Open research questions in Remote Sensing and LiDAR Applications
853 gap statements mined from Remote Sensing and LiDAR Applications papers in our 7.9M-paper local library, which holds 2,605 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
Future work could explore joint learning of geometric and texture features, perhaps by incorporating curvature, normal variance, or other geometric cues as complementary inputs when textures are incomplete. The generalization to different cities, sensor modalities, or seasonal variations remains to be tested.
TOTMSeg: A Texture-Aware Octree-Based Transformer-Mamba Framework for Large-Scale Urban Mesh Semantic Segmentation · 2026 · DOIThe lack of an explicit and repeatable inventory of TOF. The difficulty in distinguishing trees from other elements such as seasonal crops and shrubs. The need for a comprehensive assessment of TOF's ecological significance and its relationships with SDGs.
Beyond forest boundaries: trees outside forests, climate resilience and geospatial intelligence in Uttar Pradesh, India · 2026 · DOIFuture research should focus on standardising Forest Trait definitions and F-LUI indicators, quantifying and propagating uncertainties, distinguishing management effects from natural disturbances and environmental variability, and evaluating transferability across forest types, management regimes and spatial and temporal scales.
A Trait-Based Framework for Monitoring Forest Land Use Intensity with Remote Sensing: A Conceptual Review · 2026 · DOIFinally, an open question remains: could a very tall tree, located outside the LiDAR scan range, fall onto the road and create a safety hazard? This scenario is not currently addressed. However, a key limitation is its inability to detect hazards originating outside the VFV, such as a broken tree trunk that could roll onto the roadway.
Future work will address several limitations of the current model. A key focus is the integration of junctions and slip roads, which require distinct vegetation management strategies due to their complex geometries and traffic patterns. The framework will also be extended to non-highway roads, where varied layouts and vegetation profiles demand tailored solutions.
Future work should explore extending the workflow to multi-platform sensing configurations, embedding it within network-level optimization frameworks, and validating it across a wider range of forest types and terrain conditions.
Estimation of Cut and Fill and Removed Tree Volume for Forest Road Design Using Fused Aerial and Terrestrial Laser Scans: A Case Study from Oregon, USA · 2026 · DOIMobile Laser Scanning (MLS) offers a promising alternative through direct measurement of tree geometry; however, the influence of tree shape on MLS accuracy remains poorly understood.
Non-destructive tree volume estimation using mobile laser scanning: Impact of the tree shape on measurement error. · 2026 · DOITraditional global uniform screening removes a large number of tall-tree areas when tall-tree samples are scarce.
Adaptive Stratified Screening and Weakly Supervised UNet with Sparse ALS Refinement for Forest Canopy Height Mapping · 2026 · DOIExpanding field data collection efforts through collaborative initiatives, long-term ecological monitoring networks, and automated ground-based sensors should be explored to enhance our overall understanding of polar vegetation changes.
Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic: A Review of Satellite and UAV Remote Sensing, Machine Learning, and Sensor Fusion · 2025 · DOIIncorporating functional traits, such as productivity and disturbance derived from optical remote sensing indices, could be explored for future studies to investigate further improvement in classification accuracy. However, using standard LiDAR metrics that comprise only one source/dimension of LiDAR point clouds was insufficient to distinguish old-growth from second-growth forests.
Investigating LiDAR Metrics for Old-Growth Beech- and Spruce-Dominated Forest Identification in Central Europe · 2025 · DOITo overcome these limitations, future work will explore the applicability of ForestSplat to diverse forest types, including tropical rainforests, temperate forests, and coniferous woodlands, where differences in canopy density and structure may require adaptations in Gaussian Splatting parameters and multi-temporal data collection.
ForestSplat: Proof-of-Concept for a Scalable and High-Fidelity Forestry Mapping Tool Using 3D Gaussian Splatting · 2025 · DOIAlthough this study made significant progress in validating AGB prediction accuracy and elucidating influencing relationships, several issues remain that warrant further investigation.
Estimation of Forest Aboveground Biomass Using Sentinel-1/2 Synergized with Extrapolated Parameters from LiDAR Data and Analysis of Its Ecological Driving Factors · 2025 · DOIAlthough not quantified in this study, these factors are critical for evaluating the economic viability of UAV-based spraying in practical agricultural settings and warrant further investigation.
Future research should consider deep learning approaches, which can effectively capture non-linear relationships in multi-temporal data, enhancing the robustness against noise and missing observations.
When dealing with data with poor low signal-to-noise ratio (SNR), the denoising accuracy of the algorithm may be affected due to the difficulty in distinguishing signal photons from noise photons, so more robust noise detection mechanisms need to be explored in the future.
A Direction-Adaptive DBSCAN-Based Method for Denoising ICESat-2 Photon Point Clouds in Forested Environments · 2025 · DOIFuture research might consider more high-resolution validation datasets to estimate canopy cover accuracy, such as hemispherical photography and LiCOR gap fractions.
Using Unoccupied Aerial Systems (UAS) and Structure-from-Motion (SfM) to Measure Forest Canopy Cover and Individual Tree Height Metrics in Northern California Forests · 2025 · DOIThis study offers a robust method for calibrating FCH based on ZY3 stereo imagery, but it still has limitations: (1) this method mainly relies on high-resolution DEMs for generating high-precision FCH.
Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data · 2025 · DOIAlthough the deployment of quadruped robots in forestry is still a relatively nascent field, our experiments demonstrated remarkable robustness, confirming that the proposed approach is feasible with current technology. The hardware payload performed reliably, and the YOLO-based DBH detection models achieved the anticipated accuracy under diverse operational conditions.
On the other hand, the calibration performed in this work did not account for differences in backscattered energy Frontiers in Forests and Global Change 19 frontiersin.
Towards in-field live fuel moisture content estimation using multi-wavelength terrestrial laser scanning · 2026 · DOIFuture improvement would depend less on planting numbers and more on survival ratios, canopy establishment, ecological fitness and equitable socio-economic advantages - Digital technologies could facilitate carbon assessment and priority ranking for landscape…
Beyond forest boundaries: trees outside forests, climate resilience and geospatial intelligence in Uttar Pradesh, India · 2026 · DOIThe results of this study demonstrate that ICESat-2 photon observations contain detectable bathymetric information over Korean dam reservoirs, but they also reveal an important limitation in the practical availability of such information.
Potential and Limitations of ICESat-2 Photon-Level Bathymetry for Dam Reservoirs in South Korea · 2026 · DOISmall sample size of 29 plots, - Limited spatial coverage, - High operational costs of field inventories, - Potential for over-segmentation of large crowns or omission of understory individuals using region-growing segmentation…
Validation Rigor Determines Apparent Predictive Skill of UAV-LiDAR Carbon Models: A Cautionary Case Study in a Heterogeneous Tropical Savanna · 2026 · DOIInvestigating the performance of object-based approaches in heterogeneous tropical savannas, - Developing more robust segmentation algorithms for dense, multi-layered canopies, - Exploring the use of other remote sensing technologies for carbon…
Validation Rigor Determines Apparent Predictive Skill of UAV-LiDAR Carbon Models: A Cautionary Case Study in a Heterogeneous Tropical Savanna · 2026 · DOIInvestigating the application of the proposed framework in different regions or environments, - Evaluating the performance of the method using different types of input data or sensors, - Developing more advanced methods for anomaly detection…
Adaptive Stratified Screening and Weakly Supervised UNet with Sparse ALS Refinement for Forest Canopy Height Mapping · 2026 · DOIFuture work should focus on refining the initial detection stage to improve small tree recognition and enhance boundary definition for deciduous trees, potentially by incorporating texture features or spectral information…
Two-Stage Deep Learning Framework for Individual Tree Crown Detection and Delineation in Mixed-Wood Forests Using High-Resolution Light Detection and Ranging Data · 2025 · DOI
Most-cited papers in Remote Sensing and LiDAR Applications
- Lidar Remote Sensing for Ecosystem Studies · BioScience · 2002 · 1,391 citations
- Mapping global forest canopy height through integration of GEDI and Landsat data · Remote Sensing of Environment · 2020 · 1,373 citations
- The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography · Science of Remote Sensing · 2020 · 1,031 citations
- lidR: An R package for analysis of Airborne Laser Scanning (ALS) data · Remote Sensing of Environment · 2020 · 994 citations
- Spatial validation reveals poor predictive performance of large-scale ecological mapping models · Nature Communications · 2020 · 872 citations
- Terrestrial laser scanning in forest ecology: Expanding the horizon · Remote Sensing of Environment · 2020 · 529 citations
- Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation · Tropical Conservation Science · 2012 · 514 citations
- Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission · Remote Sensing of Environment · 2022 · 454 citations
- Applications in Remote Sensing to Forest Ecology and Management · One Earth · 2020 · 432 citations
- An unexpectedly large count of trees in the West African Sahara and Sahel · Nature · 2020 · 419 citations
Most recent work
- Spaceborne Canopy Height Products Should Be Complemented With Airborne Laser Scanning Data: Toward a European Canopy Height Model · Earth and Space Science · 2026
- Research progress on multimodal data fusion in forest resource monitoring · Frontiers in Plant Science · 2026
- Multispectral airborne laser scanning for tree species classification: A benchmark of machine learning and deep learning algorithms · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
- Roadside lidar-based scene understanding toward intelligent traffic perception: A comprehensive review · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
- Integration of Google’s Alpha Earth Foundations into biomass estimation combined with GEDI spaceborne lidar and field inventory data · Forest Ecology and Management · 2026
- InceptionFormer: A deep learning framework for UAV LiDAR point cloud completion to improve tree parameters estimation in dense forests · Remote Sensing of Environment · 2026
- Spatiotemporal variation in carbon sequestration in the forest ecosystem of Hainan Island over a 30-year period and its driving factors · Ecological Indicators · 2026
- AGI2P: Benchmarking Aerial–Ground Image-to-Point cloud localization with a large-scale dataset · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
- GFC2020: a global map of forest land use for year 2020 to support the EU Deforestation Regulation · Earth system science data · 2026
- Bridging forestry practice and remote sensing: scaling up forest composition with integrated UAV LiDAR and hyperspectral data · Journal of Forestry Research · 2026
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