Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).</p

Multimodal deep learning for mapping forest dominant height by fusing GEDI with earth observation data

The integration of multisource remote sensing data and deep learning models offers new possibilities for accurately mapping high spatial resolution forest height. We found that GEDI relative heights (RH) metrics exhibited strong correlation with the mean of the top 10 highest trees (dominant height) measured in situ at the corresponding footprint locations. Consequently, we proposed a novel deep learning framework termed the multi-modal attention remote sensing network (MARSNet) to estimate forest dominant height by extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2 PALSAR-2 data, Sentinel-2 optical data and ancillary data. MARSNet comprises separate encoders for each remote sensing data modality to extract multi-scale features, and a shared decoder to fuse the features and estimate height. Using individual encoders for each remote sensing imagery avoids interference across modalities and extracts distinct representations. To focus on the efficacious information from each dataset, we reduced the prevalent spatial and band redundancies in each remote sensing data by incorporating the extended spatial and band reconstruction convolution modules in the encoders. MARSNet achieved commendable performance in estimating dominant height, with an R2 of 0.62 and RMSE of 2.82 m, outperforming the widely used random forest approach which attained an R2 of 0.55 and RMSE of 3.05 m. Finally, we applied the trained MARSNet model to generate wall-to-wall maps at 10 m resolution for Jilin, China. Through independent validation using field measurements, MARSNet demonstrated an R2 of 0.58 and RMSE of 3.76 m, compared to 0.41 and 4.37 m for the random forest baseline. Our research demonstrates the effectiveness of a multimodal deep learning approach fusing GEDI with SAR and passive optical imagery for enhancing the accuracy of high resolution dominant height estimation

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson’s correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj \u3e 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images

Mapping Forest Health Using Spectral And Textural Information Extracted From Spot-5 Satellite Images

Forest health is an important variable that we need to monitor for forest management decision making. However, forest health is difficult to assess and monitor based merely on forest field surveys. In the present study, we first derived a comprehensive forest health indicator using 15 forest stand attributes extracted from forest inventory plots. Second, Pearson’s correlation analysis was performed to investigate the relationship between the forest health indicator and the spectral and textural measures extracted from SPOT-5 images. Third, all-subsets regression was performed to build the predictive model by including the statistically significant image-derived measures as independent variables. Finally, the developed model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE). Additionally, the produced model was further validated for its performance using the leave-one-out cross-validation approach. The results indicated that our produced model could provide reliable, fast and economic means to assess and monitor forest health. A thematic map of forest health was finally produced to support forest health management

Multistage Sampling and Optimization for Forest Volume Inventory Based on Spatial Autocorrelation Analysis

It is important to achieve estimates at the minimum cost, with no greater uncertainty than that which is appropriate for the objectives of the inventory. The aim of this study was to estimate the forest volume efficiently and accurately by sampling and analyzing the existing forest survey data, which is also a technical challenge. In this work, we used the spatial statistics tools in the ArcGIS software to analyze spatial autocorrelations with the data from the sixth to ninth continuous forest inventories (CFI) of Sichuan Province from 2002, 2007, 2012, and 2017. Based on the sampling framework of the CFI, we divided the sampling units into five groups using different methods to create the second-stage samples. Combined with the spatial autocorrelation analysis results, we selected certain samples from the collection of second-stage samples through stratified sampling to form the third-stage sampling units. We applied the sampling ratio, sampling accuracy, workload, and costs as the evaluation indexes for the sampling efficiency analysis. The main results are as follows: Before conversion, the forest volume density had a positively skewed distribution. There was substantial positive spatial autocorrelation, and its intensity was affected by the distance scale, especially at 187.3 km, where the spatial processes of clustering were most pronounced. At the significance level of α = 0.01, the high-volume stands were mainly concentrated in the Aba Prefecture, Garze Prefecture, and Liangshan Prefecture, while the low-volume stands were mainly concentrated in the Sichuan Basin region. The heterogeneous gatherings were staggered between the high-volume areas and low-volume areas, while the transition zone between the three prefecture regions and basin region was randomly distributed. With 95% reliability, the average estimation accuracy of the systematic sampling, random sampling, and cluster sampling in the second stage was 94.09%, which is less accurate than the CFI estimation accuracy. The mean correlation coefficients (R) between the estimated value of the forest volume and the observations of the systematic sampling, random sampling, and cluster sampling in the second stage were 0.95, 0.98, and 0.96, respectively. The relative differences (RD%) were −0.52, −0.39, and −0.36, respectively. The spatial stratified sampling in the third stage, which is based on spatial distribution pattern information, significantly reduced the sampling ratio to 1.68 per 10,000, compared with the average ratios of the CFI sampling and second-stage sampling, which were 13.73 per 10,000 and 2.75 per 10,000, respectively. With 95% reliability, the mean accuracy of the spatial stratified sampling in the third stage was 93.05%, the R was 0.94, and the RD% was −0.09. Spatial stratified sampling is more in line with the actual work conducted in annual surveys because it effectively reduces the sample size using prior spatial information, which can better meet the requirements of the annual output

Regression Models for Estimating Aboveground Biomass and Stand Volume Using Landsat-Based Indices in Post-Mining Area

This paper describes the use of remotely sensed data to measure vegetation variables such as basal area, biomass and stand volume. The objective of this research was developed regression models to estimate basal area (BA), aboveground biomass (AGB), and stand volume (SV) using Landsat-based vegetation indices. The examined vegetation indices were SAVI, MSAVI, EVI, NBR, NBR2 and NDMI.&nbsp;&nbsp; Regression models were developed based on least-squared method using several forms of equation, i.e., linear, exponential, power, logarithm and polynomial. &nbsp;Among those models, it was recognized that the best fit of model was obtained from the exponential model, log (y) = ax + b for estimating BA, AGB &amp; SV.&nbsp; The MSAVI had been identified as the most accurate independent variable to estimates basal area with R² of 0.70 and average verification values of 16.39% (4%-32.66%); while the EVI become the best independent variable for estimating aboveground biomass (AGB) with R2 of 0.72 and average of verification values of 18,10% (9%-28.01%); and the NDMI was recognized to be the best independent variable to estimate stand volume with R2 of 0.69 and average of verification values of 24.37% (-15%-38.11%)

Optical remote sensing of aboveground forest biomass and carbon stocks in resource-constrained African environments.

Ph. D. University of KwaZulu-Natal, Pietermaritzburg 2015.No abstract available

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