scholarly journals Vegetation cover and carbon pool loss assessment due to extreme weather induced disaster in Mandakini valley, Western Himalaya

2020 ◽  
Vol 21 (1&2) ◽  
pp. 49-62
Author(s):  
Yogesh Kumar ◽  
Sanjay Babu ◽  
Sarnam Singh
Keyword(s):  
Forest Cover ◽  
Western Himalaya ◽  
Flash Floods ◽  
Extreme Weather ◽  
Carbon Pool ◽  
Coefficient Of Determination ◽  
Above Ground Biomass ◽  
Loss Assessment ◽  
Subtropical Forests ◽  
Ground Biomass

Sendai Framework for 2015-2030 emphasises on the damage and loss assessment needs and its ecosystem level impacts. We have assessed the loss of forest cover and phytomass/carbon pool in the natural forest ecosystems lost due to extreme weather conditions leading flash floods and landslides during Kedarnath tragedy on June 17, 2013 in Mandakini Valley, Uttarakhand in Western Himalaya. We used high resolution satellite IRS LISS IV (5.8 m spatial resolution) of pre-disaster (2012) & post-disaster (2013). Since lost vegetation cannot be ground inventoried, a new approach was developed wherein we used pre-disaster spectral characteristics to identify  sample locations in nearby and adjacent to affected areas. We laid 45 geotagged sample plots in May 2014 on both side of the 37 landslide affected areas within a distance of 2 km from river-bed for primary data collection. Above ground biomass and Carbon was estimated using standard protocols and used species-specific volumetric equations and wood density. Above ground biomass varied from 18.05t/ha in Alpine Scrub to 252.95 t/ha in Subtropical forests. Assuming that the biomass increment and spectral properties would not change significantly, we applied several vegetation indices to get best regression model with biomass.  We found NDVI (2014) with coefficient of determination (R2) of 0.893, SE± 0.038 with linear function as the best for geospatial modelling of the biomass for pre-flood 2013 and post-flood 2014 situations. Coefficient of determination (R²) between estimated vis-à-vis modelled biomass was 0.8643. It is found that there is a net loss of 52,055.80 tonnes of forest biomass and 24,466.14 tonnes of carbon due to landslides and flash floods. The maximum biomass/carbon was lost in the sub-tropical forests. The loss of forest cover was maximum in subtropical forests.

scholarly journals Allometric equations to estimate carbon pool in soil and above-ground biomass in mangrove forests in Southeast Mexico

2013 ◽  
Author(s):  
J. J. Guerra-Santos ◽  
R. M. Cerón ◽  
J. G. Cerón ◽  
A. Alderete-Chávez ◽  
D. L. Damián-Hernández ◽  
...  
Keyword(s):  
Carbon Pool ◽  
Allometric Equations ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Southeast Mexico

scholarly journals Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

2020 ◽  
Vol 12 (20) ◽  
pp. 3351
Author(s):  
Sawaid Abbas ◽  
Man Sing Wong ◽  
Jin Wu ◽  
Naeem Shahzad ◽  
Syed Muhammad Irteza
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Carbon Sequestration ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Above Ground Biomass ◽  
Forest Cover Change ◽  
Ground Biomass

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

Climate and large-sized trees, but not diversity, drive above-ground biomass in subtropical forests

2021 ◽  
Vol 490 ◽  
pp. 119126
Author(s):  
Kauane Maiara Bordin ◽  
Adriane Esquivel-Muelbert ◽  
Rodrigo Scarton Bergamin ◽  
Joice Klipel ◽  
Rayana Caroline Picolotto ◽  
...  
Keyword(s):  
Above Ground Biomass ◽  
Subtropical Forests ◽  
Ground Biomass

scholarly journals Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Evian Pui Yan Chan ◽  
Tung Fung ◽  
Frankie Kwan Kit Wong
Keyword(s):  
Hong Kong ◽  
Point Cloud ◽  
Carbon Sink ◽  
Subtropical Forest ◽  
Airborne Lidar ◽  
Above Ground Biomass ◽  
Forest Protection ◽  
Subtropical Forests ◽  
Plot Size ◽  
Ground Biomass

AbstractSeventy-percent of the terrestrial area of Hong Kong is covered by vegetation and 40% is protected as the Country Park. The above-ground biomass (AGB) acts as reliable source of carbon sink and while Hong Kong has recognized the importance of carbon sink in forest and urged for forest protection in the latest strategic plan, yet no study has been conducted on assessing the baseline of terrestrial AGB and its carbon storage. This study compared and estimated the AGB by the traditional allometric modeling and the Light Detection and Ranging (LiDAR) plot metrics at plot-level in a subtropical forest of Hong Kong. The study has tested five allometric models which were developed from pantropical regions, subtropical areas and locally. The best model was then selected as the dependent variable to develop the LiDAR-derived AGB model. The raw LiDAR point cloud was pre-processed to normalized height point cloud and hence generating the LiDAR metric as independent variables for the model development. Regression models were used to estimate AGB at various plot sizes (i.e., in 10-m, 5-m and 2.5-m radius). The models were then evaluated statistically and validated by bootstrapping and leave-one-out cross validation (LOOCV). The results indicated the LiDAR metric derived from larger plot size outperformed the smaller plot size, with model R2 of 0.864 and root-mean-square-error (RMSE) of 37.75 kg/ha. It also found that pantropical model was comparable to a site-specific model when including the bioclimatic variable in subtropical forests. This study provides the approach for delineating the baseline of terrestrial above-ground biomass and carbon stock in subtropical forests upon an appropriate plot size is being deployed.

scholarly journals Use Basal Diameter to Establish Mixed Species Allometric Equations Predicting Woody Stand Biomass in the Sudano-guinea Savannahs of Ngaoundere, Cameroon

2020 ◽  
pp. 1-12
Author(s):  
Mamadou Laminou Mal Amadou ◽  
Halilou Ahmadou ◽  
Ahmadou Ibrahim ◽  
Tchindebe Alexandre ◽  
Massai Tchima Jacob ◽  
...  
Keyword(s):  
Tree Height ◽  
Allometric Equation ◽  
Information Criteria ◽  
Allometric Equations ◽  
Coefficient Of Determination ◽  
Leaf Biomass ◽  
Allometric Relationships ◽  
Above Ground Biomass ◽  
Basal Diameter ◽  
Ground Biomass

Little information on allometric relationships for estimating stand biomass in the savannah of Cameroon was available. Allometric relationships for estimating stand biomass were investigated in the sudano-guinea savannah of Ngaoundere, Cameroon. A total of 90 individual woody from sixteen (16) contrasting plant species belonging shrubs and trees were harvested in Dang savannah across a range of diameter classes, from 3 to 35 cm. Basal diameter (D), total height (H) and tree density were determined and considered as predictor variables, while total above-ground biomass, stem, branch and leaf biomass were the output variables of the allometric models. Among many models tested, the best ones were chosen according to the coefficient of determination adjusted (R2adj), the residual standard error (RSE) and the Akaike Information Criteria. The main results showed that the integration of tree height and density with basal diameter improved in the degree of fitness of the allometric equations. The fit allometric stand biomass model for leaf, branch, stem and above ground biomass were the following forms: Ln(LB) = -5.08 + 2.75*Ln(D) – 0.30*Ln(D2Hρ); Ln(BB) = -7.81 + 1.29*Ln(D2H) – 0.39*Ln(ρ); Ln(SB) = -5.08 + 2.40*Ln(D) +0.50*Ln(H) and Ln(TB) = -5.07 + 3.21*Ln(D) – 0.12*Ln(D2Hρ) respectively. It is concluded that the use of tree height and density in the allometric equation can be improved for these species, as far as the present study area is concerned. Therefore, for estimating the biomass of shrubs and small trees, the use of basal diameter as an independent variable in the allometric equation with a power equation would be recommended in the Sudano-guinea savannahs of Ngaoundere, Cameroon. The paper describes details of shrub biomass allometry, which is important in carbon stock and savannah management for the environmental protection.

Soil properties and neighbouring forest cover affect above-ground biomass and functional composition during tropical forest restoration

2018 ◽  
Vol 21 (2) ◽  
pp. 179-189 ◽  
Author(s):  
Renato Miazaki Toledo ◽  
Rozely Ferreira Santos ◽  
Lander Baeten ◽  
Michael P. Perring ◽  
Kris Verheyen
Keyword(s):  
Tropical Forest ◽  
Soil Properties ◽  
Forest Restoration ◽  
Forest Cover ◽  
Functional Composition ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Tropical Forest Restoration

scholarly journals The Driving Factors of Subtropical Mature Forest Productivity: Stand Structure Matters

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 998
Author(s):  
Siyuan Ren ◽  
Qingsong Yang ◽  
Heming Liu ◽  
Guochun Shen ◽  
Zemei Zheng ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Stand Structure ◽  
Forest Productivity ◽  
Subtropical Forest ◽  
Tree Size ◽  
Above Ground Biomass ◽  
Niche Complementarity ◽  
Subtropical Forests ◽  
Ground Biomass ◽  
Size Diversity

Forest productivity (increment of above-ground biomass) is determined by biodiversity but also by stand structure attributes. However, the relative strengths of these drivers in determining productivity remain controversial in subtropical forests. In this study, we analyzed a tree growth data from 500 plots with in a 20 ha mature subtropical forest in eastern China. We used spatial simultaneous autoregressive error models to examine the effects of diversity variables (species richness, evenness, and composition), stand structural attributes (stand density, tree size range and diversity), environmental factors (topography and soil), and initial above-ground biomass (AGB) on productivity. We also applied structural equation models to quantify the relative importance of diversity, stand structure, environmental factors, and initial AGB in determining forest productivity. Our results showed that stand structure together with diversity and initial AGB governed forest productivity. Tree size diversity (DBH Shannon’s diversity index) had the largest positive effect on forest productivity. These results provide new evidence that structural explanatory variables have greater contributions to productivity for mature subtropical forests, strongly supporting the niche complementarity hypothesis. Our work highlights the importance of tree size diversity in promoting high forest productivity, and suggests that regulating and conserving complexity of forest stand structure should be among the most important goals in subtropical forest management.

scholarly journals Estimating Mangrove Above-Ground Biomass Using Extreme Gradient Boosting Decision Trees Algorithm with Fused Sentinel-2 and ALOS-2 PALSAR-2 Data in Can Gio Biosphere Reserve, Vietnam

2020 ◽  
Vol 12 (5) ◽  
pp. 777 ◽  
Author(s):  
Tien Dat Pham ◽  
Nga Nhu Le ◽  
Nam Thang Ha ◽  
Luong Viet Nguyen ◽  
Junshi Xia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
Biosphere Reserve ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

This study investigates the effectiveness of gradient boosting decision trees techniques in estimating mangrove above-ground biomass (AGB) at the Can Gio biosphere reserve (Vietnam). For this purpose, we employed a novel gradient-boosting regression technique called the extreme gradient boosting regression (XGBR) algorithm implemented and verified a mangrove AGB model using data from a field survey of 121 sampling plots conducted during the dry season. The dataset fuses the data of the Sentinel-2 multispectral instrument (MSI) and the dual polarimetric (HH, HV) data of ALOS-2 PALSAR-2. The performance standards of the proposed model (root-mean-square error (RMSE) and coefficient of determination (R2)) were compared with those of other machine learning techniques, namely gradient boosting regression (GBR), support vector regression (SVR), Gaussian process regression (GPR), and random forests regression (RFR). The XGBR model obtained a promising result with R2 = 0.805, RMSE = 28.13 Mg ha−1, and the model yielded the highest predictive performance among the five machine learning models. In the XGBR model, the estimated mangrove AGB ranged from 11 to 293 Mg ha−1 (average = 106.93 Mg ha−1). This work demonstrates that XGBR with the combined Sentinel-2 and ALOS-2 PALSAR-2 data can accurately estimate the mangrove AGB in the Can Gio biosphere reserve. The general applicability of the XGBR model combined with multiple sourced optical and SAR data should be further tested and compared in a large-scale study of forest AGBs in different geographical and climatic ecosystems.

scholarly journals ESTIMATION AND MAPPING OF ABOVE GROUND BIOMASS OF MANGROVE FOREST USING ALOS-2 PALSAR-2 IN BENOA BAY, BALI, INDONESIA

2021 ◽  
Vol 15 (1) ◽  
pp. 75
Author(s):  
IGA Indah Mahasani ◽  
Takahiro Osawa ◽  
I Wayan Sandi Adnyana
Keyword(s):  
Aboveground Biomass ◽  
Policy Development ◽  
Carbon Stock ◽  
Carbon Fixation ◽  
Coefficient Of Determination ◽  
Mangrove Forests ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Bay Area ◽  
Mangrove Ecosystems

Mangrove forests are distributed in limited areas around along costlines, but they play important role in carbon fixation and carbon storafe in the tropic areas. Mangrove forests are a transitional ecosystem between land-based oceans, most of which are well-known along the tropic and subtropical coastlines. Mangrove ecosystems have an ecological function as an absorber and storage of carbon in the form of biomass. Remote sensing technology can include data spatially and temporally. This makes it easy to predict the overall extent and carbon stock. So that in the context of sustainable management of mangrove ecosystems it can be utilized to monitor mangrove carbon balance and become the basis for policy development. The objective of this study was to determine the potential above ground biomass model from ALOS-2 PALSAR-2 data in mangrove forests of Benoa Bay, Bali. In this research, the filter used is frost filtering. AGB model was constructesd by using dual-polarization L-band SAR of ALOS-2 PALSAR-2 data and field inventory plots. 40 plots were collected in the field and the allometric equation. The prediction model for aboveground biomass potential based on the ALOS-2 PALSAR-2 image on HV polarization in the mangrove Benoa Bay area, the correlation value (r) of 0.82, the coefficient of determination (R2) of 0.68. Validation model aboveground biomass-based, correlation value (r) of 0.90, the coefficient of determination (R2) of 0.82, and RMSE of ± 39.85. The potential of aboveground biomass and carbon stock in the mangrove Benoa Bay area is 364,241.87 Mg and 171,193.67 Mg C with the ability to absorb carbon dioxide (CO2) of 628,280.81 Mg CO2 Sequestration same with 3 bottles in 2020.Keywords: Mangrove; Aboveground biomass (AGB); HV Polarization; ALOS-2 PALSAR-2.

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