Geospatial assessment of long-term changes in carbon stocks and fluxes in forests of India (1930–2013)

Reddy, C. Sudhakar; Rakesh, F.; Jha, Chandra Shekhar; Athira, K.; Singh, Sonali; Alekhya, V.V.L. Padma; Rajashekar, G.; Diwakar, P. G.; Dadhwal, V. K.

doi:10.1016/j.gloplacha.2016.05.011

Cited by 24 publications

(16 citation statements)

References 26 publications

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“…The study on long-term monitoring of forests has provided the distribution, changes in forest cover and the rate of deforestation in India during 1930-2013 (Reddy et al 2016a). The study has estimated the spatial distribution of phytomass carbon density from satellite remote sensing data, historical archives and collateral data from 1930 to 2013 (Reddy et al 2016b). The Indian Forest Act (1927), failed to address many issues related to conservation of forests.…”

Section: Introductionmentioning

confidence: 99%

Predictive modelling of the spatial pattern of past and future forest cover changes in India

et al. 2017

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This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km 2 , which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880-2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multilayer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km 2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.

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Section: Introductionmentioning

confidence: 99%

Predictive modelling of the spatial pattern of past and future forest cover changes in India

et al. 2017

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“…Given the extent of the tropical forests, inaccessibility and limited ground measurements, remote sensing has long been seen as a way to increase effectiveness in assessing tropical forest AGB at regional level. Combining forest structure parameters from stand level estimates with extensive forest cover maps derived from remote sensing have been shown to have the potential for providing reasonable biomass estimates at local, regional and global level [13,14]. However, from stand level AGB estimates to wall-to-wall extrapolation using different satellite data, the assessment of carbon stocks or AGB for tropical forests is still spoilt by uncertainty [15] thus weakening the results at regional scales.…”

Section: Introductionmentioning

confidence: 99%

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

et al. 2017

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Large scale assessment of aboveground biomass (AGB) in tropical forests is often limited by the saturation of remote sensing signals at high AGB values. Fourier Transform Textural Ordination (FOTO) performs well in quantifying canopy texture from very high-resolution (VHR) imagery, from which stand structure parameters can be retrieved with no saturation effect for AGB values up to 650 Mg·ha −1 . The method is robust when tested on wet evergreen forests but is more demanding when applied across different forest types characterized by varying structures and allometries. The present study focuses on a gradient of forest types ranging from dry deciduous to wet evergreen forests in the Western Ghats (WG) of India, where we applied FOTO to Cartosat-1a images with 2.5 m resolution. Based on 21 1-ha ground control forest plots, we calibrated independent texture-AGB models for the dry and wet zone forests in the area, as delineated from the distribution of NDVI values computed from LISS-4 multispectral images. This stratification largely improved the relationship between texture-derived and field-derived AGB estimates, which exhibited a R 2 of 0.82 for a mean rRMSE of ca. 17%. By inverting the texture-AGB models, we finally mapped AGB predictions at 1.6-ha resolution over a heterogeneous landscape of ca. 1500 km 2 in the WG, with a mean relative per-pixel propagated error <20% for wet zone forests, i.e., below the recommended IPCC criteria for Monitoring, Reporting and Verification (MRV) methods. The method proved to perform well in predicting high-resolution AGB values over heterogeneous tropical landscape encompassing diversified forest types, and thus presents a promising option for affordable regional monitoring systems of greenhouse gas (GhG) emissions related to forest degradation.

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“…Alternatively, what might be the socio-environmental consequences of the government trying to force a particular priority or goal at the cost of others? (Reddy et al 2016). 4 The reasons for this divergence may be several.…”

Section: Forests and Carbon Sequestrationmentioning

confidence: 99%

“…First, differences in definition of forest is one reason. Official estimates include all tree cover (including monocultural plantations in forest lands as well as horticultural crops in private lands), which results in a rising 'forest cover' trend, while only natural tree cover shows a declining trend (Reddy et al 2016). Second, the official estimates include the amount of sequestration due to growth in forests that remained forests (termed FL-FL) and addition in carbon due to conversion of non-forest to forest (termed L-FL); but they appear not to include the carbon emissions from forest to non-forest transitions (termed FL-L), which are non-zero (Dubash et al 2018).…”

Section: Forests and Carbon Sequestrationmentioning

confidence: 99%

Climate Change and India’s Forests

Lélé¹,

Krishnaswamy²

2019

India in a Warming World

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Forests influence, and are impacted by, climate in multiple ways. India has pledged to sequester 2.5–3 gigatonne of carbon dioxide equivalent in its forests by 2030. As per official estimates, India already has a positive carbon sequestration rate, suggesting that little extra effort is required to achieve this target, but these estimates are debated. Prioritizing carbon sequestration can also adversely affect local livelihoods, biodiversity, and hydrological regulation functions. Moreover, the mechanisms through which the state hopes to achieve this target do not take into consideration the history of conflict and ongoing shifts in forest governance. The influence of forests on regional climate, especially rainfall, further complicates this debate. Climate change will, in turn, influence India’s forests in complex and unclear ways: a possible expansion of the moist broadleaf forests in central India and a shrinking of temperate broadleaf and alpine forests; a possible increase in productivity; and decline in some wildlife species in specific habitats.

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Geospatial assessment of long-term changes in carbon stocks and fluxes in forests of India (1930–2013)

Cited by 24 publications

References 26 publications

Predictive modelling of the spatial pattern of past and future forest cover changes in India

Predictive modelling of the spatial pattern of past and future forest cover changes in India

Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery

Climate Change and India’s Forests

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