Accurate Mapping Research Articles

Optimizing nurse practitioner productivity within the Veterans Health Administration.

Nurse practitioners (NPs) provide high-quality, comprehensive health care at Veterans Health Administration (VHA) medical centers in various practice settings. Accurately determining the productivity of NPs is essential to understanding their overall contribution to veteran care. There is a lack of understanding of the contributors to the variability among VHA medical centers regarding their processes for capturing these data. Using benchmark data from the VHA Support Service Center Capital Assets application, this quality improvement (QI) initiative implemented a series of plan-do-study-act (PDSA) cycles to determine whether increased education regarding billing and coding, along with person classification and labor mapping accuracy, could increase NP productivity. The problem identified through an analysis of NP productivity revealed variability across multiple VHA facilities. The PDSA method was employed in this QI project. The QI team compared the total workload relative value units (wRVU) for NPs in each participating facility pre and post a three-phase intervention. Nurse practitioners' person classification and labor mapping accuracy was evaluated at each site. An educational program regarding billing and coding was developed and delivered to the NP frontline staff. The QI team compared the total wRVUs for the NPs at each facility pre- and postintervention. An increase in the wRVUs was observed following the intervention across six facilities. Five of the seven facilities displayed stability in relative value unit improvement postintervention. Educating frontline NPs on billing, coding, person classification, and labor mapping can improve NP productivity.

Mapping the liquid territories of the Danube Delta (Romania): The atlases of the European Commission of the Danube

The European Commission of the Danube (ECD) 1856–1939 published an impressive number of maps, the most important being included in four atlases. However, despite previous efforts, until 1870, no precise map of the Danube Delta based on a unitary triangulation survey has been published. This article examines the mapping effort to recreate the triangulation network containing all the 110 points, based on the distances specified in the 1874 atlas, landmarks discovered during fieldwork and other reference layers used for georeferencing the 1870, 1886 and 1902 maps. Moreover, it offers supplementary information about ECD's vision of the liquid territories and their central feature: water and its representation on cartographic material. In our explanation, we explore the differences between various cartographic products and other sources of information used by the European Commission of the Danube to update its navigation charts. By reproducing these mapping efforts, we highlight the technical challenges for accurately mapping the Danube Delta's everchanging territory. Furthermore, by carrying out the work on digitising and indexing available cartographic material and validating them for accuracy we deliver an enhanced resource that can provide a new understanding for these maps, given their political and economic message. Finally, in this article we offer some reflections on the possible propaganda role the atlases may have played, as the accurate map building role of the ECD does not only assert technical mastery, but also makes a statement of control over the mapped territory.

R 3LIVE++: A Robust, Real-Time, Radiance Reconstruction Package With a Tightly-Coupled LiDAR-Inertial-Visual State Estimator.

This work proposed a LiDAR-inertial-visual fusion framework termed R 3LIVE++ to achieve robust and accurate state estimation while simultaneously reconstructing the radiance map on the fly. R 3LIVE++ consists of a LiDAR-inertial odometry (LIO) and a visual-inertial odometry (VIO), both running in real-time. The LIO subsystem utilizes the measurements from a LiDAR for reconstructing the geometric structure, while the VIO subsystem simultaneously recovers the radiance information of the geometric structure from the input images. R 3LIVE++ is developed based on R 3LIVE and further improves the accuracy in localization and mapping by accounting for the camera photometric calibration and the online estimation of camera exposure time. We conduct more extensive experiments on public and self-collected datasets to compare our proposed system against other state-of-the-art SLAM systems. Quantitative and qualitative results show that R 3LIVE++ has significant improvements over others in both accuracy and robustness. Moreover, to demonstrate the extendability of R 3LIVE++, we developed several applications based on our reconstructed maps, such as high dynamic range (HDR) imaging, virtual environment exploration, and 3D video gaming.

Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI

AI-based breast cancer detection can improve the sensitivity and specificity of detection, especially for small lesions, which has clinical value in realizing early detection and treatment so as to reduce mortality. The two-stage detection network performs well; however, it adopts an imprecise ROI during classification, which can easily include surrounding tumor tissues. Additionally, fuzzy noise is a significant contributor to false positives. We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. Our approach outperformed Faster R-CNN, Mask R-CNN, and YOLOv9 in breast cancer detection across 485 internal cases. We achieved superior performance in mAP, sensitivity, and false positive rate ((0.752, 0.950, 0.133) vs. (0.711, 0.950, 0.200) vs. (0.718, 0.880, 0.120) vs. (0.658, 0.680, 405)), which represents a 38.5% reduction in false positives compared to manual detection. Additionally, in a public dataset of 220 cases, our model also demonstrated the best performance. It showed improved sensitivity and specificity, effectively assisting doctors in diagnosing cancer.

Ecological condition of seagrass meadows around sea-based tin mining activities in the waters of Bangka Belitung Islands, Indonesia

Bangka Belitung islands have long been major tin producers, impacting the environment, including coastal ecosystems. Tin extraction from the seabed increases water turbidity and alters chemical composition, adversely affecting seagrass meadows. This study investigated the ecological condition of seagrass meadows around sea-based tin mining activities on the islands. We mapped seagrass habitat extent and calculated the Seagrass Ecological Quality Index (SEQI) at five locations reflecting varying environmental impacts of tin mining. Satellite imagery from Landsat 7 ETM+ (2000) and Sentinel L2A (2022) was used, showing higher mapping accuracy during low tide. Our analysis revealed that seagrass meadows near mining sites had low species diversity and significant habitat decline over the past two decades (82.79 %). SEQI values were lower, and Total Suspended Solid (TSS) levels were higher near mining operations. This study underscores the detrimental effects of tin mining on seagrass meadows, urging stringent regulations, rehabilitation, and continuous monitoring for sustainability.

3D point cloud regularization method for uniform mesh generation of mining excavations

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison).

Lithological Mapping of the Neoproterozoic Basement Rocks in the Area East El-Sibai Core Complex (SCC), Central Eastern Desert of Egypt, Based on Remote Sensing Data and Field Observations

Spectral data are an active tool for mineral exploration, lithological, and structural mapping by spectral classification; due to its accuracy, ease of use, and low cost, especially in inaccessible areas. This study aims to integrate Landsat-8/OLI and ASTER images with extensive field survey and petrographical analysis to discriminate the Neoproterozoic basement rock units in the area east Gabal El-Sibai. In image enhancement techniques, we used the Correlation Coefficient and Optimum Index Factor for choosing the best-colored images to discriminate various rock units based on spectral signature curves. The exposed rock units consist of granitic rocks of variable compositions intruded directly into ophiolitic and island arc associations. The different rock units and structural elements in this study were mapped and discriminated using False Color Composites images (7-6-1 of Landsat-8 and 9-4-3 of ASTER), Minimum Noise Fraction (6-5-1), Principal Component Analysis (4-1-2 for Landsat-8 and 3-1-4 for ASTER), two newly proposed ratios images (3/4, 6/7, 5/6 for Landsat-8 and 4/1, 3/1, 8/9 for ASTER), with the Maximum Likelihood Classification and Support Vector Machine Classification. In conclusion; the overall accuracy and the Kappa coefficient show a good agreement with the Principal Component Analysis and Band Ratio results in the current geological map. Therefore, the suggested methodology shows that optical data have a strong potential for discriminating between lithological units. This approach provides accurate information to geologists, geophysical engineers, and land-use land-cover decision-makers. It can also improve and enhance the accuracy of geological maps in the Nubian Shield in Egypt and other arid environments worldwide.

Multi-Platform Collaboration in Integrated Surveying: Ensuring Completeness and Reliability of Geospatial Data—A Case Study

Multi-platform geospatial data synergy is critical for complete and reliable surveys. This study investigates various methods for combining data from terrestrial laser scanning (TLS), orthophotos, databases of topographic objects, utility databases, tacheometry, and GNSS to assess and improve positioning accuracy and consistency of data in spatial databases. The study highlights the challenges and solutions regarding integrating various datasets to yield a complete and reliable geospatial database for building surveys and the construction and keeping of spatial databases. Input from diversified surveying technologies, such as TLS, GNSS, and orthophotos, offers detailed and precise data necessary to create and update accurate base maps and databases of topographic objects. A complete survey of a structure and its surroundings demonstrates how the synergistic application of diverse data sources helps improve the positioning accuracy and consistency of spatial databases. The results indicate the importance of multi-platform collaboration for high data quality standards in surveying, which is critical for effective planning and implementation of surveying projects.

An advanced dorsiventral leaf radiative transfer model for simulating multi-angular and spectral reflection: Considering asymmetry of leaf internal and surface structure

Understanding the optical properties of dorsiventral leaves and quantifying leaf biochemical traits through physical models are important for interpreting canopy radiative transfer and monitoring plant growth. Previous models, such as the dorsiventral leaf model (DLM), have effectively accounted for the inner asymmetry of the leaf but neglected the asymmetry of surface structures between the upper and lower epidermis. In this study, we found marked differences in bidirectional reflectance factors (BRF) between the adaxial and abaxial surfaces of leaves under multi-angular measurements due to surface structural distinctions. To address this asymmetry in both internal and surface leaf structures, we subsequently proposed an advanced DLM model (MADLM) for simulating both multi-angular and spectral BRF of two leaf sides, linking the angular reflection of leaf adaxial and abaxial sides to surface structural parameters (roughness and refractive index) based on microfacet theory. Results show that MADLM accurately simulates multi-angular and spectral BRF for both sides of dorsiventral leaves, and yields satisfactory retrieval accuracy of leaf traits from all observation geometries. For close-range hyperspectral imaging applications, we further introduced a simplified version, sMADLM, which characterizes the surface reflection of two leaf sides in terms of the product of a leaf-side dependent parameter and the wavelength-dependent Fresnel factor. The sMADLM improves the mapping accuracy of leaf biochemical traits by effectively reducing the surface reflection effects in dorsiventral leaves. The MADLM and sMADLM deepen our understanding of the optical properties of dorsiventral leaves and provide practical methods for retrieving leaf biochemical traits via optical remote sensing.

Progressive de-preference task-specific processing for generalizable person re-identification

Recently, domain generalization (DG) person re-identification (ReID) has attracted attention. Existing DG person ReID methods train on mixed datasets containing all source domains. However, these mixed datasets have huge inter-domain differences because of varying data distributions across different source domains. Such differences hinder models from learning domain-invariant representations, affecting generalization on unseen domains. To address this issue, we propose a progressive de-preference task-specific processing network (PDTP-Net) for DG person ReID. Initially, we design a progressive de-preference domain segmentation strategy to mitigate inter-domain differences by dividing multiple source domains into different phases, each comprising several training tasks. We then design a global and task-specific processing module that enhances extraction of domain-invariant features by integrating statistical information from other source domains. Finally, we design a multi-granularity attention module and a group-aware batch normalization strategy to ensure the features are more discriminative and better suited for person ReID tasks. The proposed model is validated using three DG person ReID experimental protocols: Protocol-1, Protocol-2, and leave-one-out experiments. On Protocol-1, the model improves mean average precision (mAP) and Rank-1 accuracy on all datasets by an average of 0.7% and 0.3%, respectively. On Protocol-2, the model improves mAP and Rank-1 accuracy on all datasets by an average of 2.525% and 2.725%, respectively. On the leave-one-out experiments, the model improves mAP and Rank-1 accuracy on all tasks by an average of 0.65% and 0.18%, respectively. The results on several popular datasets suggest that the model achieves state-of-the-art performance in DG person ReID.

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

Land suitability modeling integrating geospatial data and artificial intelligence

ContextSustainable agricultural practices are critical in a world grappling with climate change and pressure on natural resources. Unplanned agricultural expansion often harms ecosystems and the services they provide. Balancing food production with environmental protection demands sophisticated tools like spatial analysis and artificial intelligence to inform land-use decisions. ObjectiveThis study introduces an AI-driven process to assess land suitability for agrosilvopastoral systems, going beyond traditional methods by incorporating a broader spectrum of landscape characteristics. Our approach integrates climate, water resources, soil properties, morphological features, and accessibility to enhance the accuracy of suitability mapping. MethodsWe constructed a data cube comprising 100 geospatial layers representing diverse landscape attributes. Field observations from two watersheds in Minas Gerais, Brazil, were used to train and validate a Random Forest classification model. We evaluated the model's accuracy and quantified the influence of each attribute group on suitability determination. ResultsIntegrating climate, water, edaphic, and morphological attributes significantly improved the model's accuracy and provided a more nuanced understanding of agrosilvopastoral suitability compared to using only soil class, lithology, and slope. Climate and edaphic variables emerged as key drivers of suitability. This approach identified a more constrained, yet potentially more sustainable, distribution of suitable land. SignificanceOur findings highlight the need to transition from conventional land suitability assessments towards more holistic, data-driven approaches that consider the complex interplay of environmental factors. This model offers a valuable tool for guiding sustainable land-use planning, potentially mitigating environmental impacts while optimizing agrosilvopastoral production.

Adaptive fuzzy logic for gap filling in UAV orthomosaics: A methodology for accurate geospatial mapping

Adaptive fuzzy logic for gap filling in UAV orthomosaics: A methodology for accurate geospatial mapping

Enhancing LiDAR Mapping with YOLO-Based Potential Dynamic Object Removal in Autonomous Driving

In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas. Existing methods using LiDAR segmentation for map matching often suffer from missed detections and false positives, degrading performance. Our approach leverages YOLOv4’s robust object detection capabilities to eliminate potentially dynamic objects while retaining static environmental features, such as buildings, to enhance map accuracy and reliability. The proposed system was validated using a mid-size SUV equipped with LiDAR and camera sensors. The experimental results demonstrate significant improvements in map-matching and localization performance, particularly in urban environments. The system achieved RMSE (Root Mean Square Error) reductions compared to conventional methods, with RMSE values decreasing from 0.9870 to 0.9724 in open areas and from 1.3874 to 1.1217 in urban areas. These findings highlight the ability of the Vision + LiDAR + NDT method to enhance localization performance in both simple and complex environments. By addressing the challenges of dynamic obstacles, the proposed system effectively improves the accuracy and robustness of autonomous navigation in high-traffic settings without relying on GPS.

Cross-modal fusion approach with multispectral, LiDAR, and SAR data for forest canopy height mapping in mountainous region

The study introduces a technique for integrating multispectral, LiDAR, and Synthetic Aperture Radar (SAR) data within a machine-learning (ML) framework. By leveraging ML models, including Random Forest (RF), Gaussian Process Regression (GPR), and k-Nearest Neighbors (k-NN), successfully provides a comprehensive methodology for mapping forest canopy height (CH) and analyzes seasonal changes from 2019 to 2023 in the mountainous region of Vodno Mountain, North Macedonia. The RF model achieved the highest accuracy (R2 = 0.91, RMSE = 1.2 m), outperforming the other models when trained with Aerial LiDAR data. The forest CH models were validated against field measurements, Aerial LiDAR, and Global Ecosystem Dynamics Investigation (GEDI) data, confirming the accuracy of the approach and showing solid correlations between predicted and observed CH values. This research is significant due to its innovative approach to forest CH modeling in a region with minimal prior studies. Integrating multi-source data enables more accurate and detailed CH mapping, essential for monitoring forest biomass and carbon stocks, detecting forest disturbances, and assessing future forest management activities.

Visuospatial working memory and (free and cued) recall of survey knowledge after environment navigation.

Navigating environments is a fundamental ability of daily life, with survey knowledge playing a crucial role. Survey knowledge varies between individuals, and these variations may be related to individual differences in visuospatial working memory (VSWM) ability. However, other factors, such as the modalities of recall (cued vs. free recall) of survey knowledge, could interact with VSWM resources. The present study aimed to clarify whether various types of VSWM contribute to survey knowledge under specific recall modalities or regardless of how spatial information is retrieved. A sample of 74 young adults performed VSWM tasks with varying processing demands and degrees of active involvement. Then, they actively learned a virtual city path in a Cave Automatic Virtual Environment (CAVE) environment, and their survey knowledge was assessed using a sketch map task in free and cued recall modalities (within-participants). Cued recall demonstrated an advantage in sketch map accuracy over free recall. VSWM with simultaneous processing and active mental imagery is associated with sketch map accuracy, but not other VSWM. Importantly, no interaction was found between VSWM and the modality of recall. Therefore, survey knowledge is primarily related to VSWM, regardless of recall modality, emphasizing the importance of VSWM ability in capturing survey knowledge after active navigation.

Review on Multisensor SLAM Datasets for Advanced Perception and Mapping Technologies

Abstract. SLAM (Simultaneous Localization and Mapping) is a technique in robotics and computer vision used to build a map of an unknown environment while simultaneously tracking the location of a robot or vehicle within that environment. The primary goal is to enable autonomous systems to navigate and understand their surroundings without prior knowledge of the environment. It has evolved significantly with the integration of diverse sensor modalities which initially used either a single LIDAR (light detection and ranging, or laser imaging, detection, and ranging) or visual sensor to perform the dual tasks of mapping an environment and localizing the device within it. These systems had limitations in accuracy and robustness due to their reliance on a single type of data input. Over time, the field has advanced to incorporate multiple sensor modalities, including LIDAR, visual cameras, Inertial Measurement Units (IMUs), ultrasonic sensors, and GPS. This multi-sensor fusion approach has dramatically enhanced the precision and reliability of SLAM systems. This paper reviews the state-of-the-art datasets that combine data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar, focusing on their contributions to advancing SLAM technologies. The study analyzes the advantages and limitations of each sensor type, the challenges associated with data fusion, and the impact on perception and mapping accuracy. This review aims to provide a comprehensive understanding of how these multisensor datasets enhance SLAM systems and highlight areas for future research.

A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics

Mapping the spatial distribution of soil organic matter (SOM) content is crucial for land management decisions, yet its accurate mapping faces challenges due to complex soil-environment relationships and temporal feature capture limitations in machine learning models. This study focuses on the typical black soil region in Northeast China, specifically using Youyi Farm as the main research area and Heshan Farm as the transfer research area. A novel approach is proposed that combines the CNN-LSTM model with a Cosine Annealing Warm Restarts learning rate (CNN-LSTM-CAWR) to enhance the accuracy of SOM mapping. In this model, the Convolutional Neural Network (CNN) extracts spatial context features from static variables (e.g., climate and terrain variables), while the Long Short-Term Memory (LSTM) network captures temporal features from dynamic variables (e.g., Sentinel-2 time series from April to October). The incorporation of the CAWR learning rate helps alleviate overfitting issues. Comparing the CNN-LSTM model, CNN model, and traditional RF model, the results show that the CNN-LSTM-CAWR model achieves the highest accuracy within research Area 1 (R2 = 0.64, RMSE = 0.54 %) and maintains strong performance in the transfer research area (R2 = 0.60, RMSE = 0.57 %). CNN-LSTM-CAWR demonstrates faster convergence, thereby improving mapping precision and effectively utilizing temporal information from features to enhance overall model performance. This study underscores the significant potential of the hybrid CNN-LSTM with CAWR model, highlighting the valuable information for SOM mapping contained within Sentinel-2 time series data.

Soil carbon in the world’s tidal marshes

Tidal marshes are threatened coastal ecosystems known for their capacity to store large amounts of carbon in their water-logged soils. Accurate quantification and mapping of global tidal marshes soil organic carbon (SOC) stocks is of considerable value to conservation efforts. Here, we used training data from 3710 unique locations, landscape-level environmental drivers and a global tidal marsh extent map to produce a global, spatially explicit map of SOC storage in tidal marshes at 30 m resolution. Here we show the total global SOC stock to 1 m to be 1.44 Pg C, with a third of this value stored in the United States of America. On average, SOC in tidal marshes’ 0–30 and 30–100 cm soil layers are estimated at 83.1 Mg C ha−1 (average predicted error 44.8 Mg C ha−1) and 185.3 Mg C ha−1 (average predicted error 105.7 Mg C ha−1), respectively.

Vegetation Density Mapping of Urban Solid Waste Landfill Coverage Using Vegetation Indexes Obtained with UAV

Objective: The objective of this study is to map the vegetation cover of a municipal solid waste landfill using high-resolution vegetation index images obtained with UAV, as a tool for geotechnical monitoring. Theoretical Framework: The theoretical framework describes the state of the art of vegetation indexes, starting from the first proposal of NVDI by Rouse et al. (1973), through to the NDWI by McFeeters (1996). Additionally, it presents the method of representing radiometric variables, considering that these indexes operate based on the ratio between spectral bands, which are displayed within intervals ranging from -1 to +1. Method: The study used a UAV equipped with MAPIR multispectral camera to capture images of the landfill. NDVI and NDWI indexes were applied to assess vegetation health and coverage. Field control points determined with GNSS-RTK, along with protogrametry software, ensured mapping accuracy, creating high-resolution orhomosaics for monitoring and determining vegetation indexes. Results and Discussion: The results indicate variation in vegetation cover, revealing areas of exposed soil ang vegetation at different stages of vigor, from senescence to full growth. The NDVI and NDWI indexes highlighted regions with water stress and soil exposure, demonstrating the effectiveness of the method in environmental and geotechnical monitoring. Conclusions: It is concluded that the use of UAVs and vegetation indexes are effective for monitoring vegetation health and landfill stability, enanbling the identification of erosion areas and the need for vegetation cover restoration.