Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

Estimation and verification of green tide biomass based on UAV remote sensing

Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.

Remote sensing image encryption algorithm based on novel hyperchaos and an elliptic curve cryptosystem

Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.

Rapid identification of landslide,collapse and crack based on low-altitude remote sensing image of UAV

Landslides,collapses and cracks are the main types of geological hazards,which threaten the safety of human life and property at all times.In emergency surveying and mapping,it is timeconsuming and laborious to use the method of field artificial investigation and recognition and using satellite image to identify ground hazards,there are some problems,such as time lag,low resolution,and difficult to select the map on demand.In this paper,a10 cm per pixel resolution photogrammetry of a geological hazard-prone area of Taohuagou,Shanxi Province,China is carried out by DJ 4 UAV.The digital orthophoto model(DOM),digital surface model(DSM) and three-dimensional point cloud model(3 DPCM) are generated in this region.The method of visual interpretation of cracks based on DOM(as main)-3 DPCM(as auxiliary) and landslide and collapse based on 3 DPCM(as main)-DOM and DSM(as auxiliary) are proposed.Based on the low altitude remote sensing image of UAV,the shape characteristics,geological characteristics and distribution of the identified hazards are analyzed.The results show that using UAV low altitude remote sensing image,the method of combination of main and auxiliary data can quickly and accurately identify landslide,collapse and crack,the accuracy of crack identification is 93%,and the accuracy of landslide and collapse identification is 100%.It mainly occurs in silty clay and mudstone geology and is greatly affected by slope foot excavation.This study can play a great role in the recognition of sudden hazards by low altitude remote sensing images of UAV.

Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

Relationship between Vegetation Index and Forest Surface Fuel Load in UAV Multispectral Remote Sensing

[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.

VTOL UAVs for Forest Monitoring and Management via Remote Sensing: A Case Study in Khao Yai National Park, Thailand

The utilization of UAVs (Unmanned Aerial Vehicles) has experienced a remarkable upsurge in various industries, including forestry. Their capacity to expeditiously and effectively cover large tracts of land has resulted in their widespread adoption as a valuable forest management and monitoring tool. The versatility of UAVs extends to their capability to perform quick and efficient surveys of large areas, inventory of tree species, and monitoring of forest health. This research paper reports on the successful utilization of VTOL (Vertical Takeoff and Landing) UAV that was designed and built at the IESSD (Institute of Earth Science and Sustainable Development) located in the AAA (Asia Aviation Academy) at KMITL (King Mongkut’s Institute of Technology Ladkrabang) Prince of Chumphon Campus, Thailand. The VTOL UAV is employed for resource and environmental missions, as well as forest monitoring by using remote sensing technology. VTOL UAVs are used for aerial surveillance to conduct air photography, data collection, and processing for resource and environmental missions. This research paper presents a comprehensive analysis of the areas at risk of deforestation and forest encroachment in a particular region of Khao Yai National Park in Thailand, highlighting the potential for the resulting photographs to inform evidence-based decision-making and facilitate sustainable forest management practices. This study offers recommendations to develop VTOL UAVs remote sensing capabilities and mitigate deforestation and forest encroachment in Khao Yai National Park.

Drone remote sensing for forestry research and practices

Drones of various shapes, sizes, and functionalities have emerged over the past few decades, and their civilian applications are becoming increasingly appealing. Flexible, low-cost, and high-resolution remote sensing systems that use drones as platforms are important for filling data gaps and supplementing the capabilities of crewed/manned aircraft and satellite remote sensing systems. Here, we refer to this growing remote sensing ini- tiative as drone remote sensing and explain its unique advantages in forestry research and practices. Furthermore, we summarize the various approaches of drone remote sensing to surveying forests, mapping canopy gaps, mea- suring forest canopy height, tracking forest wildfires, and supporting intensive forest management. The benefits of drone remote sensing include low material and operational costs, flexible control of spatial and temporal resolution, high-intensity data collection, and the absence of risk to crews. The current forestry applications of drone remote sensing are still at an experimental stage, but they are expected to expand rapidly. To better guide the development of drone remote sensing for sustainable forestry, it isimportant to systematically and continuously conduct comparative studies to determine the appropriate drone remote sensing technologies for various forest conditions and/or forestry applications.

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.

Unmanned Aerial Vehicles in Environmental Monitoring——An Efficient Way for Remote Sensing

Airborne photographs can be expediently used in environmental monitoring; e.g., in the surveillance of the state of natural reserve areas, such as wetlands; or in the measurement and mapping of pollutants, such as oil spills on a lake or sea. A new and cost-effective platform of airborne remote sensing is the UAV （unmanned aerial vehicle） or drone. In this experimental work, aerial photos were made in Bakony Mountains using three UAVs equipped with small HD （high definition） cameras; resolution： 1,280 ~ 720 pixels. Within the framework of this work, a small lake was photographed, where the beginning of eutrophication was detected. This hardly can be observed from ground, however, it is visible on the aerial photos. The airborne surveillance of areas of ragweed （Ambrosia sp.） populations was also investigated. It was found that both UAVs are feasible for these tasks, and the application of these platforms for environmental monitoring is advantageous, especially in case of natural reserve areas since those are very silent and--contrary to big aircrafts and helicopters---do not disturb the ecology even in natural reserve areas and the people living there. Moreover, those could be operated in a very flexible and economic way, and the aerial photos taken are highly informative.

Optimization of Multi-Level Cooperative Remote Sensing Using Carriers Flying on Counter-Phase Trajectories

The criterion for effectiveness of remote sensing system forming the image by the aim: a) further processing;b) detection of temporal changes or differences in spectral bands;accumulation and registration in GIS system is formulated. The optimal point in trajectory, where the maximum effectiveness of system’s operation of maximum information entropy at the system output is calculated. The regime limit value of parameters are calculated, upon surpassing of which the maximal effectiveness of the system may be reached.

UAV and Satellite-Based Sensing to Map Ecological States at the Landscape Scale

Mapping ecological states in semi-arid rangelands is crucial for effective land management and conservation efforts because it identifies difference in the ecological conditions across a landscape. This study presents an innovative approach for mapping two ecological states, Large Shrub Grass (LSG) and Large Shrub Eroded (LSE), within the Sandy Loam Upland and Deep (SLUD) ecological sites using a combination of drone and satellite data. The methodology leverages the Largest Patch Index (LPI) as a proxy metric to estimate eroded areas and classify ecological states. The integration of unmanned aerial vehicle (UAV) data with satellite-based remote sensing provides a scalable approach that can benefit various stakeholders involved in rangeland management. The study demonstrates the potential of this methodology by generating spatial layers at the landscape scale to inform on the state of rangeland ecosystems. The workflow showcases the power of remote sensing technology to map ecological states and addresses limitations in spatial coverage by integrating UAV and satellite data. By utilizing the bare ground LPI metric, which indicates the connectedness of bare ground, the methodology enables the classification of ecological states at a regional scale. This cost-effective approach potentially offers a standardized and reproducible method applicable across different sites and regions. The accuracy of the classification process is evaluated by comparing the results to ground-based polygons, dirt roads, and water locations. While the model performs well in identifying eroded areas, misclassifications occur in regions with mixed vegetation cover or low biomass. Future research should focus on incorporating temporal information from historical remote sensing archives to improve understanding of ecological state dynamics. Additionally, validation efforts can be enhanced by incorporating more ground-truth data and testing the methodology in diverse rangeland areas. The workflow serves as a blueprint for scaling up ecological states mapping in similar semi-arid rangelands. Further work should involve refining the approach through additional validation and exploring new remote sensing datasets. The methodology can be replicated in other regions to inform land management decisions, promote sustainable resource use, and advance the field of ecological states mapping.

The potential of remote sensing and GIS in urban building energy modelling

As the world continues to urbanize at an unprecedented rate,the energy demand in cities is rising.Buildings account for over 75%of all the energy consumed in cities and are responsible for over two-thirds of the emissions.Assessment of energy demand in buildings is a highly integrative endeavour,bringing together the interdisciplinary fields of energy and urban studies,along with a host of technical domains namely,geography,engineering,economics,sociology,and planning.In the last decade,several urban building energy modelling tools(UBEMs)have been developed for estimation as well as prediction of energy demand in cities.These models are useful in policymaking as they can evaluate future urban energy scenarios.However,data acquisition for generating the input database for UBEM has been a major challenge.In this review,a comprehensive assessment of the potential of remote sensing and GIS techniques for UBEM has been presented.Firstly,the most common input variables of UBEM have been identified by reviewing recent publications on UBEM and then studies related to the acquisition of data corresponding to these variables have been explored.More than 140 research papers and review articles relevant to remote sensing and GIS applications for building level data extraction in urban areas and UBEM applications have been investigated for this purpose.After going through level of details required for each of the input components of UBEM and studying the possibility of acquiring some of those data using remote sensing,it has been inferred that satellite remote sensing and Unmanned Aerial Vehicles(UAVs)have a strong potential in enhancing the input data space for UBEM but their applicability has been limited.Further,the challenges of the usage of these technologies and the possible solutions have also been presented in this study.It is recommended to utilise the existing methodologies of extracting information from remote sensing and GIS for UBEM,along with newer techniques such as machine learning and artificial intelligence.

Radiative transfer models(RTMs)for field phenotyping inversion of rice based on UAV hyperspectral remote sensing

The nondestructive and rapid acquisition of rice field phenotyping information is very important for the precision management of the rice growth process.In this research,the phenotyping information LAI(leaf area index),leaf chlorophyll content(C_(ab)),canopy water content(C_(w)),and dry matter content(C_(dm))of rice was inversed based on the hyperspectral remote sensing technology of an unmanned aerial vehicle(UAV).The improved Sobol global sensitivity analysis(GSA)method was used to analyze the input parameters of the PROSAIL model in the spectral band range of 400-1100 nm,which was obtained by hyperspectral remote sensing by the UAV.The results show that C_(ab) mainly affects the spectrum on 400-780 nm band,C_(dm) on 760-1000 nm band,C_(w) on 900-1100 nm band,and LAI on the entire band.The hyperspectral data of the 400-1100 nm band of the rice canopy were acquired by using the M600 UAV remote sensing platform,and the radiance calibration was converted to the canopy emission rate.In combination with the PROSAIL model,the particle swarm optimization algorithm was used to retrieve rice phenotyping information by constructing the cost function.The results showed the following:(1)an accuracy of R^(2)=0.833 and RMSE=0.0969,where RMSE denotes root-mean-square error,was obtained for C_(ab) retrieval;R^(2)=0.816 and RMSE=0.1012 for LAI inversion;R^(2)=0.793 and RMSE=0.1084 for C_(dm);and R^(2)=0.665 and RMSE=0.1325 for C_(w).The C_(w) inversion accuracy was not particularly high.(2)The same band will be affected by multiple parameters at the same time.(3)This study adopted the rice phenotyping information inversion method to expand the rice hyperspectral information acquisition field of a UAV based on the phenotypic information retrieval accuracy using a high level of field spectral radiometric accuracy.The inversion method featured a good mechanism,high universality,and easy implementation,which can provide a reference for nondestructive and rapid inversion of rice biochemical parameters using UAV hyperspectral remote sensing.

Development of a low-cost quadrotor UAV based on ADRC for agricultural remote sensing

Unmanned aerial vehicle(UAV)has the advantages of good repeatability and high remote sensing(RS)information acquisition efficiency,as an important supplement bridging the gap of high-altitude and ground RS platforms.A quadrotor UAV was developed for the agricultural RS application in this study.The control system consists of a main processor and a coprocessor,integrating a three-axis gyroscope,a three-axis accelerometer,an air pressure sensor and a global positioning system(GPS)module.Engineering trial method(ETM)was used to tune the parameters based on the active disturbance rejection control(ADRC)method.Also a ground control station(GCS)adapted to the quadrotor was developed realizing autonomously take-off and landing,flight route planning,data recording.To investigate the performances of the UAV,several flight tests were carried out.The test results showed that the pitch angle control accuracy error was less than 4°,the flight height control accuracy error was less than 0.86 m,the flight path control accuracy error was less than 1.5 m overall.Aerial multispectral images were acquired and processed.The reflected digital number(DN)values obtained from a height of 10-100 m with 10 m interval could be referenced to classify objects.The normalized-difference-vegetation index(NDVI)values obtained from the aerial multispectral images acquired at 15 m were compared with those obtained by the GreenSeeker(GS)and PSR-1100F.The maximum error was 20.37%while the minimum error was 1.99%,which demonstrated the developed quadrotor UAV’s satisfactions for low altitude remote sensing practice.This study provided a low-cost platform for agricultural remote sensing.

Classification method of cultivated land based on UAV visible light remote sensing

The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision agriculture.UAV remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground resolution.It is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical classification.In this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of farmland.Based on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land area.After repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation parameters.Uncultivated crops and other features are separated by using the band information and texture information.The overall accuracy of this method is 86.40%and the Kappa coefficient is 0.80.The experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high precision.However,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.

应用B-IBI和UAV遥感技术评价辽河上游生态健康

采用B-IBI(benthic index of biotic integrity,底栖动物完整性指数)结合UAV(unmanned aerial vehicle,无人机)遥感技术,对辽河保护区干流上游河流生态系统健康进行评价.根据16个采样点的UAV遥感正射影像、水质参数、水体理化指标和土地利用类型等信息,从中筛选出4个采样点作为参照点,其余为受损点;通过分布范围、判别能力和相关性分析,从32个候选生物指标中筛选出6个核心指标,包括总分类单元数、摇蚊分类单元数、寡毛类分类单元数、敏感类群、前3位优势分类单元和均匀度指数.B-IBI评价结果显示,辽河干流上游处于健康(B-IBI>3.90)的河段占0.82%,亚健康(2.93<B-IBI≤3.90)的河段占31.26%,一般(1.95<B-IBI≤2.93)的河段占43.43%,较差(0.98<B-IBI≤1.95)的河段占23.57%,极差(0<B-IBI≤0.98)的河段占0.92%.B-IBI与栖境质量综合评分呈显著线性正相关(R2=0.55,P<0.01).辽河干流上游底栖动物群落以中污染水体指示种为主.

矿山开采损害InSAR/UAV融合监测关键技术及应用

煤炭开采引起的地质环境损害问题已成为关注和研究的焦点;尤其西部矿区以大规模、高强度开采主,引起的岩层及地表移动也呈现出变形速度快、损害程度深、波及范围广的特点,对其进行快速、准确、全面地监测是矿区生态环境保护、绿色矿山建设的关键。传统的地表移动观测方法和单一测量技术均难以满足需求;无人机(UAV)摄影测量与合成孔径雷达干涉(InSAR)优势互补,二者融合在地质环境损害监测方面具有优势,提出了融合UAV/InSAR监测矿山开采损害的关键理论、技术和方法。重点论述了UAV/InSAR融合新型多尺度观测站建立模式和二者协同观测方法;探讨了UAV/InSAR跨尺度异质遥感数据融合策略;提出了基于UAV/InSAR特征级融合的地表沉陷变形及关键环境因素精确提取的思路和方法;并以内蒙古王家塔煤矿为例进行了应用研究,首先采用UAV摄影测量和InSAR技术建立了工作面尺度“点-线-面”结合新模式观测站,同时获得研究区域的UAV光学影像、InSAR影像和主断面关键点水准数据;利用UAV/InSAR的特征级融合方法提取了矿区沉陷盆地,并利用UAV沉陷盆地和融合沉陷盆地分别进行求参。研究结果表明:UAV监测的最大下沉值为2487 mm,中误差为81 mm,UAV测量误差对于沉陷盆地边界影响较大,导致无法精确获取盆地边界区域;InSAR监测的最大下沉为110 mm,远小于实际,InSAR受时空失相干影响,无法准确获取大变形区域沉降;通过InSAR/UAV特征级融合,得到了整体和边界区域精度更高融合下沉盆。在求参方面,单独UAV数据求取的下沉系数与水准结果的相对误差为1.4%,主要影响角正切偏差较大,约20%,主要为UAV下沉盆地边界误差较大所致。与单独UAV求参相比,融合下沉盆地数据求出的参数更准确,主要影响角正切的相对误差仅为5%,融合数据很好解决了单一UAV反演tanβ误差偏大的问题。该工程案例表明了UAV/InSAR融合技术在西部矿区地表沉陷变形监测中具有显著的优势,可为西部煤矿开采地表损害监测提供技术支撑。

基于UAV低空遥感的荒漠林大沙鼠洞群覆盖率及分布特征研究——以新疆古尔班通古特沙漠南缘局部为例

草原鼠害是影响草原生态平衡的重要因素,草原鼠害监测是鼠害防治工作的重要环节。无人机低空遥感是一种新的鼠害监测方法,其具备高空间分辨率、高时效、低成本、低损耗等特点,在新疆鼠害监测防治方法中完成了示范工作。于2015年10月与2016年5月对新疆古尔班通古特沙漠南缘的荒漠林大沙鼠典型鼠害区进行两次无人机低空航拍,分别获取分辨率0.02m与0.024m的实验区超高分辨率影像,对两片实验区整幅影像进行目视解译,获得鼠洞分布情况;在鼠洞分布图基础上进行缓冲区分析并添加趋势线,获取鼠洞空间分布趋势,与研究区山体阴影图叠加分析鼠洞分布与地形间的相互关系。通过GIS格网与GIS叠加分析,获取鼠洞洞群覆盖率。得出如下结论:无人机低空遥感可以为大沙鼠鼠害调查提供准确度甚高的解译结果;古尔班通古特沙漠南缘局部研究区的荒漠林大沙鼠鼠害区洞群覆盖率分别为19.4%、18.8%,已为高密度发生区;研究区大沙鼠鼠洞表现出明显的聚集特征和带状分布特征;聚集特征说明鼠洞是以洞群的形式存在;研究区鼠洞带状分布特征与地形有着密不可分的关系;根据其空间分布特征可以科学规划鼠害治理方案;此研究表明无人机低空遥感在鼠害监测防治方面具有广阔的应用前景。

Application of remote sensing in the description of fluvial system in dryland:A case study of Golmud distributive fluvial system,Qaidam Basin,NW China

Distributive fluvial systems(DFS)are widespread in drylands in the northwestern China.Analyzing differences in fluvial morphology in drylands is beneficial for studying ancient rock records and the extraterrestrial surface environment.The remote sensing image,characterized by real-time and possibility of repeated observations,is a vital tool for recording and comparing fluvial systems in drylands.Satellite remote sensing technology is a method of investigating fluvial morphologies.Due to the limited accuracy of satellite imagery,there are few reports on the detailed description of the fluvial system in drylands of NW China.We analyze the pattern of fluvial morphology changes in the Golmud distributive fluvial system(DFS)in the Qaidam Basin,northwestern China,using satellite remote sensing and unmanned aerial vehicles(UAV).Firstly,we use Google Earth real-time image data,historical image data,and radar digital elevation data to extract geomorphological information;then the UAV remote sensing image data were used to interpret fluvial network information;finally,we use the gray-scale differential vector method to describe the fluvial morphologies.Three zones have been identified in the Golmud DFS:the proximal,the medial,and the distal,by comparing the differences in topographic and geomorphic characteristics,fluvial morphologies,and sedimentary characteristics of the Golmud DFS.The proximal slope is higher than the other two zones,and the geomorphic features are mainly gravel gobi.The proximal fluvial morphologies are mainly large braided rivers,and sediments are more gravelly and less sandy.The medial slope is relatively small,and the geomorphic features are mostly oasis plains.The medial fluvial morphologies are mainly meandering rivers associating with braided rivers,and sediments are more sandy and less gravelly.The distal slope is the lowest,and the geomorphic features are mostly oasis plains,lakes,and marsh plains.The distal fluvial morphologies are mainly meandering rivers,and sediments are sandy and muddy.Comparison of the DFS from proximal to medial to distal in Golmud confirmed the potential of remote sensing image technology in identifying the fluvial morphologies and sedimentary facies distribution in dryland.