林草碳汇遥感监测
Theme Keywords： remote sensingtree species coveragetree heighttree growth equationsynthetically training dataspatiotemporal simulation modelsspatial and temporal trendssingle tree-sample plot-region
- The Paper
- Abstract：In recent years, quantitative studies on CO2 fertilization effects(β) have gradually become a hotspot in global carbon cycle research. The rising atmospheric CO2 concentration largely affects the changes in gross primary productivity (GPP), which in turn may have an impact on net ecosystem productivity (NEP). Therefore, in the context of global climate change, accurately quantifying the response of GPP to the rise in atmospheric CO2 (
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-03-06 - Abstract：Forests constitute the largest carbon pools in terrestrial ecosystems, and elucidating their baseline carbon stock and carbon sink potential is crucial for attaining the nation’s “dual carbon” strategic objectives. Remote sensing, owing to its macroscale, comprehensive, dynamic, rapid, and reproducible nature, has addressed the limitations in carbon accounting for forest vegetation. The estimation of global/regional forest vegetation carbon sinks using spectral information from satellite remote sensing images, vertical structure information collected by airborne/satellite LiDAR, and ground observation data has become a popular topic. Establishing a forest vegetation carbon accounting method based on remote sensing is urgently needed to serve the national “dual carbon” goals and the demand of carbon trading market.This paper introduces a novel system for forest vegetation carbon accounting, grounded in the structure and growth equations of individual trees. Specifically, the system has the following characteristics: integrating airborne and terrestrial LiDAR data to extract structural parameters, including Diameter at Breast Height (DBH), tree height, and crown width, thereby establishing a carbon stock calculation method for forest plots at the individual tree level; developing a pixel-level regional forest carbon stock model with physical interpretability, utilizing canopy height and crown closure as key variables, to mitigate the uncertainty of machine/deep learning remote sensing regression inversion; accurately estimating regional forest carbon sinks by forecasting future forest canopy height and crown closure on the basis of the pixel-level forest carbon stock model and individual tree growth equations.Calculation of forest plot carbon stock. The DBH and tree height parameters are extracted by ground stations and unmanned aerial vehicle LiDAR, the biomass of individual trees is obtained using the allometric growth equation, and the carbon stock of forest plots is calculated. Calculation of regional forest carbon stock. The carbon stock density of forests is closely related to height. A pixel-level forest carbon stock explicit model is established using forest canopy density and height, and the model parameters are automatically calculated from the structure equation of individual trees (DBH-tree height-crown width). Prediction of regional forest carbon sinks. Future forest canopy closure and height are derived using the structure and growth (tree height-tree age) equation of individual trees, and forest carbon stock model and the latest remote sensing data are combined to calculate and update forest carbon sinks.This study adheres to the overarching theme of “forest plot carbon stock-regional forest carbon stock-regional forest carbon sink,” expanding from plot to regional spatial scales and extending from carbon stock to carbon sink across temporal scales, thereby establishing a novel remote sensing-based system for forest vegetation carbon accounting.Keywords：Forest carbon stock;Forest carbon sink;Carbon accounting;remote sensing;DBH-tree height-crown width-tree age;single tree-sample plot-region;Structure equation of individual tree;Growth equation of individual tree;crown closure4258|7351|7<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-03-06
- Abstract：Cold-temperate forests, recognized as the most extensive terrestrial ecosystems, cover vast areas around the globe and hold important ecological and social values. Accurate mapping of forest type and tree species cover fraction in these forests across space and time is crucial for quantifying ecosystem services and formulating effective forest management policies to ensure their sustainable conservation. However, despite the increasing development of remote sensing technologies, studies exploring the feasibility of inverting forest type and tree species cover fraction using medium-resolution multispectral satellite-based data, such as Landsat, in China’s cold-temperate forests are limited. This limitation is primarily attributed to the scarcity of reference data and the restricted spectral information available in multispectral images. Moreover, the quantitative impact of the temporal frequency of data acquisitions (e.g., single-date, multidate) on mapping forest type and tree species cover fraction remains largely unexplored. The timing and frequency of satellite data acquisition can significantly influence the detection and characterization of dynamic changes in forests, which in turn affects the accuracy of mapping forest attributes. To address these gaps, our study aims to map the forest type and tree species cover fraction in Mengjiagang Forest, Heilongjiang Province by employing synthetically mixed data and a random forest regression model. We extend our analysis to three decades (from 1986 to 2020) of Landsat data, mapping the cover fractions of broadleaf and needleleaf forests in Mengjiagang Forest by using an optimal broadleaf and needleleaf random forest regression model. The results of our study reveal the following key findings: (1) For forest type cover fraction inversion, the random forest regression model based on the growing season median index (including spectral bands, NDTI, and TCT indices) is the optimal model (achieving
- Abstract：The forest canopy structure plays a crucial role in regulating the exchange of substances and energy between plants and the atmosphere, thereby influencing regional microclimate and ecosystem functionality. Accurate characterization of vegetation canopy structure is of significant importance for forest ecosystem research, such carbon storage estimation, carbon cycle simulation etc. Canopy structural complexity, also known as canopy structural biodiversity, which describes the spatial distribution of branches and leaves within the canopy, has emerged as a key attribute in forest ecosystems and has found wide application in related research. For example, carbon cycle, mechanisms of community composition, sustainable forest management, wildlife conservation, forest disturbance monitoring and restoration, forest microclimate research and so on.Traditional ground-based survey methods have limitations as they only provide partial information through statistical values, which primarily involve plot-based surveys using tools such as diameter tapes, clinometers, and angle gauges to obtain individual tree information such as tree position, diameter at breast height, tree height, and crown width. The heterogeneity of these measured tree attributes and their distribution, such as diameter at breast height and tree height, or combinations of tree height, diameter at breast height, and tree density, are used to quantify canopy structure complexity, including the standard deviation, coefficient of variation, and Gini coefficient of survey attributes. However, these indices may not fully represent canopy structural complexity.The rapid development of lidar technology has enabled the rapid acquisition of three-dimensional structural information for entire forests, offering new opportunities for comprehensive and accurate characterization of canopy structure complexity. In addition to the indicators used in traditional ground-based survey methods, existing quantitative indices for canopy structure complexity based on lidar data can generally be divided into three categories: horizontal distribution indices, vertical distribution indices, and integrated distribution indices. Horizontal distribution indices primarily quantify the horizontal spatial distribution of canopy elements, without considering their vertical distribution, such as canopy cover, canopy closure, and leaf area index. Vertical distribution indices mainly describe the heterogeneity of canopy element distribution in the vertical direction while neglecting their horizontal distribution including canopy effective layers and leaf height diversity and so on. Integrated distribution indices consider both the horizontal and vertical distribution heterogeneity of canopy structure, thereby overcoming the limitations of solely considering a single direction in horizontal or vertical distribution indices, for example canopy fractal dimension, canopy roughness, and canopy entropy.Finally, we summarize the current applications of canopy structure complexity in regulating forest ecosystem functions, including light resource utilization, precipitation interception, microclimate modulation, productivity, and ecosystem stability. Additionally, there are key issues and directions that require emphasis in forest ecosystem research related to canopy structure complexity. These include investigating the cross-platform generality of lidar-based indicators, addressing scale issues, and establishing long-term monitoring methods. While the concept of forest canopy structure complexity is relatively new and has limited application in China, we anticipate that advancements in characterization methods and a deeper understanding of its implications will be facilitated by the increasing availability of long-term, multi-source remote sensing data and the utilization of various deep learning methods.Keywords：ecological remote sensing;forest canopy structure;forest ecosystem;light regulation;precipitation interception;productivity;microclimate;forest stability4279|5095|5<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-03-06
- Abstract：Forest age is a critical parameter determining forest carbon sequestration capacity and its temporal trends. Quantifying spatiotemporal variations in forest age is essential for predicting forest ecosystem carbon dynamics. While traditional forest age assessments were limited to forest plots, the development of remote sensing technology has expanded the estimation from plots to regional and global scales. Research related to forest age has gaining increasing attention across fields of forestry, ecology, and geography, etc. This article aims to review the process in forest age estimation by summarizing the main methods and their applications from related literatures and datasets published since the year of 2000.Remote sensing-based approaches fall into three main categories: 1. regression from image spectral and texture features, 2. time series change detection, and 3. tree height or biomass growth equation modeling. 1.The spectral image regression method is straightforward but often has limited regression accuracy due to the saturation effect in the spectral image information-forest age relationship. 2. The time series change detection method can achieve high accuracy but only applicable to forests with continuous remote sensing observations. 3. The tree height or biomass growth equations based strategy can broaden the limits of forest age estimation, but its estimation accuracy is sensitive to the selections of model equation and input parameters. Consequently, integrating multisource datasets and combining multiple modeling approaches have become the predominant strategy for forest age estimation. This strategy has been successfully implemented in high-resolution forest age mapping at national scales across China and Canada.The advancement of remote sensing technology has substantially improved the efficiency and accuracy of forest age estimation, extending its applicability from individual plots to regional and global scales. Large-scale forest age data have great potential for applications in forest carbon cycle modeling, biodiversity assessment, and forest management. Future research should focus on improving and updating forest measurement datasets, fully leveraging multisource and multispatial-temporal remote sensing information, and enhancing the transferability and generality of estimation models.Keywords：remote sensing;forest age;tree height;above ground biomass;carbon cycle;change detection;tree growth equation;forest management;biodiversity1494|3791|1<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-03-06
- Abstract：Land Use/Cover Change (LUCC) is a direct driver of the carbon balance in terrestrial ecosystems, and its impact on global warming is second only to fossil fuel and industrial emissions. The forest ecosystem is the largest carbon pool in terrestrial ecosystems and has an important role to play in addressing global climate change and achieving carbon neutrality targets. However, the limited availability of LUCC data has led to a significant underestimation of its impact on carbon emissions, and the lack of spatiotemporal LUCC data under future climate scenarios also introduced considerable uncertainty in exploring the response of the forest carbon cycle to LUCC. How to simulate LUCC and analyze the impact of LUCC on the carbon cycle of forest ecosystems have become key research focuses both domestically and internationally. This study systematically reviewed the progress of research on past LUCC extraction, spatiotemporal LUCC simulations, forest carbon balance estimation methods, and the impact of LUCC on the forest carbon cycle. The advantages, applicability, and existing challenges of different LUCC simulation models and forest carbon balance estimation models were listed and analyzed.First, this review summarized historical LUCC extraction methods and highlights the urgent need to integrate deep learning techniques to improve the accuracy of LUCC change detection, thereby providing more reliable data for future LUCC simulations. Second, this review generalized the mainstream models for future LUCC spatiotemporal simulations. It emphasized the importance of coupling deep learning algorithms with the SD model while integrating meteorological and socioeconomic driving factors. This approach would more comprehensively account for feedback mechanisms between natural and anthropogenic factors, thereby enhancing the accuracy and applicability of simulations. Subsequently, this review organized the commonly used methods in current forest carbon cycle modeling and highlights recent developments in the field. It noted that carbon balance estimation has increasingly shifted toward remote sensing-driven process models, gradually replacing the traditional approach of combining remote sensing with parameterized models. This shift allows for the incorporation of more comprehensive and detailed ecosystem processes based on previous methodologies. Finally, this review examined the research on the effects of LUCC on carbon cycles. It pointed out that most current studies loosely couple LUCC simulation results with process-based ecosystem models, neglecting the dynamic effects of LUCC on key physiological and biochemical parameters of the forest carbon cycle, such as LAI and chlorophyll. Future research should leverage remote sensing and other technologies to strengthen simulations of the spatiotemporal LAI distribution. This could reduce uncertainties in carbon sink estimation and improve the precision of assessments of carbon sink potential driven by LUCC.Despite great progress in recent years, future research should focus on optimizing the reconstruction of historical LUCC, spatial-temporal simulations, and the parameterization coupling of carbon cycles. This would provide a more comprehensive understanding of the response mechanisms of forest carbon cycles to LUCC. In summary, leveraging remote sensing data as a basis to simulate LUCC and drive process-based models to achieve accurate spatial and temporal simulation of forest ecosystem carbon cycle remains critical research directions in future LUCC and carbon cycle related to it.Keywords：land use/cover change;spatiotemporal simulation models;forest carbon cycle models;carbon neutrality;remote sensing4085|9264|3<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2025-03-06
