Subsidence Characteristics in North Anhui Coal Mining Areas Using Space-Air-Ground Collaborative Observations.

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space-air-ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area.

Improved estimation of aboveground biomass of regional coniferous forests integrating UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries.

BACKGROUND: Forest aboveground biomass (AGB) is not only the basis for estimating forest carbon storage, but also an important parameter for evaluating forest carbon cycle contribution and forest ecological function. Data saturation and fewer field plots limit the accuracy of AGB estimation. In response to these questions, we constructed a point-line-polygon framework for regional coniferous forests AGB mapping using field survey data, UAV-LiDAR strip data, Sentinel-1 and Sentinel-2 imageries in this study. Under this framework, we explored the feasibility of acquiring the LiDAR sampling plots using the LiDAR sampling strategy consistent with the field survey, and analyzed the potentials of multi-scale wavelet transform (WT) textures and tree species stratification for improving AGB estimation accuracy of coniferous forests in North China. RESULTS: The results showed that UAV-LiDAR strip data of high density point clouds could be used as a sampling tool to achieve sample amplification. Experimental comparison results showed that the Sentinel-based AGB estimation models incorporating the multi-scale WT textures and SAR data performed better, and the model based on coniferous forests tree species significantly improved the performance of AGB estimation. Additionally, the accuracy comparison using different validation sets indicated that the proposed LiDAR sampling strategy under the point-line-polygon framework was suitable for estimating coniferous forests AGB on a large area. The highest accuracy of AGB estimation of larch, Chinese pine and all coniferous forests was 74.55%, 78.96%, and 73.42%, respectively. CONCLUSIONS: The proposed approach can successfully alleviate the data signal saturation issue and accurately produce a large-scale wall-to-wall high-resolution AGB map by integrating optical and SAR data with a relative small number of field plots.

Correction of UAV LiDAR-derived grassland canopy height based on scan angle.

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25-65cm) and segmented (25-40cm, 40-50cm and 50-65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

Top Canopy Height and Stem Size Variation Enhance Aboveground Biomass across Spatial Scales in Seasonal Tropical Forests.

Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing.

UAV-borne LiDAR revolutionizing groundwater level mapping.

In hydrogeological research, the systematic and periodic measurement of the piezometric level is fundamental to assess aquifer storage, identify recharge and discharge areas, define flow directions and to infer the balance between inputs and withdrawals. Furthermore, knowledge of this variable and its fluctuations is essential for the efficient management and protection of groundwater resources. In this work, a novel methodology is proposed for the remote acquisition of piezometric information from traditional large-diameter wells, using drone-borne LiDAR observations. The workflow developed consists of different stages, from flight planning and parameter setting, to point cloud generation, data processing and validation and its statistical treatment to extract piezometric information. This methodology has been applied in a small coastal aquifer with numerous wells that have served as monitoring points. The UAV-LiDAR has enabled the straightforward obtention of measurements of the piezometric level with very high vertical accuracies (RMSE of 5 cm) with minimum and maximum residuals of -8.7 and 7.9 cm respectively. Likewise, the method has shown vertical accuracies 3 times better than those inferred from the official DTM of best resolution available in Spain, which is usually used in hydrogeological works. Since the technique provides absolute values of the piezometric level, it eliminates the need for laborious levelling work prior to hydrogeological campaigns. This method has proved to be an effective alternative/complementary technique to traditional measurements of the piezometric level, allowing to monitor extensive or inaccessible areas over short periods of time and to potentially reduce gaps in hydrogeological databases.

Assessment of DSM Based on Radiometric Transformation of UAV Data.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

Modeling Aboveground Biomass in Hulunber Grassland Ecosystem by Using Unmanned Aerial Vehicle Discrete Lidar.

Accurate canopy structure datasets, including canopy height and fractional cover, are required to monitor aboveground biomass as well as to provide validation data for satellite remote sensing products. In this study, the ability of an unmanned aerial vehicle (UAV) discrete light detection and ranging (lidar) was investigated for modeling both the canopy height and fractional cover in Hulunber grassland ecosystem. The extracted mean canopy height, maximum canopy height, and fractional cover were used to estimate the aboveground biomass. The influences of flight height on lidar estimates were also analyzed. The main findings are: (1) the lidar-derived mean canopy height is the most reasonable predictor of aboveground biomass (R² = 0.340, root-mean-square error (RMSE) = 81.89 g·m-2, and relative error of 14.1%). The improvement of multiple regressions to the R² and RMSE values is unobvious when adding fractional cover in the regression since the correlation between mean canopy height and fractional cover is high; (2) Flight height has a pronounced effect on the derived fractional cover and details of the lidar data, but the effect is insignificant on the derived canopy height when the flight height is within the range (<100 m). These findings are helpful for modeling stable regressions to estimate grassland biomass using lidar returns.