Digital Surface Research Articles

Near-Time Digital Mapping for Geoforensic Searches

Inadequate base maps with poor scale or low resolution demonstrate the need for contemporary topographic maps when conducting geological mapping. In neotectonic regimes and areas of dynamic geomorphology, archival or large-scale maps require time-consuming, on-site manual updating while mapping bedrock and superficial geology. In contrast, stable ground conditions may have suitable legacy maps in some locations but not in others, such as where surveying is absent, incomplete or subject to legal restrictions. The geologist tasked with mapping may have to do this on short notice at their first site visit with no time to search for or create digital or physical copies of background maps on a suitable scale. The field mapper may encounter any of the above scenarios, especially the Geoforensic specialist tasked with Search and Rescue, hazard assessment or preparation and desktop study for subsequent search teams or law enforcement. Drone-derived orthoimagery and digital surface modelling can be conducted on-site in near real time to provide high resolution georeferenced maps for direct input of geological information, thus bypassing either non-existent or unsuitable base maps.

MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fusion-based models have garnered considerable attention, exhibiting superior segmentation performance when compared with traditional single-modal techniques. Nonetheless, the majority of these multi-modal models, which rely on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for feature fusion, face limitations in terms of remote modeling capabilities or computational complexity. This paper presents a novel Mamba-based multi-modal fusion network called MFMamba for semantic segmentation of remote sensing images. Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). To capitalize on the distinct attributes of the multi-modal remote sensing data from both branches, a feature fusion block (FFB) is designed to synergistically enhance and integrate the features extracted from the dual-branch structure at each stage. Extensive experiments on the Vaihingen and the Potsdam datasets have verified the effectiveness and superiority of MFMamba in semantic segmentation of remote sensing images. Compared with state-of-the-art methods, MFMamba achieves higher overall accuracy (OA) and a higher mean F1 score (mF1) and mean intersection over union (mIoU), while maintaining low computational complexity.

Terrestrial Photogrammetry–GIS Methodology for Measuring Rill Erosion at the Sparacia Experimental Area, Sicily

Rill erosion is a major issue on a global scale, and predicting the presence, position, and development of erosive forms on hillslopes is a significant challenge for the scientific community. Several plot-scale investigations confirmed the reliability of the terrestrial photogrammetric (TP) technique for studying rill erosion and the reliability of a method for extracting the rill network from Digital Surface Models (DSMs) and measuring the corresponding volume. In this paper, for an intense erosive event that occurred at the Sparacia experimental area (Sicily, Southern Italy), TP surveys of three plots, with different length and steepness, incised by rills, were performed to reconstruct the DSMs. For each plot, the rill network was extracted from the DSMs, and the non-contributing network was distinguished from the contributing one, from which the soil loss and the consequent eroded volumes V were determined. The specific aims were to (i) establish the effect of plot steepness on rill depths and some morphometric characteristics of the drainage rill network; (ii) test and calibrate the relationship between V and the total rill length L, using all rill measurements available in the literature and those obtained in this study; and (iii) modify the V–L relationship by including climate forcing and assessing the related performance. The rill depths, h, the drainage frequency, and drainage density of the rill networks detected in the three plots were compared. The analysis demonstrated that h and the morphometric parameters of the contributing rill network increase with plot steepness s. In particular, the mean depth increases from 2.79 to 4.85 cm for slope increasing from 14.9 to 26%. Moreover, the drainage frequency of the contributing rill network varies from 0.16 m−2 for s = 14.9% to 0.47 m−2 for s = 26%, while the drainage density of the contributing rill network varies from 0.92 m−1 for s = 14.9% to 2.1 m−1 for s = 26%. Finally, using the data available in the literature and those obtained in this investigation, an empirical relationship between V and the total rill length L was firstly tested and then rearranged considering the event rainfall erosivity Re. Including Re in the rearranged equation guaranteed the best performance in V estimation.

High-resolution topography reveals morphological changes of Stromboli volcano following the July 2024 eruption.

The July 2024 eruption of Stromboli volcano has been characterised by the manifestation, at variable intensity, of the entire repertoire of volcanic events that Stromboli volcano is capable of, and is by far the one that has most changed the morphology of the crater terrace and of the Sciara del Fuoco slopein the last decades. We present the results of an Unoccupied Aircraft System (UAS) survey performed on 14 July 2024 and consisting of 4,988 visible and thermal photographs coupled with Structure-from-Motion photogrammetry that allowed us to produce a high-resolution (0.2 m/pixel) Digital Surface Model (DSM). We documented the profound morphological changes of the Stromboli volcano resulting from the 4-11 July 2024 eruption and obtained elevation and volume change estimates by differencing our survey and a UAS-derived pre-event surface (24 May 2024).

Digital mapping and surface modeling of historical objects using modern visualization techniques

Digital mapping and surface modeling of historical objects using modern visualization techniques

Unmanned Aerial Vehicle-Neural Radiance Field (UAV-NeRF): Learning Multiview Drone Three-Dimensional Reconstruction with Neural Radiance Field

In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene’s structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.

DSM Quality Assessment of GF-7 Satellite

Abstract. The Gaofen-7 (GF-7) satellite was launched on November 3, 2019. It can achieve 1:10000 scale stereo mapping and serve the application needs of basic surveying and mapping, natural resources monitoring and geographic information resource construction. These application rely on high-precision digital surface model (DSM) products as data support. Therefore, it is of great significance to analyze the quality of DSM products produced by Gaofen-7. This paper uses GF-7 optical stereo images from the regions of Zhaodong and Tianjin in China to extract DSMs. And the difference DSM is produced by subtracting the 0.5m resolution aerial reference DEM and the 30m resolution COP30 product in the same area. The quality of DSM products is evaluated by comparing and analyzing the difference DSM. The results show that when the DSM produced by the GF-7 satellite uses the aerial DEM as a reference, the elevation accuracy can reach 0.97 m in the Zhaodong area and 1.54 m in the Tianjin area, and the details of the objects are well depicted; when the COP30 product is used as a reference, the elevation positioning accuracy can reach 1.29 m in the Zhaodong area and 2.42 m in the Tianjin area, which can correctly show the overall situation of the surface objects. Among them, the elevation accuracy in the Tianjin area is relatively poor, mainly because there are many water areas and buildings in this area, and the COP30 product has data missing in this area, which affects the accuracy. In general, the quality of the DSM products generated by the GF-7 satellite is good. Through effective ground control and further processing, the DSM products can fully meet the needs of practical applications such as 1:10000 scale mapping.

Thwaites Glacier thins and retreats fastest where ice-shelf channels intersect its grounding zone

Abstract. Antarctic ice shelves buttress the flow of the ice sheet but are vulnerable to increased basal melting from contact with a warming ocean and increased mass loss from calving due to changing flow patterns. Channels and similar features at the bases of ice shelves have been linked to enhanced basal melting and observed to intersect the grounding zone, where the greatest melt rates are often observed. The ice shelf of Thwaites Glacier is especially vulnerable to basal melt and grounding zone retreat because the glacier has a retrograde bed leading to a deep trough below the grounded ice sheet. We use digital surface models from 2010–2022 to investigate the evolution of its ice-shelf channels, grounding zone position, and the interactions between them. We find that the highest sustained rates of grounding zone retreat (up to 0.7 km yr−1) are associated with high basal melt rates (up to ∼250 m yr−1) and are found where ice-shelf channels intersect the grounding zone, especially atop steep local retrograde slopes where subglacial channel discharge is expected. We find no areas with sustained grounding zone advance, although some secular retreat was distal from ice-shelf channels. Pinpointing other locations with similar risk factors could focus assessments of vulnerability to grounding zone retreat.

A new strategy based on multi-source remote sensing data for improving the accuracy of land use/cover change classification.

Land Use/Cover Change (LUCC) plays a crucial role in sustainable land management and regional planning. However, contemporary feature extraction approaches often prove inefficient at capturing critical data features, thereby complicating land cover categorization. In this research, we introduce a new feature extraction algorithm alongside a Segmented and Stratified Principal Component Analysis (SS-PCA) dimensionality reduction method based on correlation grouping. These methods are applied to UAV LiDAR and UAV HSI data collected from land use types (e.g., residential areas, agricultural lands) and specific species (e.g., tree species) in urban, agricultural, and natural environments to reflect the diversity of the study area and to demonstrate the ability of our methods to be applied in different classification scenarios. We utilize LiDAR and HSI data to extract 157 features, including intensity, height, Normalized Digital Surface Model (nDSM), spectral, texture, and index features, to identify the optimal feature subset. Subsequently, the best feature subset is inputted into a random forest classifier to classify the features. Our findings demonstrate that the SS-PCA method successfully enhances downscaled feature bands, reduces hyperspectral data noise, and improves classification accuracy (Overall Accuracy = 91.17%). Additionally, the CFW method effectively screens appropriate features, thereby increasing classification accuracy for LiDAR (Overall Accuracy = 78.10%), HIS (Overall Accuracy = 89.87%), and LiDAR + HIS (Overall Accuracy = 97.17%) data across various areas. Moreover, the integration of LiDAR and HSI data holds promise for significantly improving ground fine classification accuracy while mitigating issues such as the 'salt and pepper noise'. Furthermore, among individual features, the LiDAR intensity feature emerges as critical for enhancing classification accuracy, while among single-class features, the HSI feature proves most influential in improving classification accuracy.

Evaluation of the Usability of UAV LiDAR for Analysis of Karst (Doline) Terrain Morphology.

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Models (DEMs) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicles (UAVs), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSMs) obtained through UAV photogrammetry, and DEMs acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future; this is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating the area and slope of the study sites.

Lava flow field development and lava tube formation during the 1858–1861 eruption of Vesuvius (Italy), unravelled by historical documentation, lidar data and 3D mapping

Somma-Vesuvius is well known for its powerful Plinian explosive eruptions, however during the last eruptive cycle (1631–1944), persistent activity took place on the stratovolcano as mild and violent Strombolian, and effusive eruptions, forming more than one hundred lava flow fields. An important mechanism of lava transport within lava flow fields is the formation and development of lava tubes. The presence of lava tubes in a flow field can greatly increase their distance of emplacement. Observations of lava tubes at Vesuvius have been documented in historical records and speleological reports but no modern scientific studies are available. This work focuses on lava tubes formed in the compound lava flow field of the long-lived 1858 eruption (from 27 May 1858 to 12 April 1861) that was fed by seven eruptive fissures. The temporal and spatial evolution of the 1858 lava flow field was reconstructed using historical documentation. The exposed lava flow field surface was analysed using a 1-m resolution lidar Digital Surface Model (DSM). Surveys to fully digitize the interior and the overlying surface of the largest lava tube found in the 1858 lava flow field were conducted using a terrestrial laser scanner, optical cameras, and an Unmanned Aerial Vehicle (UAV). The accurate 3D model obtained was used to precisely quantify the inner dimensions and to better constrain the morphologies of the lava tube. Observed internal features were described and used to gain information on the formation and activity of the lava tube. Our data allowed us to understand that the described lava tube formed as an inflated lava flow inside which lava flowed through during an extended period ultimately draining out completely at the end of the eruption. Understanding how lava flow fields develop and how lava tubes form on Vesuvius is crucial to re-evaluate the last effusive activity of the volcano and its impact on hazard assessment.

Surface condition driven fatigue performance of laser powder bed fusion H13 steel

Surface variation is an inherent imperfection in the laser powder bed fusion (LPBF) processed samples, comprising of primary roughness (PRR) from the melt pool solidification and secondary roughness (SER) from adhered powder particles. Components under cyclic loading are sensitive to surface defects, making surface finishing crucial for fatigue-bearing parts. However, this undermines LPBF’s advantage of creating high-performance and geometrically complex components. This study examines the effect of surface roughness on fatigue crack initiation in LPBF H13 steel, a high-strength alloy with a tensile strength of 1800 MPa. The focus is on scenarios where contour scanning strategies are not employed. Vertically deposited samples were subjected to three surface conditions: as-built (AB) with PRR + SER, half-polished (HP) with only PRR, and well-machined (M) with no PRR or SER. Optical and digital surface roughness characterization and stress-controlled fatigue testing were performed. Results revealed that while AB-samples showed significantly rough surface compared to HP-samples, their fatigue performance did not reduce. In contrast, M-samples had a slight improvement in roughness but exhibited a fatigue life increase by a factor greater than 17. This was attributed to the PRR, specifically the solidification valleys between the neighboring melt pools. Factographic analysis showed that the PRR-driven fatigue crack initiation is a critical determinant of fatigue performance.

Gain-based Green Ant Colony Optimization for 3D Path Planning on Remote Sensing Images

Metaheuristic algorithms are powerful methods for handling complexities in 3D environments because of their adaptability property. This paper proposes a gain-based, green-ant colony optimization (GGACO) method for 3D path planning on remote sensing images. Shortest paths do not always imply minimum energy consumption. Moreover, computational complexity tends to increase in the case of higher-dimensional data. A novel method is proposed to alleviate this issue, one that provides an efficient path with minimum energy consumption by adding a gain quantity during its search. The results are validated using performance measures, viz., path length, time, and energy cost. Real-time images, along with their corresponding ground truth and “digital surface models (DSM)”, have been sourced from the “International Society for Photogrammetry and Remote Sensing (ISPRS)”. Comparisons have been made against state-of-the-art algorithms and analyzed. Finally, the convergence and stability of the proposed method have been verified; it has been found that the proposed method outperforms the existing method by 6%, 11% and 5% regarding length, computation time, and energy, respectively.

UAV REMOTE SENSING APPLICATIONS IN BEACH-CLIFF SYSTEM MONITORING OF MORPHODYNAMIC PROCESSES

This study aims to evaluate the morphodynamic changes in a beach-cliff system in northeastern Brazil using high resolution data collected by UAV-SfM techniques to understand the geomorphic changes of the coastal landscape. The study used the Geomorphic Change Detection (GCD) method applied to short-term monitoring. This method uses Digital Elevation Models (DEMs) to determine the morphological changes in terms of both erosion and deposition through DEMs of Difference (DoDs). The difference gridded models are acquired by drone-based remote sensing, which can accurately and efficiently provide ultra-high resolution imagery and digital surface model data to measure volumetric changes. The geomorphic changes are observed on high resolution maps. The multi-temporal analysis revealed significant volumes of erosion and deposition throughout the beach-cliff system over the study period. In the first interval (May/2021 - November/2021), the cliff recorded -8,715 m³ of erosion and 2,816 m³ of deposition, with the beach sector showing erosion in 86% of its area. In the second period (November/2021 - March/2022), more severe erosion rates were observed on the cliff, reaching -10,853 m³ with 51% of its area eroded, while the beach showed the opposite behavior to that previously analyzed, with 73% of the beach experiencing an accumulation of sand and a positive balance of 5,960 m³. The results of this study can be fruitful in identifying the different of geomorphology hazards, coastal impacts of cliff urbanization and in the use of remotely piloted aircraft in coastal monitoring. Furthermore, the results indicate that the management of the beach-cliff system must be integrated and never dissociated, as is generally done by coastal managers. Keywords: Geomorphometry; DEMs; Drone-Based Remote Sensing; Mass Movement; Elevation and Volumetric.

Structural geological surveys and improvement of tectonic understating of the Pre-Ordovician/Tertiary Graben boundary in vicinity of Nicolaus Copernicus Polar Station, Svalbard

Researchers around the globe are on a quest to develop solutions to mitigate and prevent global warming and climate change. This includes facilitating the switch to renewable energies. Assistant Professor Slawomir Jack Giletycz, Director of the Polar Research Station in Svalbard and part of the Structural Geomorphology Research Group, Department of Earth Sciences at National Central University in Taiwan, is interested in glacial retreat in Svalbard, Norway. As the Earth warms, glaciers are melting and land that was previously buried by glaciers is being exposed. By studying this land, researchers can glean new insights. For example, Giletycz and his collaborators are performing geological mapping of areas that have been recently exposed to subaerial conditions due to the glacial retreat. They are working to develop new geological maps and interpret tectonic and structural settings. The researchers are also investigating fluvial system development and surveying particular glaciers. They are using unmanned aerial vehicles to capture aerial images of entire glacier river systems and can use the images to build a digital surface model that can be used in applications such as QGiS or ArcGiS. In the long tem, they can monitor the development of glacier fluvial systems such as sediment discharge. Another focus for the researchers is conducting plaeocoast surveys of the Kaffiøyra plain in western Svalbard. Excitingly, the researchers are the first humans to study Svalbard in this way and, through this work, they will make significant contributions to geological mapping and underpin geology, tectonics and structures.

Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing

Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area Dürrenstein-Lassingtal in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO2 and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.

Rapid Development of A Spatial Digital Twins Using Open-source Solutions

Abstract. City-model spatial Digital Twins are a three-dimensional virtual self-updating twin of a city which is updated by real-time data. This paper explores the effectiveness of open-source data and tools that can generate a Digital Twin to be hosted on a distributed system. A spatial Digital Twin was successfully generated from a city-model made from open-source spatial data with access to real-time data. The Digital Twin application was hosted on a three-tier systems architecture. The exception to the open-source data was a Digital Surface Model (DSM) obtained from a private source. However, the DSM was an optional component. The output only met the minimum requirements of a Digital Twin while the visualization was basic where the live data was limited to global weather data rather than city-specific data. The investigation indicated that even with little data, generating a 3D city scene with an automated live data flow meets the spatial Digital Twin’s minimum requirements according to Piroumian (2023). The main contribution of the study is highlighting methods for generating and hosting a spatial Digital Twin and its associated data onto the web through using a three-tier architecture to store, host and display the Twin onto the web. The areas identified for further research include investigating variations in the distributed systems for hosting and publishing a spatial Digital Twin onto the web. A secondary subject of investigation is exploring methodologies for improving the detail of the spatial Digital Twin’s data for areas that do not receive much live data.

DSM Reconstruction from Uncalibrated Multi-View Satellite Stereo Images by RPC Estimation and Integration

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery.

3D Modelling and Measuring Dam System of a Pellucid Tufa Lake Using UAV Digital Photogrammetry

The acquisition of the three-dimensional (3D) morphology of the complete tufa dam system is of great significance for analyzing the formation and development of a pellucid tufa lake in a fluvial tufa valley. The dam system is usually composed of the dams partially exposed above-water and the ones totally submerged underwater. This situation makes it difficult to directly obtain the real 3D scene of the dam system solely using an existing measurement technique. In recent years, unmanned aerial vehicle (UAV) digital photogrammetry has been increasingly used to acquire high-precision 3D models of various earth surface scenes. In this study, taking Wolong Lake and its neighborhood in Jiuzhaigou Valley, China as the study site, we employed a fixed-wing UAV equipped with a consumer-level digital camera to capture the overlapping images, and produced the initial Digital Surface Model (DSM) of the dam system. The refraction correction was applied to retrieving the underwater Digital Elevation Model (DEM) of the submerged dam or dam part, and the ground interpolation was adopted to eliminate vegetation obstruction to obtain the DEM of the dam parts above-water. Based on the complete 3D model of the dam system, the elevation profiles along the centerlines of Wolong Lake were derived, and the dimension data of those tufa dams on the section lines were accurately measured. In combination of local hydrodynamics, the implication of the morphological characteristics for analyzing the formation and development of the tufa dam system was also explored.

Integration of very high-resolution stereo satellite images and airborne or satellite Lidar for Eucalyptus canopy height estimation

Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSMP) were combined with Digital Terrain Models extracted from airborne Lidar data (DTMALS) to create Canopy Height Models (CHMALS). The evaluation of the CHMALS was based on two in situ canopy height measurements (Hmax and Hmean). For the SP site, the CHMALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ Hmean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHMALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTMALS in absence of airborne Lidar data. The resulting CHMGedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHMGedimax) showed a bias and rRMSE of 1.3 m and 20.5% for the SP site, and 2.2 m and 24.9% for the MS site. These findings highlight the potential for integrating Pleiades and Lidar data for efficient and accurate canopy height monitoring in eucalyptus plantations.