Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests

Rajashekar, G.; Fararoda, Rakesh; Reddy, R. Suraj; Jha, Chandra Shekhar; Ganeshaiah, K. N.; Singh, J. S.; Dadhwal, V. K.

doi:10.1016/j.ecolind.2017.11.024

Cited by 43 publications

(12 citation statements)

References 26 publications

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“…Furthermore, as most of the recent studies are modelling AGB, more evidence is needed on how well these methods predict GSV. Spatially explicit information on GSV is vital for strategic forest management and can be used to estimate AGB and to support the REDD+ (reducing emissions from deforestation and forest degradation, and fostering conservation, sustainable management of forests, and enhancement of forest carbon stocks) processes [26,27].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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Section: Introductionmentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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“…However, India is also recognized for its vast forest cover and therefore has a huge potential for carbon storage. India, together with Myanmar and Indonesia accounts for 70% of the carbon stocks of Asian forests (Brown et al 1993;FAO 2010;Rajashekar et al 2018…”

Section: Climate Change and Its Mitigation: Indian Scenariomentioning

confidence: 99%

Role of Major Forest Biomes in Climate Change Mitigation: An Eco-Biological Perspective

Dar

Kothandaraman

Bhat

et al. 2020

Environmental Science and Engineering

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The rapid alteration in the global climate due to anthropogenic activities has profound eco-biological impacts, which invariably affect the ability of natural communities to effectively perform ecosystem services. The eco-biological impacts could be viewed across various dimensions including loss of biodiversity as well as ecosystem goods and services, changes in phenology, prevalence of droughts and forest fires, disease outbreaks, reduced crop yields and increase in intensity and frequency of extreme weather events. Although, the natural ecosystems are innately endowed with the ability to maintain homeostasis by means of resistance and resilience, this ability to cope up is severely impacted by various other factors like deforestation, habitat fragmentation, land-use change and biological invasion, which exacerbate the effects of climate change. The eco-biological impacts of climate change are tied with socio-economic aspects by means of market values of the produce, poverty, undernourishment, livelihood security, etc. At this crucial juncture, forest biomes offer an immense ecosystem service towards climate change mitigation through carbon sequestration. Nevertheless, the three major forest biomes, viz. tropical, temperate and boreal, with their unique characteristics, vary in their response to climate change as well as mitigation potential and response. This review chapter aims to understand the varied climate change impacts and the crucial roles of major forest biomes in climate change mitigation and their various ecological services to formulate better forest management strategies.

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“…The combined effect of the two makes the development of the forestry industry have a certain spatial correlation. Rajashekar et al found that as an important part of the global carbon cycle, the spatial distribution of forest carbon had a significant impact on the carbon intensity of different regions 19 . Wang et al indicated that the spatial agglomeration of CO2 emissions in various provinces in China was gradually increasing 20 .…”

Section: Introductionmentioning

confidence: 99%

The effect of total factor productivity of forestry industry on CO2 emissions: a spatial econometric analysis of China

Zhong

Wang

2021

Sci Rep

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Forestry plays an essential role in reducing CO2 emissions and promoting green and sustainable development. This paper estimates the CO2 emissions of 30 provinces in China from 2008 to 2017, and uses Global DEA-Malmquist to measure the total factor productivity of the forestry industry and its decomposition index. On this basis, by constructing a spatial econometric model, this paper aims to empirically study the impact of forestry industry's total factor productivity and its decomposition index on CO2 emissions, and further analyze its direct, indirect and total effects. The study finds that the impact of forestry industry's total factor productivity on CO2 emissions shows an "inverted U-shaped" curve and the inflection point is 0.9395. The spatial spillover effect of CO2 emissions is significantly negative. The increase of CO2 emissions in adjacent areas will provide a "negative case" for the region, so that the region can better address its own energy conservation and emission reduction goals. TFP of forestry industry also has positive spatial spillover effect. However, considering the particularity of forestry industry, this effect is not very significant. For other factors, such as foreign direct investment, urbanization level, industrial structure and technology market turnover will also significantly affect regional CO2 emissions.

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Spatial distribution of forest biomass carbon (Above and below ground) in Indian forests

Cited by 43 publications

References 26 publications

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Role of Major Forest Biomes in Climate Change Mitigation: An Eco-Biological Perspective

The effect of total factor productivity of forestry industry on CO2 emissions: a spatial econometric analysis of China

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