Assessment Of Aboveground Biomass Research Articles

Above‐ground biomass retrieval with multi‐source data: Prediction and applicability analysis in Eastern Mongolia

AbstractGrassland aboveground biomass (AGB) is a key variable to measure grassland productivity, and accurate assessment of AGB is important for optimizing grassland resource management and understanding carbon, water, and energy fluxes. Current approaches on large scales such as the Mongolian Steppe Ecosystem often combine field measurements with optical and/or synthetic aperture radar (SAR) data. Meanwhile, especially the representativeness of the field measurements for large‐scale analysis have seldom been accounted for. Therefore, we provide the first remotely sensed AGB product for central and Eastern Mongolia which (1) uses random forest (RF), (2) is fully validated against over 600 field samples, and (3) applies a novel method, dissimilarity index (DI), to derive the area of applicability of the model with respect to the training data. Therefore, different remote sensing data sources such as multi‐scale and multi‐temporal optical images—Worldview 2 (WV2), Sentinel 2 (S2), and Landsat 8 (L8) in combination with SAR data are tested for their suitability to provide an area‐wide estimation on large scale. The results showed that the AGB prediction by combining Sentinel 1 (S1) and S2 using RF had the highest accuracy. Furthermore, the model was applicable to at least 72.61% of the steppe area. Areas where the model was not applicable are mostly distributed along the edges of grassland. This study demonstrates the potential of combining Sentinel‐derived indices and machine learning to provide a reliable AGB prediction for grassland for extremely large ecosystems with strong climatic gradients.

Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data

Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data

Assessment of aboveground biomass and carbon accumulation in natural cenoses on the terraces of the northern slope of Khoysko‐the Makazhoy (Cheberloy) basin

Carbon exists as carbon dioxide in the atmosphere and makes up about 0.04% of the atmosphere. In the recent past, it has attracted a lot of attention as a greenhouse gas because it can influence the climate model of the world. Anthropogenic activities such as industrialization, deforestation, forest degradation and the burning of fossil fuels have caused an increase in atmospheric carbon levels and disrupted the global carbon cycle. The Climate Change Expert Group has identified five carbon pools of the terrestrial ecosystem that include biomass, namely aboveground biomass, underground biomass, debris, woody debris and soil organic matter. Among all carbon pools, aboveground biomass constitutes the major part of the carbon pool.

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

Forests, commonly viewed as the Earth’s lungs, play a crucial role in mitigating greenhouse gas emissions, regulating the globe, and maintaining ecological equilibrium. The assessment of aboveground biomass (AGB) serves as a pivotal indicator for evaluating forest quality. By integrating remote sensing images with a small number of ground-measured samples to map, forest AGBs can significantly reduce time and labor costs. Current research mainly focuses on improving the accuracy of mapping forest AGBs, such as integrating multiple-sensors remote sensing data and models. However, due to uncertainties associated with remote sensing images and complexities inherent in forest structures, the accuracy of mapping forest AGBs is constrained by both the quantity and distribution of ground samples available. The development of transfer learning methods can fully utilize ground-based measurement data and enable the application of samples across regions and time. To evaluate the potential of transfer learning methods in mapping forest AGBs, this study conducted a spatial–temporal transfer of spectral variables (SVs) and prediction models (PMs) using a direct-push transfer method, and a new evaluation metric, relative change of R-squared (RCRS), was proposed to assess the transferability of SVs and PMs. The results showed that the transferability of SVs and PMs in the spatial target domain is obviously greater than that in the temporal target domain. Compared to the temporal target domain, the RCRS for transfer SVs in the spatial target domain was lower by 20.89 (oak) and 20.88 (Chinese fir) and for transfer PMs by 24.16 (oak) and 24.79 (Chinese fir). Tree species is also one of the main factors affecting the spatial and temporal transfer of SVs, and it is challenging to transfer SVs between different tree species. The results also show that nonparametric models have better generalization performance, and their transferability is much greater than that of parametric models.

Examining the Potential of Sentinel Imagery and Ensemble Algorithms for Estimating Aboveground Biomass in a Tropical Dry Forest

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models’ field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m2. Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R2 value of 0.78 and an RMSE value of 42.25 Mg/ha−1. In contrast, the XGBoost model performed less accurately, obtaining an R2 value of only 0.60 and an RMSE value of 48.41 Mg/ha−1. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment.

Species-Specific Allometric Equations, Biomass Expansion Factor, and Wood Density of Native Tree Species in the Dry Afromontane Forest of Ethiopia

A forest is a storehouse of carbon released from different sources when the activities of sustainable forest management, planting, and rehabilitation exist. However, few allometric equations are present to determine its contribution to carbon reduction. The target of the study was to develop species-specific allometric equations and establish a database for biomass expansion factor and wood density for five tree species grown in the dry Afromontane forest of Ethiopia. A direct or destructive sampling method was used on 62 trees from different diameter classes. The diameter at breast height and the total height of selected trees ranged from 7 to 48 cm and 6.7 to 23.4 m, respectively. Trees were felled and divided into various biomass sections. Stem and big branch discs were sampled to determine the wood density and volume of the trees. Sample wood and foliage were oven-dried for three days and two days at 105°C and 70°C, respectively, to get their dry weight. Total above-ground biomass was regressed using diameter at breast height, total height, wood density, and average crown diameter as independent variables. R software version 4.0.1 was used to fit the biomass equations. The best biomass models were determined to have lower AIC and RSE and highest adj. R2. The biomass expansion factor and wood density of five tree species ranged from 1.19 to 1.40 and 0.53 to 0.74 g/cm−3, respectively. Species-specific allometric equations were better than both mixed species and pan tropical models for the assessment of above-ground biomass in the Chilimo dry Afromontane forest of Ethiopia.

Assessment of above ground biomass and soil organic carbon in the forests of Nepal under climate change scenario

IntroductionMany factors, such as climate, topography, forest management, or tree/forest attributes, influence soil organic carbon (SOC) and above-ground tree biomass (AGTB). This study focuses on assessing relationship between various predictor variables and response variables (SOC and AGTB) in the perspective of climate change scenario. The study was conducted throughout in Nepal using forest resource assessment data (2010–2014).MethodsOur study applied a random forest model to assess the status of SOC and AGTB under future climate change scenarios using 19 bioclimatic variables accompanied by other variables such as altitude, aspect, basal area, crown cover development status, distance to settlement forest types, number of trees, macro-topography, management regime, physiographic zones, slope, and soil depth. The study used 737 (70%) samples as a training data for model development while 312 (30%) samples as a testing data for model validation.Results and discussionThe respective RMSE, RMSE% and adjusted R2 of the Random Forest Model for SOC estimation were found to be 9.53 ton/ha, 15% and 0.746 while same for the AGTB were 37.55 ton/ha, 21.74% and 0.743. Particularly, changes in temperature and precipitation showed an effect on the amount of SOC and AGTB in the projected scenario i.e., CMIP6, SSP2 4.5 for 2040–2060. The study found the amount of SOC decreased by 3.85%, while AGTB increased by 2.96% in the projected scenario. The proposed approach which incorporates the effect of bioclimatic variables can be a better option for understanding the dynamics of SOC and AGTB in the future using climatic variables.

Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province

The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with >80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (norbitsp) required to characterize the OMGA to within p = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (ndaysp) required to characterize the AGB over each site was derived by multiplying the site norbitsp values with a scalar coefficient of 13.03 days. The scalar coefficient was found by counting the temporal intervals between successive GEDI orbits containing good quality forest AGB data and is equivalent to the average number of days required to obtain a GEDI orbit containing good quality forest AGB data at 50 × 50 km scale. Among the 15 sites, observation periods ranging from 65 to 221 days (0.18 – 0.61 years), 143 – 534 days (0.39 – 1.46 years), and 390 – 742 days (1.07 – 2.03 years) were required to characterize the AGB to within ±20%, ±10%, and ±5% of the site OMGA, respectively. The Intergovernmental Panel on Climate Change (IPCC) recommended accuracy requirement for forest AGB estimates is 10%. Thus, to meet this accuracy requirement the findings of this study indicate that at least 534 days (1.46 years) would be required for REDD+ site monitoring using GEDI in Mai Ndombe province. In other central African tropical forest localities these observations periods may be different depending on the forest AGB and spatial variation, cloud cover, ephemeral surface water presence, and GEDI AGB retrieval sensitivity to the forest conditions.

Modeling and Mapping of Aboveground Biomass and Carbon Stock Using Sentinel-2 Imagery in Chure Region, Nepal

The concerns about climate change in recent decades have heightened the need for effective methods for assessing and reporting forest biomass and Carbon Stocks (CS) at local, national, continental, and global scales. Accurate assessment of Aboveground Biomass (AGB) is critical for the sustainable management of forests, especially in the Chure region, a fragile and young mountainous in the lesser Himalaya of Nepal. This paper presents the modeling and mapping approach and shows how medium-resolution Sentinel-2 multispectral instrument (MSI) data can be used instead of hyperspectral data in inaccessible areas of the Chure region. The data were collected and analyzed from 72 circular sample plots. 60% (43 random sample plots) were used to create the model, while the remaining 40% (29 plots) were used for model validation. This study involved calculating 12 different vegetation indices and correlating them with plot-level AGB. Five models, including linear, logarithmic, quadratic, power, and exponential, were created, but the best model was found to be the quadratic model using normalized difference vegetation indices (NDVIs) with an R2 value of 0.777 and a correlation coefficient of 0.881. The model’s AIC and BIC values were 313.60 and 320.65, respectively. The validity of the model was performed using observed and predicted AGB values, resulting in an r value of 0.9128, an R2 value of 0.8332, and an RMSE value of 10.7657 t·h−1. Finally, the developed regression equation was used to map AGB in the study area. The AGB per pixel ranges from 0 to 129.18 t·h−1, whereas the amount of CS ranges from 0 to 61.01 t·h−1. Among the different vegetation indices used in the study, NDVI was found to be more precise in estimating and mapping biomass and carbon stocks in this study. Therefore, the study recommends using the quadratic model of NDVI for accurate estimation of AGB and CS in the Chure region of Sainamaina municipality.

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Improved estimation of aboveground biomass in rubber plantations by fusing spectral and textural information from UAV-based RGB imagery

Aboveground biomass (AGB), as a crucial indicator of forest growth and quality, plays an important role in monitoring the global carbon cycle and forest health. Rapid, accurate, and non-destructive assessment of AGB in rubber plantations is beneficial not only for predicting rubber yield but also for understanding the carbon storage potential in tropical areas. Previous studies have employed spectral information and texture features derived from unmanned aerial vehicle data to estimate the AGB of mangroves. However, few studies systematically assessed the effects of grey level co-occurrence matrix parameters for extracting texture features on AGB estimation in rubber plantations. Whether the combination of spectral information and texture features with suitable grey level co-occurrence matrix parameters selection derived from a low-cost unmanned aerial vehicle system can improve the AGB estimation accuracy remains unclear. To this end, this study evaluated the performance of spectral information and texture features derived from UAV-based high-resolution RGB imagery with different textural parameter settings. Three types of machine learning algorithms (support vector regression; random forest; extreme gradient boosting regressor) and stepwise multiple linear regression were used to compare and analyze their performance for AGB estimation of rubber plantations. The results indicated that appropriate textural parameter selection significantly improved the AGB estimation accuracy when using texture features alone. Among four regression techniques, stepwise multiple linear regression exhibited poor performance, while support vector regression performed the best. The best estimation accuracy (R2 = 0.752, RMSE = 28.72 t/ha) was obtained by support vector regression when using the combination of spectral information and texture features with the textural parameters of the orientation of 135°, displacement of 2 pixels, and moving window size parameter of 7 × 7 pixels. The findings suggested that the AGB estimation accuracy can be further improved by the integration of spectral information and texture features when considering appropriate textural parameters.

Influences of vegetation, model, and data parameters on forest aboveground biomass assessment using an area-based approach

Although aboveground biomass (AGB) estimation using area-based approaches (ABAs) and its application to forestry have been actively researched through three decades, this technology has been little operationalized in the Central European forest sector. That means specific recommendations are needed in order to apply ABA for forest biomass modelling in this region. The present study was directed to filling such gaps while examining the effect of input ABA parameters on AGB model quality in conditions of mixed mountainous forests in Central Europe. Specific objectives were to assess whether the strength of the AGB model can be impacted by 1) canopy conditions (leaf-on and leaf-off), 2) airborne LiDAR point density (2.5, 5.0, 7.5, 10.0 points/m2), 3) field methods to estimate AGB (with regeneration components or without), and 4) machine learning methods (AdaBoost, Random decision forest, multilayer neural network, and Bayesian ridge regression). The results show that canopy conditions and airborne LiDAR point densities did not affect the strength of the AGB model, but that model's strength was affected by the vegetation regeneration component in the field biomass reference and by the machine learning method tested for modelling. AdaBoost and random decision forest were the most successful methods. To evaluate the quality of an AGB model it is recommended to combine several individual evaluation functions into the model score. The study highlights several recommendations to follow when estimating AGB from ALS using an ABA in Central European forests.

Semi-Empirical Models and Revision of Predicting Approaches of Tree Aboveground Biomass Assessments

Tree aboveground biomass (e.g., bole, branches, and foliage), M, plays key roles in forest management as it is the basis for evaluating the sink and flux of, for example, carbon and nitrogen, stand productivity, dendro-energy, litter & root biomass, hydrological parameters, among others. With the aim of further simplifying and understanding M, the central objective of this research was to review available techniques to develop, test, and validate two independent novel non-destructive, semi-empirical models using four major M datasets: (i) the shape dimensional bio-physical, MSD; and (ii) the restrictive mathematical, MNR, models. The proposed models advance and test how each of both approaches: (i) constant or (ii) variable scalar coefficients perform when predicting M with major assumptions bearing bio-physical principles. Results showed that M has to be predicted eventually with variable scalar coefficients; both models predicted compatible M figures; the evaluations matched the conventional equation well; and the independent data sets were well validated; the coefficients of determination, r2, and the standard errors, Sx%, had values >96% and <20%, respectively, for most tested conifer tree species. In spite of demonstrating empirically and physically the ontogenetic-dependency of scalar coefficients, the MNR model, with constant β-scalar and variable a-intercept coefficients, performed slightly better, and precision appeared to be a function of the tree species growing in different forest ecosystems. Therefore, better parameterization advances for the testing and validation of the MSD model that uses variable scalar coefficients, which are consistent with ontogenetic principles, are preliminarily recommended for M assessments. The updated revision of models, the independent development, the construction using different assumptions, the individual mathematical and bio-physical parameterization, the consistency on M assessments, and the bearing of physical and biological properties are key pieces of scientific information presented in this report are required in modern forest management when predicting M and associated variables and attributes.

Joint estimation of aboveground biomass using “Space-Air-Ground” data in the Qilian Mountains, China

Accurate monitoring of aboveground biomass (AGB) and its spatial distribution is essential for sustainable management and development. The most frequently used method for estimating AGB with remotely sensed data is based on the relationship between AGB measured data and vegetation indices. In the past, there were some shortcomings in analyzing the spatial heterogeneity and solving scale difference. Therefore, this paper realized the space-air-ground integrated based on Sentinel-2 Multispectral Instrument (MSI), Unmanned Aerial Vehicle (UAV) and measured data. The results indicated that the RFR models have excellent adaptability to invert AGB of three different coverage regions in study area, with R2 of 0.69 and root mean square error (RMSE) of 116.22 kg·hm−2 for low-coverage region, R2 of 0.65 and RMSE of 164.16 kg·hm−2 for medium-coverage region, R2 of 0.73 and RMSE of 149.99 kg·hm−2 for high-coverage region. We found that the main driving vegetation indices was different in each region during the process of model building and it was related to the characteristics of vegetation indices. Moreover, regions with different coverage have different responses to topography. The findings showed that the spatial distribution and driving mechanism of AGB in the study area could be more clearly analyzed based on the coverage division. The introduction of coverage division reasonably enhanced the accuracy of RFR modeling AGB. This study confirmed that using the Sentinel-2 MSI, UAV images, measured data and the RFR model to realize the space-air-ground integrated is an applicable method for monitoring and assessment of AGB in Qilian Mountains, which can offer scientific basis and theoretical support to rational development of land resources and maintaining the balance of ecosystem.

Aboveground biomass of typical invasive mangroves and its distribution patterns using UAV-LiDAR data in a subtropical estuary: Maoling River estuary, Guangxi, China

Quantitative assessment of aboveground biomass (AGB) and spatial distribution pattern of exotic mangrove plants (Sonneratia apetala) is of great significance for blue carbon management and ecological restoration in typical subtropical estuaries in China. Although unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) has certain advantages in the investigation of the vertical three-dimensional structure of mangroves, the existing mangrove investigation results are mainly based on plot investigation method. Few scholars use machine learning (ML) method to estimate AGB of invasive Sonneratia apetala by combining plot investigation and LiDAR data. Therefore, on the basis of the height and intensity variables of UAV-LiDAR data, this study used four different ML algorithms, namely, xgboost regressor (XGBR), catboost regressor (CBR), light gradient boosting regressor (LGBR) and AdaBoost regressor (ABR), to estimate AGB of invasive mangrove. Then, the quantitative relationship between invasive mangrove biomass and hydrological unit was analysed. We found that CBR model had the highest accuracy in estimation of mangrove AGB (R2 = 0.7644, RMSE = 11.1725 Mg/ha), followed by XGBR model (R2 = 0.6759, 13.1053 Mg/ha). However, LGBR model (R2 = 0.3506, RMSE = 18.5510 Mg/ha) had poor fitting effect. The AGB of invasive mangroves showed a spatial distribution pattern of high in northwest and low in southeast, and its value ranged from 7.31 Mg/ha to 114.04 Mg/ha, with an average of 25.57 Mg/ha. The AGB of invasive mangroves was independent of the area size of the hydrological response unit but depended on the elevation of the beach surface and the distance from the main tidal ditch. This study demonstrates the feasibility of UAV-LiDAR remote sensing and CBR model in estimating AGB of invasive mangrove species, which can provide scientific basis and technical support for the assessment of invasive mangrove ecosystem and the protection of local mangrove tree species.

Spatial analysis of southeastern forest reserves in Nigeria using open geospatial data

ABSTRACT Tropical forest reserve system safeguards essential terrestrial ecosystems services such as atmospheric carbon sinks and biodiversity storehouses. The aim of this study was to quantify the spatial coverage of Southeastern forest reserves in Nigeria toward strengthening Nigeria’s capacity on monitoring, reporting, and verification mechanism of the Reducing Emission from Deforestation and forest Degradation (REDD+) Framework. Hence, three (3) open-source geospatial datasets were systematically analyzed. The result indicates that, about five (5) percent of the total landmass in Southeastern Nigeria is currently designated as forest reserves. The forty-seven (47) forests reserves identified in the study area account for a total of 1,335.42 km2 out of the entire 28,987 km2 landmass of the Southeast geopolitical zone. In conclusion, the current value of forestland under conservation within Southeastern Nigeria is 82% short of attaining the Food and Agriculture Organization’s prescribed 25% forest cover threshold by a margin of 5,911 km2. Therefore, for Nigeria to adequately achieve the FAO’s recommended target, every state government in the country should consistently maintain the exact 25% forest cover FAO’s threshold. As a next research line, assessment of aboveground biomass (AGB) within Nigeria’s Southeastern tropical forest reserves with open-source geospatial data, is hereby, recommended.

Acacia dealbata Link. Aboveground Biomass Assessment: Sustainability of Control and Eradication Actions to Reduce Rural Fires Risk

Invasive species are an environmental problem affecting worldwide ecosystems. In the case of Acacia dealbata Link., the negative impacts affect the productivity of the forests due to the competition established with native species while contributing to a significant increment in the available fuel load, increasing the risk of fire. In Portugal, chemical and mechanical methods are mostly used in the control of these species. However, the costs are often unsustainable in the medium term, being abandoned before completing the tasks, allowing the recovery of the invasive species. The establishment of value chains for the biomass resulting from these actions was pointed out by several authors as a solution for the sustainability of the control process, as it contributes to reducing costs. However, the problems in quantifying the biomass availability make it challenging to organize and optimize these actions. This work, which started from a dendrometrical analysis carried out in stands of A. dealbata, created a model to assess woody biomass availability. The model proved to be statistically significant for stands with trees younger than 20 years old. However, the amount of data collected and the configuration of the settlements analyzed do not allow extrapolation of the model presented to older settlements.

Assessment of Above-Ground Biomass in Pakistan Forest Ecosystem’s Carbon Pool: A Review

Climate change is acknowledged as a global threat to the environment and human well-being. Forest ecosystems are a significant factor in this regard as they act both as a sink and a source of carbon. Forest carbon evaluation has received more attention after the Paris Agreement. Pakistan has 5.1% forest cover of its total land area, which comprises nine forest types. This study covers the studies conducted on above-ground biomass and carbon stock in various forest types of Pakistan. Most of the studies on biomass and carbon stock estimation have been conducted during 2015–2020. The non-destructive method is mostly followed for carbon stock estimation, followed by remote sensing. The destructive method is used only for developing allometric equations and biomass expansion factors. The information available on the carbon stock and biomass of Pakistan forest types is fragmented and sporadic. Coniferous forests are more important in carbon sequestration and can play a vital role in mitigating climate change. Pakistan is a signatory of the Kyoto Protocol and still lacks regional and national level studies on biomass and carbon stock, which are necessary for reporting under the Kyoto Protocol. This study will help researchers and decision-makers to develop policies regarding Reducing Emissions from Deforestation and forest Degradation (REDD+), conservation, sustainable forest management and enhancement of forest carbon stocks

Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

Assessment of above-ground biomass and carbon loss from a tropical dry forest in Mexico

Primary forests in seasonally dry tropical regions have undergone intense land-use/cover change, ranging from widespread shifting agriculture to land clearing for livestock production systems, and selective logging. Despite the importance of tropical dry forests (TDF), little is known about the implications of carbon (C) emissions from deforestation in local, national, and global scales. Therefore, the main objective of this study is to quantify and understand the processes that drive major C losses of this ecosystem in Mexico. Also, we evaluated the applicability of the already published above ground biomass (AGB) maps to quantify and allocate changes in C stocks. The results suggest that biomass maps can be used to capture the patterns of AGB distribution and to identify the driving forces of C emissions. The C losses are more related to socioeconomic drivers than biophysical characteristics like topography and climate. Besides, this study shows that published current AGB maps may be used for landscape management, including conservation and restoration areas.