Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.

This paper presents methods and results of small-area population estimation using a combined Light Detection And Ranging (LiDAR), Landsat Thematic Mapper (TM) and parcel dataset for a study area in Denton, Texas, USA. A normalized digital surface model (nDSM) was created from a digital surface model (DSM) and a digital elevation model (DEM) built from LiDAR point data. Residential and commercial parcels were selected from parcel data and used as a mask to remove non-residential and non-commercial pixels from the nDSM. Classification results of residential areas from Landsat TM images acquired on two dates were used to further refine the nDSM. Using continuous and random census blocks as samples, building count, building area and building volume were calculated from the nDSM through mathematical morphological operations, zonal statistics, data conversion and spatial joining in a geographic information system (GIS). Combined with census 2000 data, a total of 10 ordinary least squares (OLS) regression models and geographically weighted regression (GWR) models were built and applied to the census blocks in the study area. Finally, accuracy assessments were carried out. The results show that the sign and magnitude of the relative estimation errors at the census-block level lead to underestimation of the total population in the study area. Possible reasons for the relatively low accuracies and problems for further investigation are also discussed.

The complexity of urban areas makes it difficult for single-source remotely sensed data to meet all urban application requirements. Airborne light detection and ranging (lidar) can provide precise horizontal and vertical point cloud data, while hyperspectral images can provide hundreds of narrow spectral bands which are sensitive to subtle differences in surface materials. The main objectives of this study are to explore: (1) the performance of fused lidar and hyperspectral data for urban land-use classification, especially the contribution of lidar intensity and height information for land-use classification in shadow areas; and (2) the efficiency of combined pixel- and object-based classifiers for urban land-use classification. Support vector machine (SVM), maximum likelihood classification (MLC), and object-based classifiers were used to classify lidar, hyperspectral data and their derived features, such as the normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), and texture measures, into 15 urban land-use classes. Spatial attributes and rules were used to minimize misclassification of the objects showing similar spectral properties, and accuracy assessments were carried out for the classification results. Compared with hyperspectral data alone, hyperspectral–lidar data fusion improved overall accuracy by 6.8% (from 81.7 to 88.5%) when the SVM classifier was used. Meanwhile, compared with SVM alone, the combined SVM and object-based method improved OA by 7.1% (from 87.6 to 94.7%). The results suggest that hyperspectral–lidar data fusion is effective for urban land-use classification, and the proposed combined pixel- and object-based classifiers are very efficient and flexible for the fusion of hyperspectral and lidar data.

While numerous studies have investigated the NDVI-LST relationship at local or regional scales, existing global analyses are outdated and fail to incorporate recent environmental changes driven by climate change and human activity. This study aims to address this gap by conducting an extensive global analysis of NDVI-LST correlations from 2000 to 2024, utilizing multi-source satellite data to assess latitudinal and ecosystem-specific variability. The MODIS dataset, which provides global daily LST data at a 1 km resolution from 2000 to 2024, was used alongside MODIS-derived NDVI data, which offers global vegetation indices at a 1 km resolution and 16-day temporal intervals. A correlation analysis was performed by extracting NDVI and LST values for each raster cell. The analysis revealed significant negative correlations in regions such as the western United States, Brazil, southern Africa, and northern Australia, where increased temperatures suppress vegetation activity. A total of 38,281,647 pixels, or 20% of the global map, exhibited statistically significant correlations, with 80.4% showing negative correlations, indicating a reduction in vegetation activity as temperatures rise. The latitudinal distribution of significant correlations revealed two prominent peaks: one in the tropical and subtropical regions of the Southern Hemisphere and another in the temperate zones of the Northern Hemisphere. This study uncovers notable spatial and latitudinal patterns in the LST-NDVI relationship, with most regions exhibiting negative correlations, underscoring the cooling effects of vegetation. These findings emphasize the crucial role of vegetation in regulating surface temperatures, providing valuable insights into ecosystem health, and informing conservation strategies in response to climate change.

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.