remote sensing

摘要： Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.

摘要： The management of forest corridors and related ecology is one of the effective strategies to minimize the adverse effects of forest degradation. It controls the connectivity of inhabitant species and the connection of the isolated patches. This study analyzed spatial and temporal forest physical degradation based on forest cover change and forest fragmentation in the Gishwati-Mukura biological corridor from 1990-2019. Remotely sensed datasets, Geographical Information System (GIS) and FRAGSTATS software were used to analyze the spatial and temporal physical degradation and changes in forest cover. The results indicated that the Gishwati-Mukura corridor experienced massive deforestation where approximately 7617.1 ha (64.22%) of forest cover was completely cleared out, which implies an annual forest loss of 262.6 ha·year-1 (2.21%) during 1990-2019. The forest cover transitions patterns and geostatistical analysis indicated that extensive deforestation was associated with intensive agriculture. The results demonstrated that agriculture has dramatically increased from 29.46% in 1990 to 57.22% in 2019, with an annual increase of 1.97%. Since Gishwati-Mukura has changed to National Park (NP), it lacks diversified scientific studies addressing the analysis of the remote and spatial patterns to investigate its physical degradation and landscape dynamics. This research study will serve as remote forest analysis gap-filling and as the cornerstone of numerous other research that will contribute to the improvement of the connectivity assessments along the Gishwati-Mukura corridor and other related ecosystems.

摘要： Glaciers are highly sensitive to climate change and are undergoing significant changes in mid-latitudes.In this study,we analyzed the spatiotemporal changes of typical glaciers and their responses to climate change in the period of 1990-2015 in 4 different mountainous sub-regions in Xinjiang Uygur Autonomous Region of Northwest China:the Bogda Peak and Karlik Mountain sub-regions in the Tianshan Mountains;the Yinsugaiti Glacier sub-region in the Karakorum Mountains;and the Youyi Peak sub-region in the Altay Mountains.The standardized snow cover index(NDSI)and correlation analysis were used to reveal the glacier area changes in the 4 sub-regions from 1990 to 2015.Glacial areas in the Bogda Peak,Karlik Mountain,Yinsugaiti Glacier,and Youyi Peak sub-regions in the period of 1990-2015 decreased by 57.7,369.1,369.1,and 170.4 km^(2),respectively.Analysis of glacier area center of gravity showed that quadrant changes of glacier areas in the 4 sub-regions moved towards the origin.Glacier area on the south aspect of the Karlik Mountain sub-region was larger than that on the north aspect,while glacier areas on the north aspect of the other 3 sub-regions were larger than those on the south aspect.Increased precipitation in the Karlik Mountain sub-region inhibited the retreat of glaciers to a certain extent.However,glacier area changes in the Bogda Peak and Youyi Peak sub-regions were not sensitive to the increased precipitation.On a seasonal time scale,glacier area changes in the Bogda Peak,Karlik Mountain,Yinsugaiti Glacier,and Youyi Peak sub-regions were mainly caused by accumulated temperature in the wet season;on an annual time scale,the correlation coefficient between glacier area and annual average temperature was-0.72 and passed the significance test at P<0.05 level in the Karlik Mountain sub-region.The findings of this study can provide a scientific basis for water resources management in the arid and semi-arid regions of Northwest China in the context of global warming.

摘要： In forest modeling to estimate the volume of wood,artificial intelligence has been shown to be quite effi-cient,especially using artificial neural networks(ANNs).Here we tested whether diameter at breast height(DBH)and the total plant height(Ht)of eucalyptus can be pre-dicted at the stand level using spectral bands measured by an unmanned aerial vehicle(UAV)multispectral sensor and vegetation indices.To do so,using the data obtained by the UAV as input variables,we tested different configurations(number of hidden layers and number of neurons in each layer)of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species.The experimental design was randomized blocks with four replicates,with 20 trees in each experimental plot.The treatments comprised five Eucalyptus species(E.camaldulensis,E.uroplylla,E.saligna,E.gran-dis,and E.urograndis)and Corymbria citriodora.DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV,so that the multispectral sensor could obtain spectral bands to calculate vegetation indices(VIs).ANNs were then constructed using spectral bands and VIs as input layers,in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first appli-cations of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher esti-mated r, lower estimated root mean squared error-RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the gener-ated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

摘要： The goal of this study is to spatially portray Taif’s urban expansion and determine for last 30 years, from 1990 to 2020. It is only including the residential neighborhoods approved by the Taif Municipality, which is responsible and organized for urban planning in the city. The geographical location of the city of Taif is a vital crossroad between eastern and western parts of the Kingdom of Saudi Arabia, which made it a tourist destination, as well as commercial and agricultural preference for many years, as it was considered the summer capital of the KSA. Moreover, it serves as the entrance to Makkah city from the eastern side. The proposed study has necessitated because the lack of recent scientific studies that dealt with the spatial analysis of urban expansion and its trends in the city of Taif and follow the stages of expansion during periods of time by relying on remote sensing and geographic information systems (GIS) techniques. The many development projects in the city of Taif, such as Taif International Airport, the new Taif project, and other projects, which will cause an increase in demand for residential, commercial, industrial and service units have also prompted the proposed study. This was investigated using a multitemporal Landsat data for the years of 1990, 2002 and 2020, as well as census data from 1990 to 2020, along with Remote Sensing (RS) and Geographic Information System (GIS) techniques. The results revealed that over the last 30 years, urban land cover has increased by 20,448 (ha) whereas other land covers, such as green area, have decreased significantly by 14,554 (ha). The results also indicate that the increase in urban areas amounted to 114.8% during the period from 1990 to 2020. The locations of new developments such as Taif airport, Taif university, Ministry of Housing projects, etc. were located to the North and Northeast. This is due to the area’s topography, which played a major role in determining the direction of urban expansion. According to the study, multiple urban centers, rising low-density dispersed communities, and leapfrogging growth were all hallmarks of urban expansion in Taif. The study demonstrated that Taif is at risk of ecosystem loss as a result of continued urban expansion. To ensure environmental sustainability, the current effort asks for actions that will restrict urban sprawl and prepare the city for future growth.

摘要： Background:Trees and forests in drylands help mitigate the challenges through provision of economic products and vital environmental services such as habitat for biodiversity,prevention of erosion and desertification,regulation of water,microclimate,and soil fertility.The condition and changes in dry forests can be assessed by using ecological indicators able to quantify spatial and temporal changes in vegetation.One of the ways to determine the condition of the forest is to study the dominant tree species and its regeneration.Our study aimed to assess whether the abundance of Prosopis flexuosa saplings is affected by environmental and biological factors.Results:To evaluate the first variables we used data from remote sensing such as satellite images and Aster Global Digital Model(GDEM).The second set of variables was about exotic and native ungulates and we used feces of these animals and camera traps to take data.We found that sapling abundance related positively to sandy substrates and negatively to Wetness Index.On the other hand,in relation to biological variables,the abundance of saplings was positively affected by density of adult trees and by number of seeds dispersed by equines,but space use by Lama guanicoe had a negative relationship with saplings.This research shows that P.flexuosa saplings are benefited from sandy substrates and the conditions around adult trees.In addition to this,we found that exotic ungulates in low densities have neutral(i.e.cattle)or positive(i.e.equines)effects on sapling abundance.Conclusions:Based on these findings,we conclude that regeneration of the population of P.flexuosa in our study area has no major problems.In addition,we corroborated that the presence of exotic and domestic ungulates in low densities does not have deleterious consequences for saplings of the dominant tree,P.flexuosa.

摘要： Target recognition based on deep learning relies on a large quantity of samples,but in some specific remote sensing scenes,the samples are very rare.Currently,few-shot learning can obtain high-performance target classification models using only a few samples,but most researches are based on the natural scene.Therefore,this paper proposes a metric-based few-shot classification technology in remote sensing.First,we constructed a dataset(RSD-FSC)for few-shot classification in remote sensing,which contained 21 classes typical target sample slices of remote sensing images.Second,based on metric learning,a k-nearest neighbor classification network is proposed,to find multiple training samples similar to the testing target,and then the similarity between the testing target and multiple similar samples is calculated to classify the testing target.Finally,the 5-way 1-shot,5-way 5-shot and 5-way 10-shot experiments are conducted to improve the generalization of the model on few-shot classification tasks.The experimental results show that for the newly emerged classes few-shot samples,when the number of training samples is 1,5 and 10,the average accuracy of target recognition can reach 59.134%,82.553%and 87.796%,respectively.It demonstrates that our proposed method can resolve few-shot classification in remote sensing image and perform better than other few-shot classification methods.

摘要： Crop planting patterns are an important component of agricultural land systems.These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments.However,the extent of these changes and their possible impacts on the environment,terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns.To fill this gap,this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets.This method features a two-level model that combines a land-use simulation and a crop pattern simulation.The output of the first level is the spatial distribution of the cropland,which is then used as the input for the second level,which allocates crop censuses to individual gridded cells according to certain rules.The method was tested using data from 2000 to 2019 from Heilongjiang Province,China,and was validated using remote sensing images.The results show that this method has high accuracy for crop area spatialization.Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

摘要： In the north Indian Ocean (NIO), maps of sea level anomaly from satellite altimetry were analysed from January-1995 to December-2000. The study attempted to trace the trajectories of the individual mesoscale anomalies manually and to understand seasonal changes in terms of phase speed. Mesoscale anomalies are detected as concentric circular shapes and diameters of ~90 km to 600 km and the minimum 30 days life cycle. Relatively higher eddy kinetic energy was noticed in the northwestern region of the NIO. Individual mesoscale anomalies, namely positive (warm, anticyclonic eddies) and negative (cold, cyclonic eddies) showing travelling direction westward in the NIO basins. In autumn, the number of negative anomalies detected is more than positive anomalies and vice versa during summer. The westward propagating positive (negative) anomalies in the Arabian Sea start appearing in winter (spring) along (away from) the west coast of India and west of 65°E;individual anomalies move to the west in spring/summer/autumn and collide along Somalia’s & Arabian coast. A group of positive (negative) anomalies trajectories appears as a tail at the southern tip of India are located west of the Laccadive ridge in winter (summer to autumn) associated with LH (LL). The Bay of Bengal (BB) trajectories show southwestward in northern BB, westward in central BB and northwestward in southern BB;individual anomalies are appearing along the west coast of Andaman & Nicobar ridge. The zonal phase speed decreases away from the equator, and the magnitude varies longitudinally in each season in the form of a wave-like pattern propagating westward from autumn to summer;the life cycle of the wave is almost 365 days (a year). The theoretical phase speed of the first mode of the baroclinic Rossby waves is quite similar to that of averaged zonal speed. Therefore mesoscale anomalies (eddies) are embedded into the large waves like phenomenon (Rossby waves), responsible for creating high variability and EKE in the region of NIO along the western boundaries.

摘要： To obtain a real-time image of atmospheric particulate matter (PM) in highly polluted areas and to understand how the anthropogenic component linked to urban activities (industrial activities, domestic heating, road traffic, waste disposal) can locally affect near-surface measurement of PM, several measurement campaigns were achieved in the Campania region (Southern Italy) using both Lidar and in-situ instruments. A comparison between the obtained results highlights a good correlation between the data and the potential of remote sensing instruments for air quality monitoring. Data analysis was performed in terms of particle backscattering coefficient profile at 355 nm, PM mass concentration, and size distribution. Wind profiles, which covered a range of altitudes from 40 m to 290 m, were also used to study sources and physical processes involved. Measurement carried out in a rural area with a landfill site highlighted the presence of a homogeneous particulate layer throughout the sounded area due to winds driving aerosol from the landfill to the surrounding areas. The size distribution in mass concentration, highlighted a modal diameter moving towards 0.9 and 2 μm with a larger mass concentration of particles in the morning, before noon and in the afternoon when a large number of trucks delivered solid wastes. Moreover, large concentrations of particulate matter were measured in a small urban town with few industrial activities which peak (211 ± 33 μg·m-3) was measured in the direction of the most urbanized area, probably due to the lighting of the domestic heating systems. Bimodal size distribution in number concentration was measured, indicative of two types of atmospheric particles sources: gas and liquid combustion (particles with sizes below 80 nm), including vehicular traffic and domestic gas-heating, and biomass combustion (particles with sizes of the order of 200 - 500 nm). Finally, data collected in a highly populated and industrialized area highlights the presence of particles having a high level of spherical geometry (aerosol depolarization below 5%) pointing towards the industrial area. Conversely, the measurements performed pointing toward other directions highlighted a diffused source of aspherical particles (depolarization values of about 3%) spreading throughout all city territory. The work showed as the co-location of remote sensing and near surface instruments is a promising approach to studying aerosol properties in the atmospheric layers and has more accurate information on atmospheric dynamics. Moreover, the correlation between the obtained results highlighted the potential of remote sensing instruments for air quality monitoring.