Search Results (50)

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in mountainous forest areas with significant topographic relief, overcoming the limitations imposed by complex terrain conditions to achieve high-precision forest stock volume estimation has emerged as one of the most challenging and cutting-edge research areas in vegetation remote sensing. Objective: This study aims to explore the feasibility and methods of forest stock volume estimation using spaceborne lidar data ICESat-2/ATL08 in complex terrain and to compare the effectiveness of three machine learning regression models for this purpose. Method: Based on the ATL08 product from ICESat-2/ATLAS data, a sequential Gaussian conditional simulation was used for spatial interpolation of forest areas in Jingdong Yi Autonomous County, Pu’er City, Yunnan Province. XGBoost, LightGBM, and Random Forest methods were then employed to develop stock volume models, and their estimation capabilities were analyzed and compared. Results: (1) Among the 57 ICESat-2/ATLAS footprint parameters extracted, 13 were retained for interpolation after analysis and screening. (2) Based on sequential Gaussian conditional simulation, three parameters demonstrating lower interpolation accuracy were eliminated, with the remaining ten parameters allocated for inversion model development. (3) In terms of inversion model accuracy, XGBoost outperformed LightGBM and Random Forest, achieving an R² of 0.89 and an rRMSE of 10.5912. The average forest stock volume derived from the inversion was 141.00 m³/hm². Conclusions: Overall, large-area forest stock volume estimation through spaceborne Lidar inversion using ICESat-2/ATLAS photon-counting footprints proved feasible for mountainous environments with complex terrain. The XGBoost method demonstrates strong forest stock volume inversion capabilities. This study provides a case study for investigating forest structure parameters in complex mountainous terrain using spaceborne lidar ICESat-2/ATLAS data. Full article

The Advanced Topographic Laser Altimeter System (ATLAS) on the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a photon-counting detection mode with a 532 nm laser to obtain high-precision Earth surface elevation data and offers a new remote sensing method for nearshore bathymetry. The key issues in using ATLAS ATL03 data for bathymetry are achieving automatic and accurate extraction of signal photons in different water environments. Especially for areas with sharply fluctuating topography, the interaction of various impacts, such as topographic fluctuations, sea waves, and laser pulse direction, can result in a sharp change in photon density and distribution at the seafloor, which can cause the signal photon detection at the seafloor to be misinterpreted or omitted during analysis. Therefore, an improved size and direction adaptive filtering (ISDAF) method was proposed for nearshore bathymetry using ATLAS ATL03 data. This method can accurately distinguish between the original photons located above the sea surface, on the sea surface, and the seafloor. The size and direction of the elliptical density filter kernel automatically adapt to the sharp fluctuations in topography and changes in water depth, ensuring precise extraction of signal photons from both the sea surface and the seafloor. To evaluate the precision and reliability of the ISDAF, ATLAS ATL03 data from different water environments and seafloor terrains were used to perform bathymetric experiments. Airborne LiDAR bathymetry (ALB) data were also used to validate the bathymetric accuracy and reliability. The experimental findings show that the ISDAF consistently exhibits effectiveness in detecting and retrieving signal photons, regardless of whether the seafloor terrain is stable or dynamic. After applying refraction correction, the high accuracy of bathymetry was evidenced by a strong coefficient of determination (R²) and a low root mean square error (RMSE) between the ICESat-2 bathymetry data and ALB data. This research offers a promising approach to advancing remote sensing technologies for precise nearshore bathymetric mapping, with implications for coastal monitoring, marine ecology, and resource management. Full article

Biomass carbon sequestration and sink capacities of tropical rainforests are vital for addressing climate change. However, canopy height must be accurately estimated to determine carbon sink potential and implement effective forest management. Four advanced machine-learning algorithms—random forest (RF), gradient boosting decision tree, convolutional neural network, and backpropagation neural network—were compared in terms of forest canopy height in the Hainan Tropical Rainforest National Park. A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. The RH80 percentile model using the RF algorithm was employed to estimate the forest canopy height distribution in the Hainan Tropical Rainforest National Park from 2003 to 2023, and the canopy heights of five forest types (tropical lowland rainforests, tropical montane cloud forests, tropical seasonal rainforests, tropical montane rainforests, and tropical coniferous forests) were calculated. The study found that from 2003 to 2023, the canopy height in the Hainan Tropical Rainforest National Park showed an overall increasing trend, ranging from 2.95 to 22.02 m. The tropical montane cloud forest had the highest average canopy height, while the tropical seasonal forest exhibited the fastest growth. The findings provide valuable insights for a deeper understanding of the growth dynamics of tropical rainforests. Full article

The intertidal zone, as a dynamic ecosystem at the interface of land and sea, plays a critical role in environmental protection and disaster mitigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS) with the ability to penetrate the water bodies, enabling its use for bathymetric measurements. However, the complex land cover types and frequent environmental changes in intertidal zones pose significant challenges for precise measurement and dynamic monitoring. In an effort to address the denoising challenges of ICESat-2 photon point cloud data in such complex environments, this study proposes an adaptive photon denoising method that is capable of dynamically adjusting the denoising strategy for different types of photon data. ATL03 data from four typical intertidal zones were selected for denoising experiments. The results indicated that the proposed adaptive denoising method achieved average recall, precision, and F-score values of 0.9885, 0.9927, and 0.9906, respectively, demonstrating excellent denoising performance and stability. This method provides an effective data processing approach for high-precision monitoring of intertidal zone topography. Full article

Forest ecosystems are incredibly valuable, and understory terrain is crucial for estimating various forest structure parameters. As the demand for monitoring forest ecosystems increases, quickly and accurately understanding the spatial distribution patterns of understory terrain has become a new challenge. This study used ICESat-2 data as a reference and validation basis, integrating multi-source remote sensing data (including Landsat 8, ICESat-2, and SRTM) and applying machine learning methods to accurately estimate the sub-canopy topography of the study area. The results from the random forest model show a significant improvement in accuracy compared to traditional SRTM products, with an R² of 0.99, ME of 0.22 m, RMSE of 3.59 m, and STD of 3.59 m. In addition, we assessed the accuracy of understory topography estimates for different landforms, canopy heights, forest cover types, and forest coverage. The results demonstrate that the estimation results are minimally impacted by ground elevation, forest cover type, and forest coverage, indicating good stability. This approach holds promise for accurately estimating understory terrain at regional and global scales, providing crucial support for monitoring and protecting forest ecosystems. Full article

Precise information on forest canopy height (FCH) is critical for forest carbon stocks estimation and management, but mapping continuous FCH with satellite data at regional scale is still a challenge. By fusing ICESat-2, Sentinel-1/2 images and ancillary data, this study aimed to develop a workflow to obtain an FCH map using a machine learning algorithm over large areas. The vegetation-type map was initially produced by a phenology-based spectral feature selection method. A forest characteristic-based model was then proposed to map spatially continuous FCH after a multivariate quality control. Our results show that the overall accuracy (OA) and average F1 Score (F1) for eight main vegetation types were more than 90% and 89%, respectively, and the vegetation-type map agreed well with the census areas. The forest characteristic-based model demonstrated a greater potential in FCH prediction, with an R-value 60.47% greater than the traditional single model, suggesting that the addition of the multivariate quality control and forest structure characteristics could positively contribute to the prediction of FCH. We generated a 30 m continuous FCH map by the forest characteristic-based model and evaluated the product with about 35 km² of airborne laser scanning (ALS) validation data (R = 0.73, RMSE = 2.99 m), which were 45.34% more precise than the China FCH, 2019. These findings demonstrate the potential of our proposed workflow for monitoring regional continuous FCH, and will greatly benefit accurate forest resources assessment. Full article

Understory terrain plays a multi-faceted role in ecosystems, biodiversity, and productivity in forests by influencing different major factors, such as hydrological processes, soils, climate, and light conditions. Strong illuminants (e.g., sunlight) from ground surfaces and atmosphere can introduce additional photons into the ATLAS system. These photons can, consequently, be mistakenly identified as laser photons reflected from ground surfaces. The presence of such ambient light, particularly under low-photon-count conditions, can significantly increase elevation measurement errors. In this context, this study aims to propose a method for extracting reliable understory elevation control points under varying forest conditions, based on the parameter attributes of ICESat-2/ATLAS data. The overall filtered data resulted in a coefficient of determination (R²), root mean square error (RMSE), and standard deviation (STD) of 0.99, 2.77 m, and 2.42 m, respectively. The greatest accuracy improvement was found in the Puerto Rico study area, showing decreases in the RMSE and STD values by 2.68 and 2.67 m, respectively. On the other hand, canopy heights and slopes exhibited relatively large impacts on noise interferences. In addition, there were decreases in the RMSE and STD values by 4.57 and 4.64 m, respectively, under the very tall canopy category, whereas under steep slope conditions, the RMSE and STD values of the filtering results decreased by 4.59 and 4.34 m, respectively. The proposed method can enhance the overall accuracy of elevation data, allowing for the significant extraction of understory elevation control points, ultimately optimizing forest management practices and improving ecological assessments. Full article

Currently, precise estimation of understory terrain faces numerous technical obstacles and challenges that are difficult to overcome. To address this problem, this paper combines LiDAR, SAR, and DEM data to estimate understory terrain. The high multivariable-precision spaceborne LiDAR ICESat-2 data, validated by the NEON, are divided into training and validation sets. The training dataset is used as a dependent variable, the SRTM DEM and Sentinel-1 SAR data are regarded as independent variables, a total of 13 feature parameters with high contributions are extracted to construct a Multiple Linear Regression model (MLR), BAGGING model, Random Forest model (RF), and Long Short-Term Memory model (LSTM). The results indicate that the RF model exhibits the highest accuracy among the four models, with R² = 0.999, RMSE = 0.701 m, and MAE = 0.249 m. Then, based on the RF model, the understory terrain at the regional scale is generated, and an accuracy assessment is performed using the validation dataset, yielding R² = 0.999, RMSE = 0.847 m, and MAE = 0.517 m. Furthermore, this paper quantitatively analyzes the effects of slope, vegetation coverage, and canopy height on the estimation accuracy of understory terrain. The results show that as slope, and canopy height increase, the estimation accuracy of the RF model for understory terrain gradually decreases. The accuracy of the understory terrain estimated by the RF model is relatively stable and not easily affected by slope, vegetation coverage, and canopy height. The research on the estimation of understory terrain holds significant practical implications for forest resource management, ecological conservation, and biodiversity protection, as well as natural disaster prevention. Full article

Forests are an important component of the Earth’s ecosystems. Forest canopy height is an important fundamental indicator for quantifying forest ecosystems. The current spaceborne photon-counting Light Detection and Ranging (LiDAR) technique has photon cloud characteristic parameters to estimate forest canopy height, and factors such as the sampling window size have not been quantitatively studied. To better understand the precision for estimating canopy height using spaceborne photon-counting LiDAR ICESat-2/ATLAS (Ice, Cloud, and Land Elevation Satellite-2/Advanced Topographic Laser Altimeter System), this study quantified the impact of photon-counting characteristic parameters, sampling window size, and forest cover. Estimation accuracy was evaluated across nine study areas in North America. The findings revealed that when the photon-counting characteristic parameter was set to H70 (70% of canopy height) and the sampling window length was 20 m, the estimation results aligned more closely with the airborne validation data, yielding superior accuracy evaluation indicators with a root mean square error (RMSE) of 4.13 m. Under forest cover of 81%–100%, our algorithms exhibited high estimation accuracy. These study results offer novel perspectives for the application of spaceborne photon-counting LiDAR ICESat-2/ATLAS in forestry. Full article

In September 2018, NASA launched the ICESat-2 satellite into a 500 km high Earth orbit. It carried a truly unique lidar system, i.e., the Advanced Topographic Laser Altimeter System or ATLAS. The ATLAS lidar is capable of detecting single photons reflected from a wide variety of terrain (land, ice, tree leaves, and underlying terrain) and even performing bathymetric measurements due to its green wavelength. The system uses a single 5-watt, Q-switched laser producing a 10 kHz train of sub-nanosecond pulses, each containing 500 microjoules of energy. The beam is then split into three “strong” and three “weak” beamlets, with the “strong” beamlets containing four times the power of the “weak” beamlets in order to satisfy a wide range of Earth science goals. Thus, ATLAS is capable of making up to 60,000 surface measurements per second compared to the 40 measurements per second made by its predecessor multiphoton instrument, the Geoscience Laser Altimeter System (GLAS) on ICESat-1, which was terminated after several years of operation in 2009. Low deadtime timing electronics are combined with highly effective noise filtering algorithms to extract the spatially correlated surface photons from the solar and/or electronic background noise. The present paper describes how the ATLAS system evolved from a series of unique and seemingly unconnected personal experiences of the author in the fields of satellite laser ranging, optical antennas and space communications, Q-switched laser theory, and airborne single photon lidars. Full article

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area. Full article

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth’s surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool’s use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world’s largest complex of shallow-water coastal lagoons. Full article

Spaceborne photon-counting LiDAR holds significant potential for shallow-water bathymetry. However, the received photon data often contain substantial noise, complicating the extraction of elevation information. Currently, a denoising algorithm named ordering points to identify the clustering structure (OPTICS) draws people’s attention because of its strong performance under high background noise. However, this algorithm’s fixed input variables can lead to inaccurate photon distribution parameters in areas near the water bottom, which results in inadequate denoising in these areas, affecting bathymetric accuracy. To address this issue, an Adaptive Variable OPTICS (AV-OPTICS) model is proposed in this paper. Unlike the traditional OPTICS model with fixed input variables, the proposed model dynamically adjusts input variables based on point cloud distribution. This adjustment ensures accurate measurement of photon distribution parameters near the water bottom, thereby enhancing denoising effects in these areas and improving bathymetric accuracy. The findings indicate that, compared to traditional OPTICS methods, AV-OPTICS achieves higher $F1$-values and lower cohesions, demonstrating better denoising performance near the water bottom. Furthermore, this method achieves an average $MAE$ of 0.28 m and $RMSE$ of 0.31 m, indicating better bathymetric accuracy than traditional OPTICS methods. This study provides a promising solution for shallow-water bathymetry based on photon-counting LiDAR data. Full article

In the ecological context of global climate change, ensuring the stable carbon sequestration capacity of forest ecosystems, which is among the most important components of terrestrial ecosystems, is crucial. Forest fires are disasters that often burn vegetation and damage forest ecosystems. Accurate recognition of firegrounds is essential to analyze global carbon emissions and carbon flux, as well as to discover the contribution of climate change to the succession of forest ecosystems. The common recognition of firegrounds relies on remote sensing data, such as optical data, which have difficulty describing the characteristics of vertical structural damage to post-fire vegetation, whereas airborne LiDAR is incapable of large-scale observations and has high costs. The new generation of satellite-based photon counting radar ICESat-2/ATLAS (Advanced Topographic Laser Altimeter System, ATLAS) data has the advantages of large-scale observations and low cost. The ATLAS data were used in this study to extract three significant parameters, namely general, canopy, and topographical parameters, to construct a recognition index system for firegrounds based on vertical structure parameters, such as the essential canopy, based on machine learning of the random forest (RF) and extreme gradient boosting (XGBoost) classifiers. Furthermore, the spatio-temporal parameters are more accurate, and widespread use scalability was explored. The results show that the canopy type contributed 79% and 69% of the RF and XGBoost classifiers, respectively, which indicates the feasibility of using ICESat-2/ATLAS vertical structure parameters to identify firegrounds. The overall accuracy of the XGBoost classifier was slightly greater than that of the RF classifier according to 10-fold cross-validation, and all the evaluation metrics were greater than 0.8 after the independent sample test under different spatial and temporal conditions, implying the potential of ICESat-2/ATLAS for accurate fireground recognition. This study demonstrates the feasibility of ATLAS vertical structure parameters in identifying firegrounds and provides a novel and effective way to recognize firegrounds based on different spatial–temporal vertical structure information. This research reveals the feasibility of accurately identifying fireground based on parameters of ATLAS vertical structure by systematic analysis and comparison. It is also of practical significance for economical and effective precise recognition of large-scale firegrounds and contributes guidance for forest ecological restoration. Full article

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R²), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for c_dem, f_dem, f_ndvi, p_dem, and a_ndvi were Gaussian models, while those for h1_b7, h2_b7, h3_b7, and h4_b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with c_dem and p_dem from GEDI, and also showed an extremely significant correlation with a_ndvi, h1_b7, h2_b7, h3_b7, and h4_b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with f_dem and f_ndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R² = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 10⁷ Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus. Full article