Unmanned Aerial Vehicle Remote Sensing (UAV-RS) has emerged as a transformative technology in high-resolution Earth observation, with widespread applications in precision agriculture, ecological monitoring, and disaster response. However, a systematic understanding of its scientific evolution and structural bottlenecks remains lacking. This study collected 4985 peer-reviewed articles from the Web of Science Core Collection and conducted a comprehensive scientometric analysis using CiteSpace v.6.2.R4, Origin 2022, and Excel. We examined publication trends, country/institutional collaboration networks, keyword co-occurrence clusters, and emerging research fronts. Results reveal an exponential growth in UAV-RS research since 2015, dominated by application-driven studies. Hotspots include vegetation indices, structure from motion modeling, and deep learning integration. However, foundational challenges—such as platform endurance, sensor coordination, and data standardization—remain underexplored. The global collaboration network exhibits a “strong hubs, weak bridges” pattern, limiting transnational knowledge integration. This review highlights the imbalance between surface-level innovation and deep technological maturity and calls for a paradigm shift from fragmented application responses to integrated systems development. Our findings provide strategic insights for researchers, policymakers, and funding agencies to guide the next stage of UAV-RS evolution.

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate.

In karst mountainous tobacco fields, white plastic mulch, bare rock, and weeds are spatially interspersed and can affect semantic segmentation accuracy in UAV RGB imagery. This study constructed seven background-composition scenarios from UAV RGB orthomosaics acquired in Zhenfeng County, Guizhou Province, China, and evaluated DeepLabV3+, U-Net, U-Net++, and U-Net Former in terms of overall performance, scene-specific accuracy, and cross-scene stability. DeepLabV3+ achieved the highest validation mIoU (0.91) during hyperparameter selection, whereas U-Net performed best on the spatially independent test set, with an mIoU of 0.86, a tobacco-class IoU of 0.74, and an OA of 0.99. Under single-background conditions, bare rock yielded the highest IoU for all four models, while the relative effects of white plastic mulch and weeds were model-dependent. Composite-background effects were non-monotonic: adding weeds to bare-rock scenes reduced IoU by 16.89–28.98%, whereas the three-background scenario did not yield the lowest IoU. U-Net achieved the highest scene-wise IoU in all seven scenarios and the lowest cross-scene coefficient of variation (8.06%). This study provides a reference for sample optimization, model selection, and error diagnosis in complex mountainous tobacco plant identification.

Land-cover studies often compare feature configurations at one pixel size and report aggregate accuracy, leaving their behavior across aggregation supports, classes, and landscape strata incompletely characterized. We compared five pixel sizes (2, 5, 10, 15, and 30 m) derived from one pan-sharpened Gaofen-2 image in Huajiang Town, Guizhou Province, China. Random forests with eight combinations of spectral, index, texture, and terrain features were evaluated for seven classes using the same 1583 reference polygons and five repeated spatial-block partitions. The reported performance compares predictions for these polygons, not accuracy over all map pixels. Macro-F1 for spectral decreased from 70.59 ± 0.62% at 2 m to 62.47 ± 0.38% at 30 m; the standard deviations describe sensitivity to the repeated partitions. Averaged across the five pixel sizes, the index-, texture-, and terrain-augmented pipelines exceeded their separately screened spectral baselines by 1.68, 2.35, and 5.39 percentage points, respectively. These contrasts include changes in feature screening and do not isolate individual feature effects. Performance also varied across classes and terrain-zone sample groups. Within one image, one sampled region, and the specified aggregation and validation design, feature-pipeline contrasts varied across scale, class, and terrain-zone strata; external map accuracy and transferability remain to be tested with independent, support-matched reference data.