Quantification and monitoring of deforestation in India over eight decades (1930–2013)

Reddy, C. Sudhakar; Jha, Chandra Shekhar; Dadhwal, V. K.; Krishna, P. Hari; Pasha, S. Vazeed; Satish, K. V.; Dutta, Kalloli; Saranya, K. R. L.; Rakesh, F.; Rajashekar, G.; Diwakar, P. G.

doi:10.1007/s10531-015-1033-2

Cited by 153 publications

(36 citation statements)

References 29 publications

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“…BiLISA cluster and significant maps of spatial autocorrelation between deforestation and residential growth in 1972-1987 (a, b); 1987-2000 (c, d); and 2000-2010 (e, f) decreased. Generally, this part of our results confirms the finding of prior studies (Xu et al, 2000;Jat et al, 2008;DeFries et al, 2010;Geymen, 2013;Pir-Bavaghar, 2015;Reddy et al, 2016) that the population growth as one significant driver in converting forests into other human needs, like expansion of settlements and farmlands in developing countries. In the next section, we model the influence of residential growth rate on the deforestation rate in the area through some powerful spatial regression methods.…”

Section: Spatial Autocorrelationsupporting

confidence: 92%

“…The values of deforestation induced by farmland expansions obtained about 40,940 ha in second period and 15,820 ha in third period. Likewise, sprawling of farmlands was known as one of the main causes of deforestation and vegetation degradation in the prior studies (DeFries et al, 2010;Kelarestaghi & Jafarian Jeloudar, 2011;Davudirad et al, 2016;Reddy et al, 2016). Our analyses of occurred deforestation in different altitudes indicated that the majority of removed forests were in the lowland forests (<800 m) with 64·58% (80,958 ha), which are more affected by anthropogenic activities like farming and expanding settlements.…”

Section: Results Of Deforestation and Residential Area Growthmentioning

confidence: 68%

“…Deforestation and forest degradation are main factors of climate change aggravation such as CO 2 increase in the atmosphere, carbon stocks reduction (Phelps et al, 2010;Pearson et al, 2014), changes in floodplain sediment deposition rates (Keesstra, 2007), changes in hydrology and water quality (Lal, 1997), increase in soil erosion and flood (Boahene, 1998;de la Paix et al, 2013), desertification (Honey-Rosés, 2009) and occurrence of mass movements (Glade, 2003). The aforementioned concerns are increasing in developing countries because of drastic forest conversion to other land-use types, which is connected to demographic changes, socio-economic issues, forest conservation, forest management and environmental factors (Reddy et al, 2016). Hence, quantifying the effects of significant variables on deforestation and forest degradation leads to succeeding actions to reduce and deal with their adverse outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…The effects of spreading residential areas onto deforestation are particularly important (DeFries et al, 2010;Yousif et al, 2015). The high population growth rate in developing countries was introduced by some scholars as one of the most significant variables in raising the demands to expand settlements and farmlands through removing forests in tropical forests (DeFries et al, 2010;Reddy et al, 2016), north of Ethiopia (Belay et al, 2015) and Hyrcanian forests (Kelarestaghi & Jafarian Jeloudar, 2011;Pir-Bavaghar, 2015). Moreover, Iran experienced a rapid population growth during the past half century.…”

Section: Introductionmentioning

confidence: 99%

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Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

et al. 2017

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This study aimed to examine deforestation and residential growth trends and their spatial dependencies from 1972 to 2010 in Northeast of Iran. First, change rates of forests and residential areas were mapped using Landsat satellite images in 1972–1987, 1987–2000 and 2000–2010. Then, the forest change patterns were interpreted using univariate local Moran's I (local univariate spatial autocorrelation), and the spatial autocorrelation between deforestation and residential growth was tested through bivariate local Moran's I (bivariate local spatial autocorrelation). Furthermore, the spatial relationships between deforestation and residential growth rates were quantified by ordinary least squares, spatial lag (SL) and geographically weighted regression. Results indicated that approximately 25% of forests have been converted to other land‐use types in the span of 38 years, since 1972. Local univariate spatial autocorrelation maps showed that significant values of high–high cluster scattered in all locations in the first span, in the east and south aspects in the second duration, and in the eastern part in the third span. Bivariate local spatial autocorrelation indicated a meaningful Moran's I values of −0·12, −0·26 and −0·20 between deforestation and residential growth, chronologically. Analyses of spatial regression models showed that geographically weighted regression performed better than SL and ordinary least squares in the first (R2 = 0·315, AIC = 6,160) and third periods (R2 = 0·27, AIC = 6,351), whereas, the validity of SL was the highest in the second period (R2 = 0·36, AIC = 6,288). However, the overall trends of deforestation and residential growth have decreased, but the rate of deforestation induced by residential growth is still significant. Spatial exploration of residential growth in deforestation leads to determine its influences in local scale for better conservation of these valuable natural resources. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Spatial Autocorrelationsupporting

confidence: 92%

Section: Results Of Deforestation and Residential Area Growthmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

et al. 2017

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“…(Schneider, Pontius 2001) applied logistic regression to survey deforestation in Massachusetts, USA, and found that population growth in the area in recent years has led to the expansion of roads and residential areas and the expansion of deforestation in the state. (Sudhakar, et al 2016) used satellite imagery and the Land Change Modeler method to investigate the extent of deforestation in India between 1930 and 2013, and concluded that about 40% of India's forests during these years they are removed. The forests of northern Iran are of great value, but in recent years have been transformed into other uses for various reasons.…”

Section: Introductionmentioning

confidence: 99%

Investigating of Forest Change in Golestan Province Using Landsat Image

Zoraghi¹,

Saadi²,

Hasanlou³

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, forests in the north of the country have been attacked due to human interference. Increasing population and development of residential and agricultural areas have led to deforestation. Change detection is one of the most common methods for evaluating natural resources. The aim of this study is to monitor changes in forests of Golestan province in two period times from 1990 to 2019, using Landsat images. Accordingly, by incorporating those data sets land use maps are produced. Also, the SVM algorithm is used with six different classes including forest (F), urban area (U), agriculture (A), uncultivated land (UL), water (w) and bare soil (BS). The achieved overall accuracies are 85.48% and 89.86%. Then the map and matrix changes were obtained by post-classification comparison method. The results showed that the Golestan province's forests were reduced and converted to agricultural and urban land uses.

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Assessment of uncertainties in global land cover products for hydro‐climate modeling in India

Madhusoodhanan

Sreeja

Eldho

2017

Water Resources Research

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Earth's land cover (LC) has significant influence on land‐atmospheric processes and affects the climate at multiple scales. There are multiple global LC (GLC) data sets which are yet to be evaluated for uncertainties and their propagation into the simulation of land surface fluxes (LSFs) in land surface/climate modeling. The present study assesses the uncertainties in seven GLC products with reference to a regional data set for the simulation of LSFs in India using a macro‐scale land surface model. There is considerable overestimation of the extent of croplands in most of the GLCs. The uncertainties in LCs exert significant bias in the simulation of the LSFs of actual evapotranspiration (ETa), latent heat (LE), and sensible heat (H) fluxes. Uncertainty propagation in LSFs is proportional to the bias in cropping intensity under rainfed condition. The high underrepresentation of cropland area in the UMd data set results in highest bias in LSFs whereas the least cropland bias in Globland30 leads to least bias. Irrigation has higher potential to alter the LSFs than uncertainties related to LC especially in regions with large area under irrigation like India. The changes in LSFs are higher in arid/semiarid regions with medium irrigation intensity than in subhumid regions with high irrigation intensity. This has significant implications for the country's future irrigation expansion plans in the arid/semiarid regions. The study also emphasizes the need for focused efforts to quantify the uncertainties from varying irrigation intensities in the next generation CMIP6 experiments.

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Quantification and monitoring of deforestation in India over eight decades (1930–2013)

Cited by 153 publications

References 29 publications

Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

Analysing Spatial and Statistical Dependencies of Deforestation Affected by Residential Growth: Gorganrood Basin, Northeast Iran

Investigating of Forest Change in Golestan Province Using Landsat Image

Assessment of uncertainties in global land cover products for hydro‐climate modeling in India

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