Aboveground Biomass Map Research Articles

The Effect of Synergistic Approaches of Features and Ensemble Learning Algorith on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8.

Although the combination of Airborne Laser Scanning (ALS) data and optical imagery and machine learning algorithms were proved to improve the estimation of aboveground biomass (AGB), the synergistic approaches of different data and ensemble learning algorithms have not been fully investigated, especially for natural secondary forests (NSFs) with complex structures. This study aimed to explore the effects of the two factors on AGB estimation of NSFs based on ALS data and Landsat 8 imagery. The synergistic method of extracting novel features (i.e., COLI1 and COLI2) using optimal Landsat 8 features and the best-performing ALS feature (i.e., elevation mean) yielded higher accuracy of AGB estimation than either optical-only or ALS-only features. However, both of them failed to improve the accuracy compared to the simple combination of the untransformed features that generated them. The convolutional neural networks (CNN) model was much superior to other classic machine learning algorithms no matter of features. The stacked generalization (SG) algorithms, a kind of ensemble learning algorithms, greatly improved the accuracies compared to the corresponding base model, and the SG with the CNN meta-model performed best. This study provides technical support for a wall-to-wall AGB mapping of NSFs of northeastern China using efficient features and algorithms.

Spatial cross-validation is not the right way to evaluate map accuracy

For decades scientists have produced maps of biological, ecological and environmental variables. These studies commonly evaluate the map accuracy through cross-validation with the data used for calibrating the underlying mapping model. Recent studies, however, have argued that cross-validation statistics of most mapping studies are optimistically biased. They attribute these overoptimistic results to a supposed serious methodological flaw in standard cross-validation methods, namely that these methods ignore spatial autocorrelation in the data. They argue that spatial cross-validation should be used instead, and contend that standard cross-validation methods are inherently invalid in a geospatial context because of the autocorrelation present in most spatial data. Here we argue that these studies propagate a widespread misconception of statistical validation of maps. We explain that unbiased estimates of map accuracy indices can be obtained by probability sampling and design-based inference and illustrate this with a numerical experiment on large-scale above-ground biomass mapping. In our experiment, standard cross-validation (i.e., ignoring autocorrelation) led to smaller bias than spatial cross-validation. Standard cross-validation was deficient in case of a strongly clustered dataset that had large differences in sampling density, but less so than spatial cross-validation. We conclude that spatial cross-validation methods have no theoretical underpinning and should not be used for assessing map accuracy, while standard cross-validation is deficient in case of clustered data. Model-free, design-unbiased and valid accuracy assessment is achieved with probability sampling and design-based inference. It is valid without the need to explicitly incorporate or adjust for spatial autocorrelation and perfectly suited for the validation of large scale biological, ecological and environmental maps.

Signatures of Wetland Impact: Spatial Distribution of Forest Aboveground Biomass in Tumen River Basin

The Tumen River Basin, located in the cross-border region of China, North Korea, and Russia, constitutes an important ecological barrier in China. Forest here is mainly distributed around wetland, with the distribution of wetland having the potential to regulate regional forest carbon storage. However, the spatially explicit map of forest aboveground biomass (AGB) and potential impact of drivers, i.e., wetland distribution and climate, is still lacking. We thus use a deep neural network and multi-source remote sensing data to quantify forest AGB in the Tumen River Basin. Our results show the mean forest AGB is 103.43 Mg ha−1, with divergent spatial variation along its distance to wetland. The results of correlation analysis showed that with sufficient soil moisture supply, temperature dominant spatial variation of forest aboveground biomass. Noted that using the space for time substitution, we find when wetland decreased by less than 11.1%, the forest AGB decreased by more than 8%. Our result highlight the signatures of wetland impact on its nearby forest carbon storage, and urge the wetland protection, especially under the warming and drying future.

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

A COMPARISON OF DECISION TREE-BASED MODELS FOR FOREST ABOVE-GROUND BIOMASS ESTIMATION USING A COMBINATION OF AIRBORNE LIDAR AND LANDSAT DATA

Abstract. Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for forest monitoring purposes. Thus, the main objective of this paper was to investigate the performance of decision tree-based models to predict AGB at a site in Huntington Wild Forest (HWF) in Essex County, NY using continuous forest inventory (CFI) plots. The results of decision tree, random forest, and deep forest regression models were compared using light detection and ranging (LiDAR), Landsat 5 TM, and a combination of them. The results illustrated the importance of integration of Landsat 5 TM and LiDAR data, which benefits from both vertical forest structure and spectral information reflected by canopy cover. In addition, the deep forest model with a root mean square error (RMSE) of 51.63 Mg/ha and R-squared (R2) of 0.45 outperformed other regression tree-based models, regardless of the dataset.

Scaled biomass estimation in woodland ecosystems: Testing the individual and combined capacities of satellite multispectral and lidar data

Airborne laser scanning (ALS) data enable accurate modeling and mapping of aboveground biomass (AGB), but the limited spatial and temporal extents of ALS data collection limit the capacity for broad-scale carbon accounting. Conversely, while space-based remote sensing instruments provide increased spatial and temporal coverage, it can be difficult to directly link field-level vegetation biometrics to satellite data due to coarser spatial resolution and positional uncertainty. The combined use of ALS and satellite remote sensing data may offer a solution to efficient, accurate, and consistent AGB mapping across time and space. Such airborne-spaceborne data fusion has been demonstrated successfully in high-biomass settings; however, the unique structural conditions of dryland woodland ecosystems, with open canopies and low leaf area indices, pose mapping challenges that require further study. These challenges are particularly acute with large footprint spaceborne lidar, where short, widely-spaced trees may limit the capacity for accurate AGB estimation. In this study, we present a scaled methodological framework for linking field-measured woodland AGB to ALS data and, in turn, linking ALS-modeled AGB to satellite data, using piñon-juniper woodlands in southeastern Utah as a case study. We compare the effectiveness of this scaling approach using two satellite sensors, Landsat 8 OLI (multispectral) and GEDI (lidar). Since the predicted outputs of our local-scale model are being used as inputs to our regional-scale model, we also demonstrate an approach for propagating uncertainty throughout this nested, multiscale analytical framework, leveraging the inherent variability within a random forest's decision trees. Given the positional uncertainty of GEDI footprints, we test a range of different footprint sizes for their relative effects on ALS-GEDI AGB model accuracy. Our local-scale (field-ALS) predictive model was able to account for 74% of variance in AGB, and estimate AGB with a root mean squared error (RMSE) of 14 Mg/ha, a mean absolute error (MAE) of 11.09 Mg/ha. Our regional-scale (ALS-Landsat/GEDI) analysis with propagated uncertainty revealed that the combined use of Landsat and GEDI metrics produced the best predictive model (R2 = 0.68; RMSE = 12.71 Mg/ha; MAE = 9.40 Mg/ha), followed by Landsat-only metrics (R2 = 0.66, RMSE = 13.08 Mg/ha; MAE = 9.71 Mg/ha), and GEDI-only metrics (R2 = 0.49, RMSE = 16.01 Mg/ha; MAE = 12.14 Mg/ha). These results suggest that Landsat may be better-suited than GEDI for estimating AGB in woodland environments where low canopy covers and short trees limit the capacity for precisely characterizing vegetation structure within large-footprint, waveform lidar data. The footprint size analysis revealed that larger simulated footprints (e.g., 30 m radius and greater) produced higher GEDI model accuracies; however, increasing footprint radii beyond 30 m does not significantly increase model accuracy. This research represents an important step forward in improving our capacity for reliably mapping woodland AGB, and provides an early test case for the application of GEDI data to woodland AGB mapping.

Sentinel-1 Coherence for Mapping Above-Ground Biomass in Semiarid Forest Areas

The interferometric coherence of repeat-pass C-band synthetic aperture radar observations has shown potential for mapping forest above-ground biomass (AGB) when images were acquired with short temporal repeat intervals and under stable environmental conditions. Since 2014, Sentinel-1 collects repeated interferometric image pairs at global scale with repeat intervals of 6 or 12 days. For a semiarid forest site in California, we evaluated one year of Sentinel-1 6- and 12-day (60 and 59 images, respectively) repeat-pass coherence images with the objective of retrieving AGB. The retrieval was performed by inverting the semiempirical Interferometric Water Cloud Model. The retrieval based on individual coherence images was most accurate (~50% relative root-mean-square difference with respect to a Lidar-derived map) when images were acquired in extended periods of dry conditions. The retrieval accuracy improved by approximately 15% units when combining multiple coherence images in the retrieval. Finally, the accuracy of the retrieval with multitemporal 6- or 12-day repeat-pass coherence images differed by only 2% units. The results of this study advocate consideration of Sentinel-1 repeat-pass coherence in AGB mapping efforts in semiarid forest areas.

Estimating above-ground biomass of Pinus densata Mast. using best slope temporal segmentation and Landsat time series

A time-series filtering algorithm named the best slope temporal segmentation filtering (BSTS) algorithm was developed to improve the estimation accuracy of above-ground biomass (AGB) of Pinus densata Mast. In this algorithm, the best slope was used to interrupt the time series. Following interruptions, linear regression was employed to simulate each segment of time series. By splicing the time-series fragments, a new reconstructed time series would be generated. The algorithm was used to reconstruct the annual synthetic Landsat surface reflectance images collection in Shangri-La from 1987 to 2019. Permanent sampling plots of Pinus densata Mast. in 1987, 1992, 1997, 2002, 2007, 2012, and 2017 were collected. Then the multiple linear regression and random forests regression (RFR) were used to estimate the AGB of the Pinus densata Mast. The Landsat time series used before and after filtering were evaluated based on the quantitative analysis. The results with RFR showed that for modeling, the R2 of the time series reconstructed by the BSTS is 0.913, which is higher than the data before reconstructed (R2 = 0.869). The root-mean-square error (RMSE) of the reconstructed time series (RMSE = 35.38 t / ha) is significantly lower than the data before reconstructed (RMSE = 43.43 t / ha). For validation, the average prediction accuracy (p) before filtering is 61.92%, and the relative root-mean-square error (rRMSE) is 48.52%; whereas the p after filtering is increased to 70.66%, and the rRMSE is reduced to 35.38%. In summary, the BSTS has improved the accuracy of remote sensing-based estimation of AGB of Pinus densata Mast., which provided a method for large area AGB mapping for Pinus densata Mast. in Shangri-La.

Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models estimating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems.

Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery

AbstractCoastal wetland biomass is an important indicator of wetland productivity, carbon storage, health, and vulnerability to climate change. The ability to estimate aboveground biomass (AGB) in wetlands at ecologically relevant scales is complicated by the spatial variability inherent to patterns in wetland vegetation and the biogeomorphic processes that help create them. Remote sensing provides an approach for mapping wetland biomass, but the spatial resolutions of satellite and airborne imagery often constrain the types of ecological patterns and processes that can be detected. Unmanned Aerial Vehicles (UAVs) have previously been used to capture fine‐scale (≤1 m) variability in AGB in coastal wetland settings. However, it remains unclear if a UAV approach to estimating wetland biomass is transferrable across diverse wetland sites or how it compares to commonly used satellite‐based approaches. Here, we test the capabilities of UAVs in remotely quantifying AGB and compare biomass estimation using UAV and Landsat satellite imagery (30 m resolution) in several wetland sites in Southern California. Field surveys highlight significant spatial variability in wetland plant community AGB and height that influence remote biomass estimation. Relationships between UAV vegetation indices and AGB were site‐specific and influenced by vegetation types. Biomass estimation using UAVs (r2 = 0.40, RMSE = 534.6 g m−2) showed better correlation with NDVI than a Landsat‐based approach (r2 = 0.26, RMSE = 596.8 g m−2). We found combining high‐resolution UAV AGB maps and Landsat NDVI to develop AGB models showed the highest correlation (r2 = 0.45, RMSE = 659.7 g m−2) and provided additional spatial information to aid scaling field data to satellite imagery. Overall, UAVs captured more spatial complexity in aboveground biomass at finer scales than is possible with moderate‐resolution Landsat pixels, indicating that UAVs can be used to characterize patterns of within‐marsh variability resulting from local‐scale (≤ 100s of meters) ecological processes.

Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model

The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models.

High-resolution forest carbon mapping for climate mitigation baselines over the RGGI region, USA

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA’s Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r 2 = 0.38, bias = −2.3, RMSE = 45.2 Mg ha−1), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r 2 = 0.32, bias = 4.0, RMSE = 49.4 Mg ha−1) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA’s spaceborne lidar missions.

Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data

We evaluate the potential of using a process-based ecosystem model (BEPS) for crop biomass mapping at 20 m resolution over the research site in Manitoba, western Canada driven by spatially explicit leaf area index (LAI) retrieved from Sentinel-2 spectral reflectance throughout the entire growing season. We find that overall, the BEPS-simulated crop gross primary production (GPP), net primary production (NPP), and LAI time-series can explain 82%, 83%, and 85%, respectively, of the variation in the above-ground biomass (AGB) for six selected annual crops, while an application of individual crop LAI explains only 50% of the variation in AGB. The linear relationships between the AGB and these three indicators (GPP, NPP and LAI time-series) are rather high for the six crops, while the slopes of the regression models vary for individual crop type, indicating the need for calibration of key photosynthetic parameters and carbon allocation coefficients. This study demonstrates that accumulated GPP and NPP derived from an ecosystem model, driven by Sentinel-2 LAI data and abiotic data, can be effectively used for crop AGB mapping; the temporal information from LAI is also effective in AGB mapping for some crop types.

Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity

A map of natural forest aboveground biomass (AGB) can serve as a baseline for forest managers to integrate multi-temporal information to account for the trend of reducing carbon emission from the forest degradation over time. Taking advantage of spectral and spatial enhancement in Landsat OLI images, a machine learning based kNN algorithm was applied to derive AGB maps of a pristine forest of Pinus kesiya in 2014 and 2019 via allometric models of the pine and inventory data. AGB productivity of the study site was derived and examined for diagnosing the pristine pine forest development. The radiance of the Landsat image was first restored and then normalized by the FLAASH model, a radiation transfer model-based algorithm used to derive surface reflectance image, and finally a pansharpening technique was applied to improve the spatial resolution while retaining the spectral resolution of the reflectance image. Results showed that the pansharpened surface reflectance image was able to derive AGB maps with a satisfactory accuracy of root mean square percentage error, RMSPE = 15%, which shows significant improvement on the performance of AGB estimation using the raw image (RMSPE = 56%) and non-pansharpened surface reflectance image (RMSPE = 48%). The loss of AGB stocks is mainly caused by landslide, forest fire, and agricultural expansion, particularly the use of fire for land preparation for cropping. Land covers other than pine stands have complicated the edge of the pristine pine forest. The non-forest cover types surrounded the forest enlarged the boundary of the forest and made the pristine forest slightly fragmented. The results also showed that the AGB loss/gain of a forest parcel is negatively/positively related to the size of the parcel. Appropriate forest land zoning and community-based forest management for the surrounding non-pristine pine forest could have the benefit of mitigating the degradation of the pristine pine forest and improving the biomass stocks for better REDD achievements.

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.

Estimation of aboveground biomass from PolSAR and PolInSAR using regression-based modelling techniques

In the field of forestry studies, microwave remote sensing has broad applications due to the penetration into the semi-transparent media. This feature is used for the estimation of biophysical parameters and monitoring of deforestation. Therefore, the estimation of biophysical parameters is essential for assessing carbon stock management. Hence, the aboveground biomass (AGB) using synthetic aperture radar (SAR) data is recognized as typical approaches in forest application. However, the integrated use of polarimetric (PolSAR) and interferometric (PolInSAR) data might be more efficient tools for AGB mapping. Accordingly, in this study with the integrated data, the efficiency of machine learning techniques including random forest regression (RFR) and multiple linear regression (MLR) model were assessed and compared for the prediction of AGB. The analyses were performed using an image pair of fully polarimetric Radarsat-2 C-band data set and the related field data of Malhan Forest Range, Dehradun Forest Division, which were collected using the systematic sampling technique. Particularly, the training and testing of the models were done using the field sample plots. The experimental results showed that the RFR algorithm provided a better prediction result of AGB than the MLR model. The correlation coefficient (R 2) and root-mean-square error (RMSE) for the RFR algorithm was estimated to be around 0.65 and 24.33 Mg/ha, respectively, while for the MLR model, R2 and RMSE are estimated as 0.54 and 33.05 Mg/ha, respectively. Therefore, it was concluded that the prediction of AGB through the machine learning technique using PolSAR and PolInSAR data has a significant advantage for accurate estimation of the AGB.

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.

联合GF-6和Sentinel-2红边波段的森林地上生物量反演

光谱反射率能反映地物差异，是森林地上生物量（Aboveground Biomass，AGB）遥感反演的理论基础。红边波段处于近红外与红光波段交界处快速变化的区域，能对植被冠层结构和叶绿素含量的微小变化做出快速反应，对植被生长状况较敏感。研究以GF-6和Sentinel-2多光谱影像作为数据源，结合野外调查AGB数据，构建落叶松和樟子松AGB线性和非线性估测模型，通过比较模型精度选择最优模型进行森林AGB反演和空间分布制图。结果表明：GF-6和Sentinel-2影像红边波段反射率与落叶松、樟子松AGB均呈显著相关（P<0.05），红边波段对AGB估测较敏感。多变量估测模型整体估测效果优于单变量模型，所有模型中多元线性回归模型取得了最优的决定系数（落叶松R²=0.66，樟子松R²=0.65）和最低的均方根误差（落叶松RMSE=31.45 t/hm²，樟子松RMSE=54.77 t/hm²）。相比单个数据源，联合GF-6和Sentinel-2影像构建的多元线性回归模型估测效果得到了显著提升，模型RMSE对于落叶松和樟子松AGB估测分别最大降低了22.9%和11.2%。增加红边波段进行AGB估测能显著提高模型估测精度，三组数据源分别加入红边波段信息后进行建模，模型RMSE得到了显著降低。GF-6拥有800 km观测幅宽和高效的重访周期，可以快速地提供大尺度时间序列数据，在森林地上生物量反演和动态监测方面有着很大潜力。;Spectral reflectance can reflect the difference of ground objects, which is the theoretical basis of forest aboveground biomass (AGB) in remote sensing inversion. Red edge band is located in the special wavelength and scope which change fast at the junction of near-infrared and red bands, which can quickly respond to the small changes of vegetation canopy structure and chlorophyll content. Compared with other bands, red edge band is more sensitive to vegetation growth and chlorophyll change. In this study, GF-6 and Sentinel-2 multispectral images were used as data sources, and the linear and nonlinear models were constructed on the basis of Larch and Scotch pine field survey data for AGB estimation. The estimation accuracy of all models was compared, and the model with the highest estimation accuracy was regarded as the optimal model for the final AGB mapping in the study area. The results showed that the red edge reflectance of GF-6 and Sentinel-2 images were both significantly correlated with AGB (P<0.05), and red edge bands were more sensitive to AGB estimation than other bands. On the whole, the estimation effect of multivariable estimation model was better than that of univariate model. Multivariate linear regression (MLR) model obtained the highest determination coefficient (R²=0.66 for Larch and 0.55 for Scotch pine) and the lowest root mean square error (RMSE=31.45 t/hm² for Larch and 54.77 t/hm² for Scotch pine). Compared with single data source (GF-6 or Sentinel-2), the estimation effect of multiple linear regression model constructed by combining GF-6 and Sentinel-2 images was significantly improved, and the RMSE of AGB estimation model for Larch and Scotch pine decreased by 22.9% and 11.2% in highest measure, respectively. Taking the red edge band as additional variables for AGB estimation can significantly improve the estimation accuracy and effect of the model. The RMSE of the model was significantly reduced by adding red edge band information to three groups of data sources which are GF-6, Sentinel-2 and Sentinel-2 combined with GF-6. GF-6 has an observation width of 800 km and efficient revisit period, which can provide large-scale time series data quickly. As a remote sensing data source, GF-6 has great potential in forest aboveground biomass estimation and dynamic monitoring.