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Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides effective photon-counting light detection and ranging (LiDAR) data for estimating forest height across extensive geographical areas. Although prior studies have illustrated canopy conditions during leaf-on and leaf-off phases may influence ICESat-2 derived forest heights, a comprehensive understanding of this effect remains incomplete. This study seeks to comprehensively assess how varying canopy conditions (leaf-on/leaf-off) affect ICESat-2 forest height retrieval and modelling. First, the accuracies of ICESat-2 terrain and canopy heights under leaf-on and leaf-off conditions were validated. Second, random forest algorithm was utilized to model forest height by integrating ICESat-2, Sentinel-2, and other ancillary datasets. Finally, we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling. Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets. Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leaf-on conditions. Notably, the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover. Furthermore, the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples. [ABSTRACT FROM AUTHOR]

Accurate fractional crop-planting area (FCPA) mapping is a challenging task as it requires leveraging the advantages of geographic data in detailed spatial expression and agricultural statistics in the description of crop types and quantitative characteristics. We present a robust method to disaggregate the agricultural statistics within each county unit to 1-km scale grid by combining particle swarm optimization (PSO)-based feature selection with Random Forest (RF) regression, and an iterative area-gapallocation(IAGA) method is implemented to reconcile the discrepancies between the disaggregating results and statistics. The agriculture in Gansu Province, China, is characterized by complex heterogeneous smallholder farming landscapes. We tested this methodology in Gansu and explored the synergistic estimation of FCPA for six types of crops (i.e. wheat, maize, oil-bearing, vegetable,orchards, and other crops) in 2010. The results showed that the derived FCPA maps matched well with the statistics in terms of quantity, while also providing spatial details. The quantitative evaluation results indicated that the derived FCPA had good accuracy,with a higher R2 above 0.97, a lower RMSE below 1%, and an absolute error between 1.53-5.24%. The proposed methodology provides valuable insights for practical large-scale FCPA mapping at a high spatial resolution in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Remote sensing and land resource surveys have been used in recent decades for land use/land cover (LULC) mapping; however, keeping the developed LULC up-to-date and consistent with land survey statistics remains challenging. This study developed a practical and effective framework to automatically update existing LULC products and bridge the gap between remote sensing classification results and land survey data. This study employed Landsat imagery time series, change detection algorithms, sample migration, and random forests to develop a framework for updating existing LULC products in China from 1980–2015 to 1980–2022. The updated LULC maps reflect the post-2015 LULC changes well and maintain continuity with the pre-2015 products. Additionally, a statistical space allocation method based on the minimum cross-entropy strategy was proposed to optimize the LULC maps, increasing the correlation coefficient (r) with China's second and third national land survey statistics from 0.41–0.89 to 0.86–0.99. Thus, the framework and products developed in this study provide valuable tools for sustainable land use and policy planning. [ABSTRACT FROM AUTHOR]

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) can collect earth surface elevation data with high precision on a global scale. However, the collected photon data contains a large amount of background noise due to the influence of sunlight, cloud reflection, and other factors. For photon data of different scenes, how to effectively denoise and achieve accurate classification of photon point clouds is crucial for subsequent applications. This study proposes a random forest based method for denoising and classifying ICESat-2 photon data in urban areas by fusing spectral features from Sentinel-2 images and spatial distribution features from photon data. The experimental results show that the method can effectively identify various types of photons. Compared with the reference data, the overall accuracy of photon denoising and classification is 95.97% on average, and the average kappa coefficient is 94.18%. Further analysis demonstrates that the addition of sentinel-2 spectral information can effectively improve the classification accuracy of photon point clouds in urban areas, and the photon classification method of combining photon lidar data and optical images can be a promising solution to improve classification accuracy. [ABSTRACT FROM AUTHOR]

The extraction of large dam candidate regions is critical for broad-scale efforts to rapidly detect large-area dams. The framework proposed in this paper attempts to combine random forest classification models and spatial analysis methods with large dam candidate area extraction methods for large-scale areas. First, we studied the combination of optical, microwave, texture, and topographic features of the dam and constructed a multisource remote sensing and topographic feature vector of the dam. Secondly, we constructed random forest classifiers in different study areas and evaluate their performance. Then we explored the geographic characteristics of the dams and their relationships with other features. Finally, we introduced the spatial analysis method to constrain the large dam candidate area. The proposed framework was tested in a total area of 968,533 km2 in five countries and achieved promising results, which constrained the candidate area to less than 1.06% of the total area. We calculated the completeness rate of large dams using the multi-source dam datasets. The framework achieved a completeness rate of more than 97.62%. Our results show that the entire framework is reliable for automated and fast large dam candidate area acquisition based on data from open remote sensing products. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a model, called the Daily Downward Shortwave Radiation Random Forest Model over Rugged Terrain (DSRMT), to accurately calculate the downward shortwave radiation over a terrain without bright surface on clear days at a daily scale (DSRdaily−rugged). It was built by using the random forest method based on the comprehensive samples from CERES4_SYN1deg_Ed4A within 17 typical mountainous regions. DSRMT could directly estimate DSRdaily-rugged from the instantaneous direct and diffuse solar radiation on a flat surface during 10:30–14:30hrs on each day by comparing with the terrain factors from a digital elevation model, broadband albedo from the Global Land Surface Satellite, and ancillary information. The in-situ validation results showed that it generally delivered superior performance in estimating DSRdaily-rugged at any time during 10:30–14:30hrs, especially at noon, yielding a validated root mean-squared error (RMSE) of 24.90–29.22 Wm−2 and mean absolute error (MAE) of 19.16–22.94 Wm−2, and the average weighted DSRdaily-rugged were usually more accurate with the RMSE and MAE of 21.63 and 17.14 Wm−2. Overall, DSRMT was found to deliver satisfactory performance because of its high accuracy, robustness, ease of implementation, and efficiency, so it has the strong potential to be widely used in practice. [ABSTRACT FROM AUTHOR]

Urban landscape forms can be effective in reducing increasing PM2.5 concentrations due to urbanization in China, making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM2.5 variations. Three landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020, using a spatiotemporal geographically weighted regression model, random forest models and partial dependence plots. The results show that there are spatiotemporal differences in the impacts of landscape indices on PM2.5. the proportion of urban green infrastructure (PLAND-UGI) and the fractal dimension of urban green infrastructure (FRACT-UGI) exacerbate PM2.5 concentrations in the northwest, the proportion of impervious surfaces (PLAND-Impervious) mitigates air pollution in northwest and southwest China, and shannon's diversity index (SHDI) has seasonal differences in the northwest. PLAND-UGI is the landscape index with the largest contribution (30%) and interpretable range. The relationship between FRACT and PM2.5 was more complex than for other landscape indices. The results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM2.5, contributing to clean urban development and sustainable development. [ABSTRACT FROM AUTHOR]

Crown fire damage is a mixture of three principal fire-related components: charred material, scorched foliage, and unaltered green canopy. This study estimated the abundance of these physical alterations in two immediate post-fire Mediterranean forest contexts (Portugal and Italy) by applying linear spectral mixture analysis (LSMA) on Sentinel-2 imagery. The tree crowns fire damage was subsequently mapped, integrating fractional abundance information in a random forest (RF) algorithm, comparing the accuracy resulting from the adoption of generic or image spectral libraries as the primary investigative goal. Although image-derived endmembers resulted in more effectiveness in terms of fire-related components abundance quantification (LMSA-derived RMSE < 0.1), the F-scores always were ≥ 90% whether generic endmembers or image endmembers derived information was employed. The environmental heterogeneity of the two study areas affected the fire severity gradients, with a prevalence of the charred (PT) (45–46%) and green class (IT) (44–53%). Post-fire temporal monitoring was initialized by applying the proposed strategies, and the preliminary results showed a positive recovery trend in forest vegetation from the first year following the fire event, with a reduced charcoal predominance and an increasing proportion of green components. [ABSTRACT FROM AUTHOR]

Wetlands provide vital ecological services for both humans and environment, necessitating continuous, refined and up-to-date mapping of wetlands for conservation and management. In this study, we developed an automated and refined wetland mapping framework integrating training sample migration method, supervised machine learning and knowledge-driven rules using Google Earth Engine (GEE) platform and open-source geospatial tools. We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland (DLW) during 2015–2021. First, the continuous change detection (CCD) algorithm was utilized to migrate stable training samples. Then, annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples. Ultimately, annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories, with Overall Accuracy (OA) ranging from 81.82% (2015) to 93.84% (2020) and Kappa Coefficient (KC) between 0.73 (2015) and 0.91 (2020), demonstrating satisfactory performance and substantial potential for accurate, timely and type-refined wetland mapping. Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring, conservation and sustainable development of wetland ecosystem. [ABSTRACT FROM AUTHOR]

In recent years, past changes in global and regional land surface temperatures (LST) have been well studied, however, future LST changes have been largely ignored owing to data limitations. In this study, three climate variables of CMIP6, namely air temperature (AT), precipitation (Pre), and leaf area index (LAI), were spatially corrected using the Delta downscaling method. On this basis, by combining MODIS LST, elevation, slope and aspect, a random forest (RF) model was built to calculate the LST from 2022 to 2100. The absolute variability (AV) and Mann-Kendall (M-K) tests were used to quantitatively detect interannual and seasonal LST changes in different Shared Socioeconomic Pathways (SSPs) scenarios. The results showed that the AV value increased successively from SSP1-2.6 to SSP2-4.5 and then to SSP5-8.5. Compared with the base period (2003-2021), the increment in interannual, spring, summer and autumn LST during 2022–2100 was mainly between 1 and 2 °C under three scenarios. The interannual and seasonal LST were spatially characterized by significant warming over large areas, and the increasing was the fastest under SSP5-8.5. These results indicate that, in the future, LST will increase further over large areas, especially in winter. [ABSTRACT FROM AUTHOR]

To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges. One of them is the need for high-quality training samples. This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space. We migrated training samples collected for Washington, D. C. in 1979 to classify the city's land covers in 1982 and 1984. The classifier trained with Washington, D. C.'s samples were used in classifying Boston's and Tokyo's land covers. The results showed that the overall accuracies achieved using migrated samples in 1982 (66.67%) and 1984 (65.67%) for Washington, D. C. were comparable to that of 1979 (68.67%) using a random forest classifier. Migration of training samples between cities in the same urban ecoregion, i.e. Washington, D. C., and Boston, achieved higher overall accuracy (59.33%) than cities in the different ecoregions (Tokyo, 50.33%). We concluded that migrating training samples across time and space in the same urban ecoregion are feasible. Our findings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale. [ABSTRACT FROM AUTHOR]

The high-temporal-resolution monitoring of key management nodes in cotton management via agricultural remote sensing is vital for field cotton macro-statistics, particularly for predicting cotton production and obtaining comprehensive data. This study examines Shihezi, Xinjiang as a case study, utilizing Sentinel-1 and Sentinel-2 data from 2019 to 2021. Three machine learning models(RF, SVM, and CART) were employed to extract annual crop classification area rasters, monitor weekly cultivation progress, and monitor abandoned cropland during the cultivation period. The results demonstrate that the random forest model has produced satisfactory results in gridded extraction for cotton classification areas, achieving the producer's accuracy of the cotton category reached 98.5%, and the kappa coefficient is 0.947. Cotton cultivated in 2021 began is a week later than in 2020, yet exhibited a faster cultivate speed. The proportion of abandoned cotton fields in the study area rose in 2020 compared to 2019. The methodology presented in this study has a certain reference value for exploring the monitoring of continuous changes in crops over the years and macro-monitoring of important activities in the entire growth cycle. [ABSTRACT FROM AUTHOR]

Satellite images are widely used for crop yield estimation, but their coarse spatial resolution means that they often fail to provide detailed information at the field scale. Recently, a new generation of high-resolution satellites and CubeSat platforms has been launched. In this study, satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables, and the improvement in wheat yield estimation was evaluated. Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression. Nine vegetation indices (NDVI, GNDVI, MSAVI2, MTVI2, MTCI, reNDVI, SAVI, EVI and WDVI) and spectral bands were tested. During the model training, the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data. However, combining environmental data with the PlanetScope data realized the highest estimation accuracy. For the validated models, adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy, and the results were slightly better with the Sentinel-2 data than with the PlanetScope data. Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing. [ABSTRACT FROM AUTHOR]

The selection of discretization criteria and interval numbers of landslide-related environmental factors generally fails to quantitatively determine or filter, resulting in uncertainties and limitations in the performance of machine learning (ML) methods for landslide susceptibility mapping (LSM). The aim of this study is to propose a robust discretization criterion (RDC) to quantify and explore the uncertainty and subjectivity of different discretization methods. The RDC consists of two steps: raw classification dataset generation and optimal dataset extraction. To evaluate the robustness of the proposed RDC method, Lushan County of Sichuan Province in China was chosen as the study area to generate the LSM based on three datasets (optimal dataset, original dataset with continuous values, and statistical dataset) using three popular ML methods, namely, convolution neural network, random forest, and logistic regression. The results show that the areas under the receiver operating characteristic curve (AUCs) of the optimal dataset for the abovementioned ML models are 0.963, 0.961, and 0.930 which are higher than those of the original dataset (0.938, 0.947, and 0.900) and statistical dataset (0.948, 0.954, and 0.897). In conclusion, the RDC method can extract the more representative features from environmental factors and outperform the other conventional discretization methods. [ABSTRACT FROM AUTHOR]

Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets. [ABSTRACT FROM AUTHOR]

Urban Functional Zone (UFZ) identification is vital for urban planning, renewal, and development. Point of Interest (POI), as one of the most popular data in UFZ studies, is transformed into a geo-corpus under specific sampling strategies, which can be used with Natural Language Processing (NLP) technology to extract geo-semantic features and identify UFZs. However, existing studies only capture a single spatial distribution pattern of POIs, while ignoring the other spatial distribution information. In this paper, we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings. Subsequently, random forest model was leveraged to classify UFZs based on those embeddings. A set of combination experiments were designed for performance validation. The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%, with an improvement of 8.5% compared to the baseline methods. The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs. [ABSTRACT FROM AUTHOR]

Snow and cloud discrimination is a main factor contributing to errors in satellite-based snow cover. To address the error, satellite-based snow cover performs snow reclassification tests on the cloud pixels of the cloud mask, but the error still remains. Machine Learning (ML) has recently been applied to remote sensing to calculate satellite-based meteorological data, and its utility has been demonstrated. In this study, snow and cloud discrimination errors were analyzed for GK-2A/AMI snow cover, and ML models (Random Forest and Deep Neural Network) were applied to accurately distinguish snow and clouds. The ML-based snow reclassified was integrated with the GK-2A/AMI snow cover through post-processing. We used the S-NPP/VIIRS snow cover and ASOS in situ snow observation data, which are satellite-based snow cover and ground truth data, as validation data to evaluate whether the snow/cloud discrimination is improved. The ML-based integrated snow cover detected 33–53% more snow compared to the GK-2A/AMI snow cover. In terms of performance, the F1-score and overall accuracy of the GK-2A/AMI snow cover was 73.06% and 89.99%, respectively, and those of the integrated snow cover were 76.78–78.28% and 90.93–91.26%, respectively. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

The normalization of LST relative to environmental parameters is of great importance in various environmental applications. The purpose of this study was to develop a new approach for LST normalization relative to environmental variables. These included topographic variables (i.e. solar irradiance and near-surface temperature lapse rate (NSTLR)) as well as biophysical properties (i.e. vegetation, wetness, and albedo). The study was conducted in two phases, namely (1) using global and (2) local optimization strategies to calculate the regression coefficients of environmental variables in the partial least squares regression (PLSR) and build the non-linear linking model in the random forest regression (RFR). The RMSEs between actual LST and modeled LST based on the global and local optimization strategies using PLSR (RFR) were 2.202 (0.935) and 0.939 (0.835) °C, respectively. The results showed that RFR had higher efficiency than PLSR in normalizing LST. Moreover, the local optimization method outperformed the global optimization method in terms of normalization accuracy. The results of this study could be very useful in many environmental applications such as identifying thermal anomalies, and surface anthropogenic heat island modeling. [ABSTRACT FROM AUTHOR]

The extensive use of greenhouses has brought soared economic benefits for farming practitioners in China and an overview of the spatio-temporal distribution of greenhouses is of great interest to agricultural practitioners and decision-makers. In this study, Landsat image based greenhouse maps in Guanzhong Plain, Shaanxi, China were made using random forest classification algorithm through visual interpretation on the Google Earth Engine. The 7-year's changes in greenhouse areas were investigated (i.e. 2000, 2003, 2006, 2010, 2013, 2015 and 2019) with yearly overall accuracy more than 90%. The results showed that the total area of greenhouses in Guanzhong Plain demonstrated an increasing trend, from 5.92 km2 in 2000 to 194.42 km2 in 2019 with a considerable growth between 2010 and 2015. The dominant drivers for the increase are largely attributed to the government policy as well as economic profitability. The distribution of greenhouse shifts to central and eastern regions of Guanzhong Plain. Greenhouses preferentially expand to the area near to rural roads, main rivers, and high elevation, with more than 45% greenhouses distributed within 1 km of the county rural road. The principal component analysis based suitability evaluation showed that a total of 38.44% of the area was suitable for greenhouse. [ABSTRACT FROM AUTHOR]

This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent variables including altitude, slope, and distance from barren, crop, greenery, roads, and urban areas for 1998, 2008, and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately, independent from applying a cellular automata (CA) model. Therefore, the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely. [ABSTRACT FROM AUTHOR]

The Upper Guinean Forest region of West Africa, a globally significant biodiversity hotspot, is among the driest and most human-impacted tropical ecosystems. We used Landsat to study forest degradation, loss, and recovery in the forest reserves of Ghana from 2003 to 2019. Annual canopy cover maps were generated using random forests and results were temporally segmented using the LandTrendr algorithm. Canopy cover was predicted with a predicted-observed r2 of 0.76, mean absolute error of 12.8%, and mean error of 1.3%. Forest degradation, loss, and recovery were identified as transitions between closed (>60% cover), open (15–60% cover) and low tree cover (< 15% cover) classes. Change was relatively slow from 2003 to 2015, but there was more disturbance than recovery resulting in a gradual decline in closed canopy forests. In 2016, widespread fires associated with El Niño drought caused forest loss and degradation across more than 12% of the moist semi-deciduous and upland evergreen forest types. The workflow was implemented in Google Earth Engine, allowing stakeholders to visualize the results and download summaries. Information about historical disturbances will help to prioritize locations for future studies and target forest protection and restoration activities aimed at increasing resilience. [ABSTRACT FROM AUTHOR]

Monitoring and classifying disturbed forests can provide information support for not only sustainable forest management but also global carbon sequestration assessments. In this study, we propose an autoencoder-based model for forest disturbance detection, which considers disturbances as anomalous events in forest temporal trajectories. The autoencoder network is established and trained to reconstruct intact forest trajectories. Then, the disturbance detection threshold is derived by Tukey's method based on the reconstruction error of the intact forest trajectory. The assessment result shows that the model using the NBR time series performs better than the NDVI-based model, with an overall accuracy of 90.3%. The omission and commission errors of disturbed forest are 7% and 12%, respectively. Additionally, the trained NBR-based model is implemented in two test areas, with overall accuracies of 87.2% and 86.1%, indicating the robustness and scalability of the model. Moreover, comparing three common methods, the proposed model performs better, especially for intact forest detection accuracy. This study provides a novel and robust approach with acceptable accuracy for forest disturbance detection, enabling forest disturbance to be identified in regions with limited disturbance reference data. [ABSTRACT FROM AUTHOR]