Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.     

Small-scale surface mining of gold placers: Detection,mapping, and temporal analysis through the use of free satellite imagery

Informal small-scale mining is spread in many countries and provides livelihood to numerous families in rural areas yet often with devastating social and environmental impacts. The alluvial gold mining process in Colombia, also known as placer mining, involves excavations using heavy machinery and creates large footprints of bare soil and mining ponds. The very dynamic nature of this extractive activity and its spread in rural and remote areas make its mapping and monitoring very challenging. The use of freely available satellite data of the Copernicus programme provides great new possibilities to study these activities and provides stakeholders integrated data to better understand the spatial and temporal extent of the activities and mitigate affected areas. The objective of this work is to assess the potential of Sentinel-2 data to identify mining areas and to understand the dynamics in landcover change over a study area located at the border of the municipalities of El Bagre and Zaragoza in Bajo Cauca, Colombia. The study utilizes a classification approach followed by post-processing using field knowledge on a set of images from 2016 to 2019. Sequential pattern mining of classified images shows the likelihood of certain annual and seasonal changes in mining-impacted landcover and in the natural vegetation. The results show a slight reduction in the detected mining areas from 2016 to 2019. On the other hand, there are more mining activities in the dry season than in the wet season. Excavated areas of bare soil have a 50% chance to remain in excavation over the considered period or they transition to non-vegetated areas or mining ponds. Vegetation loss due to the extractive activities corresponds to about 35% while recovered vegetated areas are 7% of the total excavated areas in June 2019. An analysis of abandoned sites using NDVI shows that it takes a much longer period than the one considered in this paper for potential natural recovery of vegetation. Finally, the work was disseminated among stakeholders and the public on MapX (https://mapx.org), an online open platform for mapping and visualizing geospatial data on natural resources. It is a pilot study the will be the basis of the analysis of more regions in the department of Antioquia.     

遥感技术在图们地区地质灾害危险性评价中的应用

图们市是地质灾害多发地区,这些地质灾害具有密集性、易发性、群发性和重复性的特点,对当地人民生命财产安全构成威胁,更制约着当地社会经济可持续发展。本次研究以2005年SPOT-5影像、2015年GF-2影像为数据源,选取地貌、岩土体类型、地质灾害点密度、地质构造、森林覆盖率、降雨量和人类工程活动7个评价因子,采用网格分析法获取各因子的量化分级图。通过Arc GIS的栅格计算功能,将各因子加权叠加获取危险性指数分布图,再通过surfer重分类生成研究区地质灾害危险性区划图。研究表明,图们市地质灾害危险性程度以轻度和微度为主,重度和中度区主要集中在重要交通干线和居民集中生活区,应加强危险性重度和中度区的地质灾害监测和排查工作。     

Monitoring Spatial and Temporal Land Use/Cover Changes; a Case Study in Western Black Sea Region of Turkey

Rapid land use/land cover changes have taken place in many cities of Turkey. Land use and land cover changes are essential for wide range of applications. In this study, Landsat TM satellite imageries date from 1987, 1993, 2000 and 2010 were used to analyse temporal and spatial changes in the Western Black Sea Region of Turkey. Zonguldak and Eregli two largest and economic important cities which have been active coal mining and iron fabric areas. Maximum Likelihood Classification technique was implemented and the results were represented in classes of open area, forest, agricultural, water, mining, urban and pollution in the sea. Urban areas on both cities increased from 1987 to 2010. The agricultural and open areas from 1987 to 2010 decreased in parallel to land use and land cover change in both cities. Meanwhile, forest areas increased continuously with about 20 % from 1987 to 2010 in both cities. As industrial activity, the coal fields doubled from 1987 to 2010.     

地性线的山地区域的卫星影像几何精纠正

提出了一种山地区域基于DEM地性线的控制纠正新方法,该方法以数字地形模型DEM为无几何变形的控制基准纠正卫星影像。阐述了提取沟谷、山脊、山峰和凹地区域的地性线的原理和算法,给出了山地区域基于地性线进行卫星图像几何精纠正实施步骤,进一步讨论了地性线提取、控制点采集存在的问题,以及解决问题的途径。实验结果表明,对于山地区域,地性线的空间数量数倍于水系、道路等常规地图层;地性线来源于DEM,其空间稳定性和可靠性更高,可以用于山地区域的卫星影像的严格控制纠正。用该方法进行几何纠正处理,几何误差能控制在一个像元的水平上。     

应用RS、GIS技术提取广州市绿地信息的方法

以广州市为例,以高分辨率的遥感影像及电子地图为主要数据源,以遥感图像处理软件及地理信息系统为主要分析工具,探讨了通过RS和GIS来提取绿地信息的方法,其中重点阐述了遥感影像处理、绿地信息提取及建库过程。     

Evidence of Possible Recharge Zones for Lake Salda (Turkey)

This study explores the evidence of recharge locations using hydrogeochemical and physicochemical measurements in an alkaline lake, Lake Salda, in Burdur, Turkey. In-situ measurements have been performed using a conductivity–temperature–depth device to map the physicochemical dynamic of the lake. Water and sediment samples were collected on the surface and floor of the lake. A seismic study was also carried out in order to observe the geometry of the lake floor. In addition, thermal distribution was mapped using the thermal band of Landsat 7 ETM+ and Landsat 8 satellite images. Temperature and specific conductance measurements were mapped using a new technique, Empirical Bayesian Kriging (EBK), from the lake’s surface to the floor. According to interpolation maps obtained from the EBK, possible water inputs were observed close to a fault at the south-eastern part of the lake. The results of thermal band imaging also reveal the probability of a fault effecting the recharge on the surface. The results of water and sediment samples present a richness in Mg²⁺ and Fe²⁺ elements respectively on the floor of the lake. Finally, seismic results show some possible recharge zones on the floor of the lake, and sediment results indicate that there should be peridotite occurrence below the alluvium unit.     

Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping

Abstract

The aim of this study was to determine how well the landslide susceptibility parameters, obtained by data-dependent statistical models, matched with the parameters used in the literature. In order to achieve this goal, 20 different environmental parameters were mapped in a well-studied landslide-prone area, the Asarsuyu catchment in northwest Turkey. A total of 4400 seed cells were generated from 47 different landslides and merged with different attributes of 20 different environmental causative variables into a database. In order to run a series of logistic regression models, different random landslide-free sample sets were produced and combined with seed cells. Different susceptibility maps were created with an average success rate of nearly 80%. The coherence among the models showed spatial correlations greater than 90%. Models converged in the parameter selection peculiarly, in that the same nine of 20 were chosen by different logistic regression models. Among these nine parameters, lithology, geological structure (distance/density), landcover-landuse, and slope angle were common parameters selected by both the regression models and literature. Accuracy assessment of the logistic models was assessed by absolute methods. All models were field checked with the landslides resulting from the 12 November 1999, Kayna?li Earthquake (Ms = 7.2).     

高分三号卫星全极化SAR影像九寨沟地震滑坡普查

基于光学遥感影像的区域滑坡普查易受云雾天气的影响,存在滑坡体调查不全面的问题,无法满足震后应急调查与恢复重建的需求。本文提出了一种极化SAR卫星数据滑坡普查方法,采用高分三号全极化SAR卫星影像数据,以九寨沟地震震区为实验区,在深入分析滑坡体和其他地物类型散射特征的基础上,融合极化特征、纹理特征和地形特征等多维特征信息,结合高分二号影像获取的训练样本,构建基于BP神经网络的全极化SAR数据滑坡自动识别模型,实现滑坡体的自动快速识别。与高分辨率光学影像与无人机航空影像目视解译结果相比较,总体识别精度为92.8%,Kappa系数为0.715,识别准确度满足地震应急实际应用的需求。研究成果可用于震区大区域滑坡体的普查,为后续开展无人机高分辨率影像滑坡体详查、灾后应急与景区恢复提供辅助信息支撑,并促进国产高分SAR卫星数据在防震减灾中的应用。     

Dynamics of coastal landform features along the southern Tamil Nadu of India by using remote sensing and Geographic Information System

This article reveals an application of multi-spectral satellite data for analysing the dynamics of different coastal landform features along the southern coastal Tamil Nadu of India. An integrated approach comprising visual image interpretation and maximum-likelihood supervised classification has been employed to classify the coastal landforms by using IRS data (during the period 1999–2006). The quality of image classification has been assessed by performing the accuracy assessments with the existing thematic maps and finally the coastal landforms have been mapped. The study reveals that the dynamics of coastal landforms such as sandy beaches, mud-flats, sand dunes and salt marshes along the study area are mostly influenced by the coastal processes, sediment transport, geomorphology and anthropogenic activities. Major anthropogenic sources for the perturbation of beach sediment budgets and a cause of beach erosion along the study area are excessive sand mining, removal of sand dunes, coastal urbanization, tourism and developmental activities.     

Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery

Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2–10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.     

变化检测技术及其在高分辨率卫星遥感影像中的应用

对高分辨率遥感卫星影像利用变化检测技术更新现有的影像数据库或地图数据库是一项对测绘具有重要意义的工作。对现有的变化检测技术进行了分类总结,然后提出了对高分辨率遥感卫星影像进行变化检测的实施流程,最后结合数学形态学算法对SPOT5影像(2.5 m分辨率)进行了相关实验。     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

面向区域与要素的城市1∶2000地形图数据库增量更新方法

大比例尺地形图数据库的更新是一项长期的重要任务。本文分析了1∶2000地形图数据库快速更新的难点,提出了一种航空摄影与卫星遥感、区域更新与要素更新相结合的大比例尺地形图数据库半自动快速更新方法。采用变化检测方法从更新前后卫星遥感影像中提取变化区域和变化要素,然后分别采用面向区域和面向要素的方法从高分辨率航空影像上测量变化地物,最后通过半自动空间实体匹配的方法建立现状库与历史库中要素的回溯关联,从而实现地形图数据库的半自动增量式快速更新。利用该方法对中山市东区的1∶2000地形图进行了更新试验。试验结果表明,引入卫星影像进行自动变化检测后,在航空影像上分区域和要素两种模式采集更新城市大比例尺地形图数据库,效率比传统方法提高25%。     

Assessment of Shuttle Radar Topography Mission Elevation Data Based on Topographic Maps in Turkey

Depending on scale, topographic maps depicting the shape of the land surfaces of the Earth are produced from different data sources. National topographic maps at a scale of 1:25 000 (25K maps) produced by General Command of Mapping are used as the base map set in Turkey. This map set, which consists of approximately 5500 sheets, covers the whole country and is produced using photogrammetric methods. Digital Elevation Models (DEMs) created from these maps are also available. Recently, another data source, Synthetic Aperture Radar (SAR) interferometric data, has become more important than those produced by conventional methods. The Shuttle Radar Topography Mission (SRTM) contains elevation data with 3 arc-second resolution and 16 m absolute height error (90 percent confidence level). These data are freely available via the Internet for approximately 80 percent of the Earth's land mass. In this study, SRTM DEM was compared with DEM derived from 25K topographic maps for different parts of Turkey. The study areas, each covering four neighboring 25K maps, and having an area of approximately 600 km², were chosen to represent various terrain characteristics. For the comparison, DEMs created from the 25K maps were obtained and organized as files for each map sheet in vector format, containing the digitized contour lines. From these data, DEMs in the resolution of 3 arc-second were created (25K-DEM), in the same structure as the SRTM DEM, allowing the 25K-DEMs and the SRTM DEM to be compared directly. The results show that the agreement of SRTM DEM to the 25K-DEM is within about 13 m, which is less than the SRTM's targeted error of 16 m. The spatial distribution of the height differences between SRTM-DEM and the 25K-DEM and correlation analysis show that the differences were mainly related to the topography of the test areas. In some areas, local height shifts were determined.     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

Monitoring the source of trans-national dust storms in north east Asia

Abstract

The vast expanse of the Gobi desert across the Mongolian plateau experiences frequent dust storms. In this isolated region remote sensing techniques can provide an effective measurement of dust storms. Brightness temperature channels 4, 5 of AVHRR/NOAA satellite data, emissive bands of MODIS/TERRA satellite data and meteorological station measurement data were used and tested for dust and sandstorm mapping in the desert area of Mongolia and northern China. The differences between thermal bands in combination with geographic information system (GIS) layers were used for mapping in this study. The results show that dust and sand storm maps can be achieved from emissive bands for monitoring of dust and sandstorms. Most active dust storm sources can occur in truly remote areas where there is little or no human activity, although many sources are associated with areas where human impacts are well documented. Thus, on a regional scale dust mobilisation appears to be dominated by natural sources.     

汶川大地震灾情综合地理信息遥感监测与信息服务系统

结合此次汶川大地震灾情综合地理信息监测与评估工作,着重探讨航空航天遥感技术在地震灾情监测与评估中的方法和技术路线.通过集成多平台和多传感器数据.根据不同区域受灾严重程度不同的情况,研究制定了地震灾区灾情综合地理信息监测指标;通过综合震前震后多源数据,制定了快速几何处理、快速数据质量综合分析、快速变化提取、快速目标判读和次生滑坡灾害空间危险性评估的技术流程,实现了汶川大地震震区灾情综合地理信息的解译、制图和统计评估.在此基础上,开发了汶川地震灾情综合地理信息服务系统,实现了灾情监测信息的综合管理、可视化查询和统计分析.并对当前工作中存在的问题进行了探讨.