Predicting Forest Cover and Density in Part of Porhat Forest Division, Jharkhand, India using Geospatial Technology and Markov Chain

ABSTRACT: The increasing population has posed a threat to the existence of the forests, which provide many services to us. Of late, they seem to be degraded, deforested and converted into other land use classes. In such situation, it becomes necessary to monitor and analyze the changes such that in future protection measures are enforced suitably. Geospatial technology, which is a combination of satellite remote sensing data, GIS and GPS offers better prospective in analyzing the changes in natural resources over various spatial scales and spectral resolutions. The present study aims to study both qualitatively and quantitatively, analyzing and predicting the changes in forest cover by generating forest cover classification map, area statistics, transition matrix in part of Saranda forest of West Singbhum district of the state of Jharkhand, India using remote sensing and GIS. The study evaluates the magnitude, rate and dynamics of change in the spatial extent of the forest between 1975 and 2015 using multi-temporal datasets (Landsat MSS 1975, ETM+ 1999 and OLI/TIRS 2015. The analysis revealed that the dense forests periodically are showing a decreasing trend which constitutes approximately 50%, 33% and 27% of the study area in 1975, 1999 and 2015 respectively. Finally using Markov chain analysis (MCA) forest cover area statistics was predicted for the year 2031. This analysis would help to have a holistic view of the future scenario of forests which would guide the policy makers and managers. Strict policy implementation to safeguard the forests against various anthropogenic pressures and community involvement is necessary to prevent further destruction of forests.

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Small-scale vegetation mapping of large territories: perspectives of using modern remote sensing and GIS technologies, the Indian experiences

Geobotanical mapping ◽

10.31111/geobotmap/1994-1995.51 ◽

1996 ◽

pp. 51-54 ◽

Cited By ~ 1

Author(s):

N. V. M. Unni

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Forest Cover ◽

Human Life ◽

Remote Sensing Data ◽

Modern World ◽

Small Scale ◽

Vegetation Map ◽

Gis Technologies ◽

Forest Cover Mapping

The recognition of versatile importance of vegetation for the human life resulted in the emergence of vegetation science and many its applications in the modern world. Hence a vegetation map should be versatile enough to provide the basis for these applications. Thus, a vegetation map should contain not only information on vegetation types and their derivatives but also the geospheric and climatic background. While the geospheric information could be obtained, mapped and generalized directly using satellite remote sensing, a computerized Geographic Information System can integrate it with meaningful vegetation information classes for large areas. Such aft approach was developed with respect to mapping forest vegetation in India at. 1 : 100 000 (1983) and is in progress now (forest cover mapping at 1 : 250 000). Several review works reporting the experimental and operational use of satellite remote sensing data in India were published in the last years (Unni, 1991, 1992, 1994).

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Imaging Spectroscopy for Conservation Applications

Remote Sensing ◽

10.3390/rs13020292 ◽

2021 ◽

Vol 13 (2) ◽

pp. 292

Author(s):

Megan Seeley ◽

Gregory P. Asner

Keyword(s):

Remote Sensing ◽

Decision Making ◽

Case Studies ◽

Spatial Scales ◽

Imaging Spectroscopy ◽

Remote Sensing Data ◽

Decision Makers ◽

High Spectral Resolution ◽

Conservation Areas ◽

Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

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Capacity Development for Use of Remote Sensing for REDD+ MRV Using Online and Offline Activities: Impacts and Lessons Learned

Remote Sensing ◽

10.3390/rs13112172 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2172

Author(s):

Sarah Carter ◽

Martin Herold ◽

Inge Jonckheere ◽

Andres Espejo ◽

Carly Green ◽

...

Keyword(s):

Remote Sensing ◽

Forest Cover ◽

Hybrid Approach ◽

Remote Sensing Data ◽

Lessons Learned ◽

Multiple Partners ◽

Large Audience ◽

Forest Cover Changes ◽

The Impact ◽

And Training

Four workshops and a webinar series were organized, with the aim of building capacity in countries to use Earth Observation Remote Sensing data to monitor forest cover changes and measure emissions reductions for REDD+ results-based payments. Webinars and workshops covered a variety of relevant tools and methods. The initiative was collaboratively organised by a number of Global Forest Observations Initiative (GFOI) partner institutions with funding from the World Bank’s Forest Carbon Partnership Facility (FCPF). The collaborative approach with multiple partners proved to be efficient and was able to reach a large audience, particularly in the case of the webinars. However, the impact in terms of use of tools and training of others after the events was higher for the workshops. In addition, engagement with experts was higher from workshop participants. In terms of efficiency, webinars are significantly cheaper to organize. A hybrid approach might be considered for future initiatives; and, this study of the effectiveness of both in-person and online capacity building can guide the development of future initiatives, something that is particularly pertinent in a COVID-19 era.

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Remote sensing and GIS forest landscape assessment for sustainable forest management

Journal of Agriculture Forestry and the Social Sciences ◽

10.4314/joafss.v17i1.2 ◽

2021 ◽

Vol 17 (1) ◽

pp. 12-26

Author(s):

A.F. Chukwuka ◽

A. Alo ◽

O.J. Aigbokhan

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Forest Cover ◽

Sustainable Forest Management ◽

Markov Chain Model ◽

Remote Sensing Data ◽

Chain Model ◽

Spatial Metrics ◽

Forest Reserve ◽

Land Cover Types

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environment

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Evaluating the environmental impact of payments for ecosystem services in Coatepec (Mexico) using remote sensing and on-site interviews

Environmental Conservation ◽

10.1017/s037689291100052x ◽

2011 ◽

Vol 38 (4) ◽

pp. 426-434 ◽

Cited By ~ 53

Author(s):

JASON SCULLION ◽

CRAIG W. THOMAS ◽

KRISTINA A. VOGT ◽

OCTAVIO PÉREZ-MAQUEO ◽

MILES G. LOGSDON

Keyword(s):

Remote Sensing ◽

Ecosystem Services ◽

Forest Cover ◽

Remote Sensing Data ◽

Environmental Benefits ◽

Payments For Ecosystem Services ◽

Programme Implementation ◽

Before And After ◽

Monitoring And Enforcement ◽

Landsat Satellite

SUMMARYOver the last decade, hundreds of payments for ecosystem services (PES) programmes have been initiated around the world, but evidence of their environmental benefits remains limited. In this study, two PES programmes operating in the municipality of Coatepec (Mexico) were evaluated to assess their effectiveness in protecting the region's endangered upland forests. Landsat satellite data were analysed to assess changes in forest cover before and after programme implementation using a difference-in-differences estimator. Additionally, surveys and interviews were conducted with local residents and a subset of PES programme participants to evaluate the programmes’ social and environmental impacts, particularly the effect of the programmes on landowner behaviour. The remote-sensing data show that deforestation was substantially lower on properties receiving PES payments compared to properties not enrolled in the programmes, but the programmes did not prevent the net loss of forests within Coatepec. Moreover, the on-site interviews suggest that the payments may have had little impact on deforestation rates, and that other factors contributed to the conservation of forests in PES properties. These findings suggest that risk-targeted payments, robust monitoring and enforcement programmes, and additional conservation initiatives should be included in all PES schemes to ensure environmental effectiveness.

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Forest areas remote monitoring within the limits of transport objects impact

Automobile Roads and Road Construction ◽

10.33744/0365-8171-2021-109-109-118 ◽

2021 ◽

pp. 109-118

Author(s):

Dmytro Liashenko ◽

◽

Dmytro Pavliuk ◽

Vadym Belenok ◽

Vitalii Babii ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Data ◽

Remote Monitoring ◽

Forest Cover ◽

Landsat Imagery ◽

Remote Sensing Data ◽

Transport Infrastructure ◽

Nature Management ◽

Collecting Methods ◽

Remote Sensing Methods

The article studies the issues of using remote sensing data for the tasks of ensuring sustainable nature management in the territories within the influence of transport infrastructure objects. Peculiarities of remote monitoring for tasks of transport networks design and in the process of their operation are determined. The paper analyzes the development of modern remote sensing methods (satellite imagery, the use of mobile sensors installed on cars or aircraft). A brief overview of spatial data collecting methods for the tasks of managing the development of territories within the influence of transport infrastructure (roads, railways, etc.) has made. The article considers the experience of using remote sensing technologies to monitor changes in the parameters of forest cover in the Transcarpathian region (Ukraine) in areas near to highways, by use Landsat imagery.

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A study on deforestation in hilly areas of Haryana using Remote Sensing and GIS technique

Environment Conservation Journal ◽

10.36953/ecj.2020.21308 ◽

2020 ◽

pp. 69-77

Author(s):

Anju Jangra ◽

Anurag Airon ◽

Ram Niwas

Keyword(s):

Remote Sensing ◽

Forest Cover ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Geographical Information ◽

Special Focus ◽

Landsat 8 ◽

Deforestation Rate ◽

Gis Technique ◽

Prime Concern

Forest is an essential part or backbone of the earth ecological system. In a country like India, the people and the economy of nation is mainly relies on the diversity of natural resources. In today's world degradation of forest resources is a prime concern for many of the scientists and environmentalists because the canvas had been transformed from last few decades to cultivated and non-cultivated land. In India, Haryana state has lowest forest cover i.e. 3.59% followed by Punjab 3.65%. Over the several decades, the advancement of Remote Sensing and Geographical Information System (GIS) technique has emerged as an efficient tool to monitor and analyse deforestation rate in hilly areaor over a variety of location. Remote sensing based vegetation indices show better sensitivity than individual band reflectance and hence are more preferred for assessment and monitoring of tress. The aim of the present study was to analyse the deforestation in hilly areas in Haryana State (India) by remote sensing data with a special focus on Panchkula and Yamunanagar. The information was collected through the LANDSAT 8 satellite of NASA. The result revealed that the deforestation rate is high in Hilly areas of Haryana. The study shows that the forest cover in hilly areas of Haryana in 2013 was 50,879.07 hectares and in 2019 it was 44,445.51 hectares of land. Thereby decrease in forest cover of 6,433.56 hectares had been observed in the study period of 2013-2019 i.e. 6 years. Spatial variations in deforestation were also mapped in GIS for the hilly areas in Panchkula and Yamunanagar districts of Haryana.

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Estimation of forest cover restoration damaged by Siberian silkmoth using remote sensing data and maps

E3S Web of Conferences ◽

10.1051/e3sconf/20197502005 ◽

2019 ◽

Vol 75 ◽

pp. 02005

Author(s):

Elena Fedotova

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Tree Species ◽

Forest Cover ◽

Remote Sensing Data ◽

Deciduous Forests ◽

Sensing Data ◽

Current State

The current state of the land cover has been estimated in the territories where in different years (1885, 1955, 1995) the forests were damaged by Siberian silkmoth. Dark-needle taiga is restored through the change of tree species. In 20 years in areas of dark-needle taiga there are graminoid communities, in 60 years we have deciduous forests there, and in 130 - dark needle forests, but not everywhere.

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Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Biogeosciences ◽

10.5194/bg-11-6827-2014 ◽

2014 ◽

Vol 11 (23) ◽

pp. 6827-6840 ◽

Cited By ~ 51

Author(s):

M. Réjou-Méchain ◽

H. C. Muller-Landau ◽

M. Detto ◽

S. C. Thomas ◽

T. Le Toan ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Spatial Scales ◽

Forest Biomass ◽

Remote Sensing Data ◽

Field Measurements ◽

Forest Degradation ◽

Forest Carbon ◽

Mitigation Strategies ◽

Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

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