastal context leads to a qualitative degradation of this resource by various sources of anthropogenic and natural pollution. In this context, we present the results of a comparative study using "DKPR" and "RUSLE" models to evaluate the degree of surface water vulnerability against pollution, especially in the sub-watershed of the Joumouaa dam, a hydraulic infrastructure providing drinking water for the Targuist city. The "DKPR" model adopted as a qualitative approach involves four parameters: Accessibility of the aquatic environment (D), Water functioning of the soil and subsoil (K), Physiography watershed (P), Rainfall erosivity (R). The final result is a resource vulnerability map obtained by combining index maps of these four parameters using remote sensing and GIS. The "RUSLE" model applied as a quantitative approach integrates five factors in a multiplying function, namely: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and soil conservation practices (P) in a remote sensing and GIS environment. The analysis of the final vulnerability maps of the approaches mentioned above will be helpful support for water resource managers and decision-makers better identify areas of high risk and their protection.

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