Resolution Unmanned Aerial Vehicle Research Articles

Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

Application of an improved U-Net with image-to-image translation and transfer learning in peach orchard segmentation

Peach cultivation holds a significant economic importance, and obtaining the spatial distribution of peach orchards is helpful for yield prediction and precision agriculture. In this study, we introduce a new U-Net semantic segmentation model, utilizing ResNet50 as a backbone network, augmented with an Efficient Multi-Scale Attention (EMA) mechanism module and a LayerScale adaptive scaling parameter. To address style differences between images from Unmanned Aerial Vehicle (UAV), Google Earth, and Sentinel-2 satellite, we incorporate Cycle-Consistent Generative Adversarial Networks (CycleGAN). This synthesis ensures that UAV images conform to a comparable style found in Google Earth and Sentinel-2 images, while feature details of high spatial resolution UAV images are transferred to Google Earth and Sentinel-2 images through transfer learning. The results demonstrate that using ResNet50 as a backbone network for the U-Net model yields higher accuracy compared to using VGG16 for the U-Net model. Specifically, the Mean Intersection over Union (MIoU) values for UAV and Sentinel-2 images are higher by 0.49 % and 0.95 %, respectively. The MIoU values for UAV, Google Earth, and Sentinel-2 images increased by 0.87 %, 1.71 %, and 1.74 %, respectively, with the introduction of EMA. Additionally, with the introduction of LayerScale adaptive scaling parameters, the MIoU values increased by 0.31 %, 0.33 %, and 1.44 %, respectively, further enhancing the segmentation accuracy of the model. After applying CycleGAN and transfer learning, the MIoU increased by 1.02 %, 0.15 %, and 1.57 % for UAV, Google Earth, and Sentinel-2 images, respectively, resulting in MIoU values of 97.39 %, 92.08 %, and 84.54 %. The comparative analysis with DeepLabV3+, PSPNet, and HRNet models demonstrates the superior mapping performance of the proposed method. Moreover, the method exhibits good generalization and mapping speed across six test sites in the research area. Overall, this approach ensures high precision and efficiency in peach orchard mapping, accommodating various spatial resolutions, and holds potential for addressing diverse requirements in peach orchard mapping applications.

Mapping invasive noxious weed species in the alpine grassland ecosystems using very high spatial resolution UAV hyperspectral imagery and a novel deep learning model

ABSTRACT The term “invasive noxious weed species” (INWS), which refers to noxious weed plants that invade native alpine grasslands, has increasingly become an ecological and economic threat in the alpine grassland ecosystem of the Qinghai-Tibetan Plateau (QTP). Both the INWS and native grass species are small in physical size and share a habitat. Using remote sensing data to distinguish INWS from native alpine grass species remains a challenge. High spatial resolution hyperspectral imagery provides an alternative for addressing this problem. Here, we explored the use of unmanned aerial vehicle (UAV) hyperspectral imagery and deep learning methods with a small sample size for mapping the INWS in mixed alpine grasslands. To assess the method, UAV hyperspectral data with a very high spatial resolution of 2 cm were collected from the study site, and a novel convolutional neural network (CNN) model called 3D&2D-INWS-CNN was developed to take full advantage of the rich information provided by the imagery. The results indicate that the proposed 3D&2D-INWS-CNN model applied to the collected imagery for mapping INWS and native species with small ground truth training samples is robust and sufficient, with an overall classification accuracy exceeding 95% and a kappa value of 98.67%. The F1 score for each native species and INWS ranged from 92% to 99%. In conclusion, our results highlight the potential of using very high spatial resolution UAV hyperspectral data combined with a state-of-the-art deep learning model for INWS mapping even with small training samples in degraded alpine grassland ecosystems. Studies such as ours can aid the development of invasive species management practices and provide more data for decision-making in controlling the spread of invasive species in similar grassland ecosystems or, more widely, in terrestrial ecosystems.

Low-amplitude brittle deformations revealed by UAV surveys in alluvial fans along the northwest coast of Lake Baikal: Neotectonic significance and geological hazards

Many large deltas and other areas, underlain by unconsolidated sediments, are heavily populated but impacted by various natural deformational processes. The causes and mechanisms of the deformation are often obscure because of difficulties in the identification of their geological source. We used an unmanned aerial vehicle (UAV) to survey an area of up to several km2 to detect and map surface discontinuities with displacements of a few centimeters, which can indicate the presence of initial geological deformations. We encountered such offsets in the alluvial fans on the northwestern coast of Lake Baikal, overlying the damage zones of several faults. These deformations at the different sites are referred to as primary or secondary co-seismic ruptures, brittle creep, or cryogenic fissures, which have specific origins. They are located a few hundred meters from a principal fault plane, from junctions of faults, and in the peripheral parts of alluvial fans, making these areas potentially hazardous. We analyzed the digital surface models of local segments of one of the delta plains between 2020 and 2021 and established that its periphery has subsided by an average of 5–10 cm. In areas of accumulation of the river sediment, the thickness of coarse clastic deposits has increased by approximately the same amount. Locally and in the coastal zone, the vertical surface changes are larger. In the axial parts of some seismically induced gravitational failures, the subsidence reached 33 cm over a period of 11 months and 19 days. Our results show that sediments of alluvial fans are very susceptible to various tectonic and exogenous deformational processes. The interpretation of the ultra-high resolution UAV's images can help the recognition the low-amplitude brittle deformations at an early stage of their development. Therefore, such UAV surveys are critical in the discernment of neotectonic activity and its related hazards over short observation periods.

Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images

Monitoring tree phenology is important for understanding ecosystem functioning and for assessing ecosystem responses to climate change. Satellite imagery offers open-access global coverage but is restricted to forest-level analyses, due to its coarse spatial resolution. Unmanned aerial vehicle (UAV) imagery can monitor phenology at the individual tree level by utilizing a centimeter-scale resolution. Two research objectives were identified for this study: (1) to derive phenological metrics at the individual tree level, using various vegetation indices (VIs); and (2) to assess the accuracy of automatic crown delineation in a diverse ecosystem. To achieve this, fourteen multi-spectral UAV flights were performed, and the ability of the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), optimized soil-adjusted vegetation index (OSAVI), and chlorophyll index red-edge (CIre) to model seasonal phenology was assessed. A double logistic model was fitted on the VI observations for each individual tree, to derive the start of season (SOS) and end of season (EOS). Individual tree crowns were delineated automatically using marker-controlled watershed segmentation (MCWS), and the treetops were identified using a local maximum filter (LMF). Overall, the automatic segmentation performed well (F-score: 0.79, IoU: 0.58), with higher accuracies in single-species areas, while it underperformed in complex mixed forest structures. All VIs captured a strong seasonal signal for the deciduous trees and derived SOS and EOS estimates consistent with literature and ground observations. General phenological patterns included an early silver birch SOS, a quick beech budburst, and large within-species phenology variations for oak trees. Seasonal VI variation for coniferous evergreen trees was limited, and the resulting phenology estimates proved unreliable. In conclusion, these findings emphasize the capabilities of UAV imagery for individual tree crown phenology monitoring. However, they also show the difficulty of monitoring evergreen phenology with the commonly-used VIs and stress the need for further investigations.

Image Processing Techniques based Feature Extraction for Insect Damage Areas

Monitoring of forests is important for the diagnosis of insect damage to vegetation. Detection and monitoring of damaged areas facilitates the control of pests for practitioners. For this purpose, Unmanned Aerial Vehicles (UAVs) have been recently used to detect damaged areas. In order to distinguish damage areas from healthy areas on UAV images, it is necessary to extract the feature parameters of the images. Therefore, feature extraction is an important step in Computer Aided Diagnosis of insect damage monitored with UAV images. By reducing the size of the UAV image data, it is possible to distinguish between damaged and healthy areas from the extracted features. The accuracy of the classification algorithm depends on the segmentation method and the extracted features. The Grey-Level Co-occurrence Matrix (GLCM) characterizes areas texture based on the number of pixel pairs with specific intensity values arranged in specific spatial relationships. In this paper, texture characteristics of insect damage areas were extracted from UAV images using with GLCM. The 3000*4000 resolution UAV images containing damaged and healthy larch trees were analyzed using Definiens Developer (e-Cognition software) for multiresolution segmentation to detect the damaged areas. In this analysis, scale parameters were applied as 500, shape 0.1, color 0.9 and compactness 0.5. As a result of segmentation, GLCM homogeneity, GLCM contrast and GLCM entropy texture parameters were calculated for each segment. When calculating the texturing parameters, neighborhoods in different angular directions (0,45,90,135) are taken into account. As a result of the calculations made by considering all directions, it was found that GLCM homogeneity values ranged between 0.08 - 0.2, GLCM contrast values ranged between 82.86 - 303.58 and GLCM entropy values ranged between 7.81 - 8.51. On the other hand, GLCM homogeneity for healthy areas varies between 0.05 - 0.08, GLCM contrast between 441.70 - 888.80 and GLCM entropy between 8.93 - 9.40. The study demonstrated that GLCM technique can be a reliable method to detection of insect damage areas from UAV imagery.

Classification of Rice Growth Stage on UAV Image Based on Convolutional Neural Network Method

Currently, the majority of the agricultural sector in Indonesia is carried out by small communities. Half of the Indonesian people (approximately 10 million people) work in the agricultural sector and utilize agricultural land. Some of the tools used by farmers are still using traditional tools, but some are already using modern farming tools. In general, agricultural tools are divided into 3 categories, namely agricultural tools used before the seeds are planted, agricultural tools used when caring for seedlings that are growing and developing, and agricultural tools used when harvesting. One of the technologies used in agriculture is the use of drones or Unmanned Aerial Vehicles (UAV) in the process of sowing fertilizers and seeds and spraying pesticides. The current use of UAVs supports agriculture with manual operation and based on GPS waypoint positioning. In the process, the visual aspects that can be obtained from the UAV have not been considered, so the treatment carried out on agricultural land is the same. The problem of similarity in treatment can lead to similar treatment on heterogeneous agricultural land. Agricultural land should be treated according to the conditions of the land. Because the condition of the land will affect the growth of the planted vegetation. Another problem found in agricultural land is the different rice growth in each paddy field. Rice growth can be seen by farmers through visual aspects but farmers cannot directly see the visual condition of rice growth as a whole because of the large area of land. Utilization of UAV by taking high-resolution aerial imagery can provide visuals of the overall condition of rice from various angles of image capture. The general objective of this research is to classify rice growth on high resolution UAV images based on the Convolutional Neural Network (CNN). The data used in this study were acquired using a multirotor UAV in the same rice field area. The data consists of 500 images consisting of 5 groups. Group 1-2 is the vegetative phase, group 3 is the generative phase and group 4-5 is the ripening phase. CNN is used to conduct training with variations of epochs are 100, 250 and 500. The best accuracy results are obtained in the training epoch 500 with 96% of Accuration

A fusion of structured and unstructured datasets in curating fire damage

Accurate documentation and mitigation of wildfires is significant in addressing Sustainable Goal eleven (11) as well as the African Unions Agenda 2063 Goal one (1) which resonate an intention to preserve sustainable environments, communities, and cities for future generations. Poor and unscientific documentation of wildfire leads to error prone and quickly outdated event curation, making it difficult to create comprehensive overviews of wildfire events. Although several geospatial studies have recorded advances in wildfire data curation, many of these rely on structured spatial datasets which enjoy numerous advantages but can be prone to temporal or time-voids in data. Voids in data can be mitigated through interpolation techniques, but these are not without challenges for the modellers tasked with optimally filling the voids. On this backdrop, this paper therefore aims to test and extend strategies of wildfire reporting by investigating the strengths of fusing mainstream spatial data (including high resolution unmanned aerial vehicle (UAV) footage) with unstructured Twitter (tweets) data in order to disrupt common practise and contribute to the global agenda. The novelty of this contribution lies in its unusual hybrid data approach used to statistically establishes long- and short-term pre-fire conditions tested on the African landscape. It proceeds to illustrate how the social media data can create fascinating and yet accurate visual timelines of the events that surprisingly compared strongly to the results from structured sources. Finally, the study detects the extent of damage from the wildfire using supervised classification, burn indices and three-dimensional (3D) reconstruction. A 95% positive detection rate was reported, and it affirmed the place of unstructured data in mainstream scientific approaches such as wildfire documentation. The work presented therefore contributes towards meeting African Union's Agenda 2063 and the United Nations Sustainable Development Goals (2030) in curation, documentation and mitigation of sustainable community and environments.

Automatic detection and counting of oil palm trees using remote sensing and object-based deep learning

Palm oil is one of the world's highest vegetable oil producers. Providing accurate oil palm plantation statistics is essential to supporting effective and efficient decision-making, especially since the oil palm industry has a strategic role in achieving several Sustainable Development Goals (SDGs). The existing conventional data collection has been focusing on field survey methods which require considerable human resources and costs, long collection and processing times, and have difficulty in reaching remote areas. Remote sensing with satellite imagery and Unmanned Aerial Vehicles (UAV) can be an alternative due to its advantages which include a more efficient workforce, shorter update times, comprehensive area coverage, and the ability to reach remote locations. In this study, we propose the utilization of Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and UAV with image processing threshold and object-based deep learning methods to detect and count the number of oil palm trees. Experimental results in a case study in Rokan Hulu Regency, Riau, Indonesia show that the object-based deep learning model of the You Only Look Once (YOLO) architecture achieves excellent performance with an F1-Score of up to 91.05%. This model is superior to the widely adopted baseline model of the image processing threshold, which only reached an F1-Score of 40.59%. Our findings suggest the use of very high resolution images with object-based deep learning is promising for automatic detection and counting of oil palm trees to support the accurate and sustainable agricultural monitoring.

Extraction of Rural Residential Land from Very-High Resolution UAV Images Using a Novel Semantic Segmentation Framework

Accurate recognition and extraction of rural residential land (RRL) is significant for scientific planning, utilization, and management of rural land. Very-High Resolution (VHR) Unmanned Aerial Vehicle (UAV) images and deep learning techniques can provide data and methodological support for the target. However, RRL, as a complex land use assemblage, exhibits features of different scales under VHR images, as well as the presence of complex impervious layers and backgrounds such as natural surfaces and tree shadows in rural areas. It still needs further research to determine how to deal with multi-scale features and accurate edge features in such scenarios. In response to the above problems, a novel framework named cascaded dense dilated network (CDD-Net), which combines DenseNet, ASPP, and PointRend, is proposed for RRL extraction from VHR images. The advantages of the proposed framework are as follows: Firstly, DenseNet is used as a feature extraction network, allowing feature reuse and better network design with fewer parameters. Secondly, the ASPP module can better handle multi-scale features. Thirdly, PointRend is added to the model to improve the segmentation accuracy of the edges. The research takes a plain village in China as the research area. Experimental results show that the Precision, Recall, F1 score, and Dice coefficients of our approach are 91.41%, 93.86%, 92.62%, and 0.8359, respectively, higher than other advanced models used for comparison. It is feasible in the task of high-precision extraction of RRL using VHR UAV images. This research could provide technical support for rural land planning, analysis, and formulation of land management policies.

Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

Invasive alien plants (IAPs) are increasingly threatening biodiversity worldwide; thus, early detection and monitoring tools are needed. Here, we explored the potential of unmanned aerial vehicle (UAV) images in providing intermediate reference data which are able to link IAP field occurrence and satellite information. Specifically, we used very high spatial resolution (VHR) UAV maps of A. saligna as calibration data for satellite-based predictions of its spread in the Mediterranean coastal dunes. Based on two satellite platforms (PlanetScope and Sentinel-2), we developed and tested a dedicated procedure to predict A. saligna spread organized in four steps: 1) setting of calibration data for satellite-based predictions, by aggregating UAV-based VHR IAP maps to satellite spatial resolution (3 and 10 m); 2) selection of monthly multispectral (blue, green, red, and near infra-red bands) cloud-free images for both satellite platforms; 3) calculation of monthly spectral variables depicting leaf and plant characteristics, canopy biomass, soil features, surface water and hue, intensity, and saturation values; 4) prediction of A. saligna distribution and identification of the most important spectral variables discriminating IAP occurrence using a fandom forest (RF) model. RF models calibrated for both satellite platforms showed high predictive performances (R2 > 0.6; RMSE <0.008), with accurate spatially explicit predictions of the invaded areas. While Sentinel-2 performed slightly better, the PlanetScope-based model effectively delineated invaded area edges and small patches. The summer leaf chlorophyll content followed by soil spectral variables was regarded as the most important variables discriminating A. saligna patches from native vegetation. Such variables depicted the characteristic IAP phenology and typically altered leaf litter and soil organic matter of invaded patches. Overall, we presented new evidence of the importance of VHR UAV data to fill the gap between field observation of A. saligna and satellite data, offering new tools for detecting and monitoring non-native tree spread in a cost-effective and timely manner.

Mapping of Rumex obtusifolius in nature conservation areas using very high resolution UAV imagery and deep learning

Rumex obtusifolius (Rumex or broad leaved dock) is one of the most common weeds in grasslands. It spreads quickly, lowers the nutritional value of the grass, and is poisonous for livestock due to its oxalic acid content. Mapping it is important before any control treatment is applied. Current methods for mapping Rumex either involve manual work or the utilization of ground robots, which are not efficient in large fields. This study investigated the feasibility of using aerial images from unmanned aerial vehicles (UAV) and deep learning to map Rumex in grasslands. Seven pre-trained CNN models were tested using transfer learning on UAV images acquired at 10 m, 15 m, and 30 m height. Based on Cross Validation results, MobileNet performed the best in detecting Rumex, with an F1-Score of 78.36% and an AUROC of 93.74%, at 10 m height. At 15 m, the detection performance was relatively lower (F1-score = 72.00%, AUROC = 88.67%), but the results showed that the performance can increase with more data. Experiments also showed that Rumex detection was dependent on the flight height since the algorithm was unable to detect the plants at 30 m height. The code and the datasets used in this work were released in an open access repository to contribute to the advances in grassland management using UAV technology.

Applying convolutional neural networks for detecting wheat stripe rust transmission centers under complex field conditions using RGB-based high spatial resolution images from UAVs

The use of unmanned aerial vehicle (UAV) provide a timely and low-cost means of accessing high spatial resolution imagery for crop disease detection. In this study, convolutional neural networks (CNNs) and RGB-based high spatial resolution images from UAVs were explored to detect wheat stripe rust transmission centers (Infected area accounted less than 1.35 %) occurrence in complex fields conditions in Hubei, China. To take full advantage of end-to-end learning capabilities, CNNs semantic segmentation architecture (deeplabv3+) was applied to per-pixel classify the imagery for the detection of healthy wheat and stripe-rust-infected wheat (SRIW). Using a rich dataset with diverse field conditions and sunlight illumination properties, we were able to accurately detect SRIW (Rust class F1 = 0.81). The study also evaluated the impact of classification framework and spatial resolution on model training. It revealed that the model accuracies improved for the rust class when the multi-branching binary framework instead of the multi-classification framework for CNN training with unbalanced classes. A coarser spatial resolution (8 cm) significantly decreased the model accuracy (Rust class F1-score). In addition, the Macro-disease index(MDI) was defined to quantitatively measure the occurrence of SRIW. Our results demonstrate the capability of ultra-high spatial resolution UAV imaging in detecting SRIW. With the end-to-end deep learning segmentation method greatly reducing the need for intensive preprocessing, the combination of CNNs and RGB-based ultra-high spatial resolution images from UAVs provides a simple and rapid method for accurate detection of crop disease on a large scale.

Off-Road Navigation Maps for Robotic Platforms using Convolutional Neural Networks

Abstract. As part of AMADEE-20, an integrated Mars analog field mission in the Negev Desert in Israel conducted by the Austrian Space Forum, an exploration cascade for the remote sensing of extraterrestrial terrain was implemented. For this purpose, aerial robots were conceptualized, which were used in an iterative process to generate a navigational map for an autonomous ground vehicle. This work presents the process for generating navigation maps using multiple aerial image sources from satellites as well as from low orbiting aerial vehicles. First, Deep Learning methods are used to analyze a high altitude aerial images of a large area, creating a basis map for mission planning and navigation. Second, high resolution unmanned aerial vehicle (UAV) images were recorded on low altitude for a pre-defined area of interest, processed with Deep-Learning and Structure from Motion and used to update the basis map. This approach results in a high accuracy navigation map for autonomous, off-road robot navigation. Experiments during the AMADEE-20 mission in the Israeli Negev Desert validated the proposed methods by sending an autonomous ground vehicle through the environment using the generated map.

Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles

Arctic vegetation communities are rapidly changing with climate warming, which impacts wildlife, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but scale mismatches between field and satellite-based monitoring cause challenges. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite-based mapping. We assess the viability of using high resolution UAV imagery and UAV-derived Structure from Motion (SfM) to predict cover, height and aboveground biomass (henceforth biomass) of Arctic plant functional types (PFTs) across a range of vegetation community types. We classified imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Predicted values were compared to field estimates to assess results. Cover was estimated with root-mean-square error (RMSE) 6.29-14.2% and height was estimated with RMSE 3.29-10.5 cm, depending on the PFT. Total aboveground biomass was predicted with RMSE 220.5 g m<sup>-2</sup>, and per-PFT RMSE ranged from 17.14-164.3 g m<sup>-2</sup>. Deciduous and evergreen shrub biomass was predicted most accurately, followed by lichen, graminoid, and forb biomass. Our results demonstrate the effectiveness of using UAVs to map PFT biomass, which provides a link towards improved mapping of PFTs across large areas using earth observation satellite imagery.

How Can We Understand the Past from Now On? Three-Dimensional Modelling and Landscape Reconstruction of the Shuanghuaishu Site in the Central Plains of China

The Shuanghuaishu (SHS) site in China is one of the 100 most important archaeological discoveries over the past 100 years; its historical heritage can be traced directly back 5300 years. Understanding the early landscape of the site would provide important information about the origin of Chinese civilization. The SHS site is buried and surface traces are difficult to see; therefore, we attempted to reconstruct the early landscape of the site based on a current surface landscape model and environmental archaeological analysis. We created a modern three-dimensional (3D) landscape model of the study area from high spatial resolution unmanned aerial vehicle (UAV) aerial photographs and analysed the distance change between the Yellow River and SHS site in the past 60 years from CORONA and Landsat images. By combining environmental archaeological survey results, archaeological excavation data, relevant papers, and field measurements, we reconstructed the paleotopography of the SHS site during the Yangshao period (7000–5000 aBP). On this basis, 3D natural and human landscapes during the Yangshao period were rebuilt. The results show that (1) Satellite images acquired at different resolutions can provide multiscale spatial information about the site, and high-precision models of current conditions can be quickly generated from UAV aerial photography. (2) From 1960 to 2020, the shortest distance between the SHS site and the Yellow River was approximately 512 m. The location of bedrock on Mang Mountain can be used to infer the early extent of the northern terrace at the site. (3) Environmental archaeology provided information about the palaeoenvironment of the site area. By incorporating spatial information technology and 3D visualization, we can better restore the early landscape of the SHS site. Our work integrates environmental archaeology, field archaeology, and spatial technology, enabling data and modelling support for the visual interpretation of the SHS site.

Color Balance Method of Dense Point Cloud in Landslides Area Based on UAV Images

Landslides are a natural geological phenomenon and often cause economic losses, property damage, and loss of lives. The detection and recognition based on the dense point cloud derived from Unmanned Aerial Vehicle (UAVs) images play an important role in understanding the mechanism of landslides. However, the brightness difference of UAV images with large altitude differences usually makes the dense point cloud within a landslide area show color stratification, and the processing results of popular commercial and open-source software are not ideal. In this study, a multi-view UAV image brightness compensation method based on regular 3D points is proposed, and is combined with the open-source software OpenMVG and OpenMVS to obtain a dense point cloud with balanced color on the landslide’s surface. The selected study area is located in the active landslide region caused by the Tengqing coal mining area in Guizhou Province, China. The terrain following flight method was used to acquire complete high resolution UAV images of the study area. Experimental results show that the global gain and offset compensation methods proposed in this study can effectively reduce the 22.3% brightness difference in the overlapping area of the adjacent images. The comparison results and the processing results of other commercial software show that the method can be applied to the color balance of the dense point cloud for UAV images with large-altitude differences.

Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images

Monitoring crops and weeds is a major challenge in agriculture and food production today. Weeds compete directly with crops for moisture, nutrients, and sunlight. They therefore have a significant negative impact on crop yield if not sufficiently controlled. Weed detection and mapping is an essential step in weed control. Many existing research studies recognize the importance of remote sensing systems and machine learning algorithms in weed management. Deep learning approaches have shown good performance in many agriculture-related remote sensing tasks, such as plant classification, disease detection, etc. However, despite the success of these approaches, they still face many challenges such as high computation cost, the need of large labelled datasets, intra-class discrimination (in growing phase weeds and crops share many attributes similarity as color, texture, and shape), etc. This paper aims to show that the attention-based deep network is a promising approach to address the forementioned problems, in the context of weeds and crops recognition with drone system. The specific objective of this study was to investigate visual transformers (ViT) and apply them to plant classification in Unmanned Aerial Vehicles (UAV) images. Data were collected using a high-resolution camera mounted on a UAV, which was deployed in beet, parsley and spinach fields. The acquired data were augmented to build larger dataset, since ViT requires large sample sets for better performance, we also adopted the transfer learning strategy. Experiments were set out to assess the effect of training and validation dataset size, as well as the effect of increasing the test set while reducing the training set. The results show that with a small labeled training dataset, the ViT models outperform state-of-the-art models such as EfficientNet and ResNet. The results of this study are promising and show the potential of ViT to be applied to a wide range of remote sensing image analysis tasks.

Road marking extraction in UAV imagery using attentive capsule feature pyramid network

Accurately and precisely delineating road-markings from very high spatial resolution unmanned aerial vehicle (UAV) images face many challenges, such as complex scenarios, diverse road marking sizes and shapes, and absent and occluded road markings. To address these issues, we formulate an attentive capsule feature pyramid network (ACapsFPN) by integrating capsule representations with attention mechanisms into the feature pyramid network (FPN), aiming at improving road marking extraction accuracy. Different from the current convolutional neural network (CNN) models based on scalar neuron representations, capsule networks characterize entity features by leveraging vectorial capsule neurons, whose lengths and instantiation parameters contribute to the identification of features and their variants. By constructing a capsule FPN, the ACapsFPN is capable of extracting and integrating multi-level and multi-scale capsule features to provide high-quality and semantically-strong feature abstractions. By formulating a multi-scale context feature descriptor and the ternary feature attention modules, the ACapsFPN can emphasize informative features to generate a class-specific feature representation. Quantitative and qualitative evaluations show the ACapsFPN provides a valuable means for extracting road markings in UAV images under different kinds of complex conditions. In addition, comparative analyses with existing alternatives also demonstrate the superiority and robustness of the ACapsFPN in UAV road marking extraction.

基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于无人机影像的阿拉善东南部荒漠灌丛植被空间格局研究 DOI: 10.5846/stxb202109142585 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41771101）；中国科学院"一带一路"生态水文学团队项目（Y929731） Spatial pattern of shrub in southeast Alxa Desert based on high resolution unmanned aerial vehicle images Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱区灌丛植被空间格局受多种物理和生态过程影响，能够指示生态系统的状态。研究通过量化灌丛斑块大小的空间分布来评估阿拉善高原东南部覆沙荒漠植被生态系统的状态，采用点格局分析法分析灌木种群的相互关系，以阐明不同灌木种在斑块格局形成中的作用，并结合土壤条件及下垫面粗糙度等指标验证评估的准确性，探讨灌丛空间格局差异的内在机理。结果表明，研究区样方2灌丛斑块大小符合截尾幂律分布，其他样方符合对数正态分布，前者的空间结构及生境条件均优于后者，说明植被空间格局可以准确表征生态系统状态。在局地尺度上灌木种内和种间呈现不同的相互关系，以竞争关系为主导是导致斑块破碎化的主要驱动机制。小灌木（如猫头刺）的种内互利关系有利于促进多样化斑块形态的形成，而大灌木（如沙冬青和蒙古扁桃）种间的互利作用则有利于形成异质性更强的复杂空间格局。基于灌丛斑块的空间格局评估生态系统状态，可为保护和恢复生态脆弱区受损植被提供重要的借鉴。 Abstract:The vegetation spatial pattern in drylands, which is dictated by various physical and ecological processes, could be interpreted as an indicator of the ecosystem state. According to the statistical analysis of shrub patch size distribution, we estimated the ecosystem state of desert vegetation in the southeast of the Alxa Plateau. The interactions among shrub populations were shown by point pattern analysis, to illustrate that different species might act as various ingredients in patchiness. Soil properties and roughness of underlying surface were surveyed to support the estimation. Ultimately, the internal mechanism of the differentiation of shrub spatial pattern was discussed. The results show that the truncated power law is the best fitting model for the patch sizes distribution in quadrat 2, while the lognormal distribution is the best for others. Assessment of ecosystem states is verified by the comparation of habitat conditions, which shows that quadrat 2 is better too. The interaction at small scales between shrubs varies with different species, competition dominated relationship is the main reason for the narrowing down of patch size distribution. The positive relationship within species of small patch size, such as Oxytropis aciphylla, is conducive to the formation of diversified patches, whereas species of large patch size with facilitation at small scales, such as Ammopiptanthus mongolicus and Prunus mongolica, give rise to a complex pattern. Sound management should consider to comprise assessment of ecosystem states through shrub spatial patterns, which will provide indications of conservation and restoration of vulnerable vegetation. 参考文献 相似文献 引证文献