Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones.     

基于GIS的TM数据分类及辅助制图研究

本文初步研究了遥感、GIS和制图一体化实用技术方法。对黄土丘陵区和沙漠地区TM数据进行了特征信息分析;给出了分层分类和GIS辅助分类结果;经模糊推理和人机交互修改,将提“纯”的遥感专题数据作为GIS的动态信息源,对GIS进行扩充与更新;最后在GIS支持下分层提取专题图并进行辅助制图。     

基于NPP-VIIRS夜间灯光数据和Landsat-8数据的城镇建筑用地提取方法改进——以广州市为例

以广州市为例,将Landsat-8-OLS数据与NPP-VIIRS夜间灯光数据相结合提取城镇建筑用地,在常规的NDBI指数、ULI指数方法的基础上进行改进,以获得更加精确的城镇建筑用地信息。首先,利用NPP-VIIRS夜间灯光数据得到城镇建筑用地的大致分布范围,基于Landsat-8-OLS数据提取NDBI、NDVI指数;然后通过分析夜间灯光建筑用地范围、NDVI指数,NDBI指数三者的特性,分别对三者图像进行二值化、求交运算来提取城镇建筑用地;最后将提取结果与传统的NDBI指数、ULI指数方法的提取结果进行对比,利用Google Earth高空间分辨率影像数据进行精度验证。结果表明,结合NPP-VIIRS数据和Landsat-8-OLS数据的多指数提取相交运算,可有效剔除提取结果中包含的裸土信息,与NDBI等指数方法相比,能得到更高的精度。     

基于GIS与空间统计分析的可持续发展度量方法研究——以缅甸Myingyan District为例

利用空间信息系统所提供的强大空间数据处理和分析能力，并将之与统计分析软件包的统计分析功能进行有效的集成，建立了基于空间统计分析的可持续发展定量分析评价模型（ＳＢＳＡ）。从在缅甸中部Myingyan县的应用实例来看，该模型能够提示影响Myingyan县可持续发展能力的主导因子，通过对这些因子和可持续发展综合指数的定量化、空间化的分析，为政府部门规划区域综合发展方案，制定发展政策提供很好的决策支持。     

GIS支持下应用PSO-SVM模型预测滑坡易发性

地质灾害不仅造成了严重的经济损失和生态破坏,同时也威胁着人类的生存。地质灾害易发性评价是地质灾害风险评价的基础,以往的研究中重在探讨易发性评价方法的选取,而对于地质灾害易发性指数如何分级的研究较少。为了探索地质灾害易发性评价精度验证与定量的分级标准,以四川省汶川县为例,选取12种广泛应用的地质灾害易发性影响因子,运用信息量模型进行易发性评价,运用成功率曲线检验模型的评价精度,提出历史地质灾害累计比例分段法,并与其他8种分级方法进行对比分析与分级精度验证。研究结果表明,用验证样本成功率曲线与非灾害点样本成功率曲线两种方法检验模型评价精度确定了评价模型预测结果的合理性。历史地质灾害累计比例分段法在易发性分级面积比例精度验证、地质灾害频率比分级精度验证与发生地质灾害位置分级精度验证3种方式中均显现出较好的合理性,在9种分级方法中为最优分级标准。     

High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data

Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (H_canopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 H_canopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 H_canopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 H_canopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted H_canopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in H_canopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.     

基于GIS的医疗资源可达性分析——以吉林省白城市为例

空间可达性是评价医疗资源分布合理性的重要指标之一,借助GIS强大的空间分析功能,可以对医疗资源可达性进行科学的分析与评价。本文尝试基于白城市的行政区划、交通数据以及医疗资源相关资料,使用GIS技术、空间可达性指标和改进的潜能模型对医疗资源可达性进行综合分析,为医疗资源的规划及分配提供决策参考。     

A Doubly Weighted Approach to Urban Data Disaggregation in GIS: A Case Study of Wuhan, China

Urban models are evolving to require more and more detailed data that in many cases have to be spatially disaggregated from larger zones. This paper deals with the disaggregation of statistical data in an urban context in which land use data are available at a less detailed level. With the availability of land use data, the traditional approach of areal weighting is improved with an areal and land use weighted approach. This weighted approach is further elaborated to include homogeneous weight zones (HWZ) so as to reflect general geographical variations among the same land use type. A case study in Wuhan, China has demonstrated the effectiveness of the doubly weighted approach within the specified context.     

基于GIS的城镇建设用地发展适宜性评价——以常州市金坛区为例

为更加深入地了解村镇尺度城镇建设用地发展适宜性情况,提高土地利用综合效益,本文以常州市金坛区遥感影像数据和土地利用现状数据为基础,采用层次分析法和GIS空间分析技术,选择自然、区位、生态和土地转换适宜度四大类10个指标,建立了适用于平原地区的具有区域代表性的城镇建设用地发展适宜性评价指标体系,分析了金坛区城镇发展适宜区分布与空间自相关特征。研究结果能为城市规划、土地规划等提供依据,尤其在城镇建设用地发展布局上具有重要的现实意义。     

Terrain Characterization for Soil Resource Mapping Using IRS-P6 Data and GIS - A Case Study From Basaltic Terrain of Central India

In the present study, landforms and soils have been characterized in Borgaon Manju watershed of basaltic terrain located in Akola district, Maharashtra, Central India. Terrain characterization using Shuttle Radar Topography Mission (SRTM) elevation data (90 m) and IRS-P6 LISS IV data in conjunction with adequate field surveys shows nine distinct landforms. Soil resource inventory shows fourteen soil series in the study area. Soils formed on gently sloping (3–8 %) subdued plateau are very shallow (23 cm), moderately well drained, moderate (15–40 %) surface stoniness, severely eroded, clayey and slightly alkaline in reaction, whereas, the soils formed on level to nearly level (0–1 %) slope in the main valley are very deep (>150 cm), well drained, very slight (<3 %) surface stoniness, moderately eroded with clayey surface and moderately alkaline in reaction. Soils in the watershed are grouped into Lithic Ustorthents, Vertic Haplustepts, Calcic Haplustepts, Typic Haplustepts, Typic Haplusterts and Sodic Calciusterts. The study demonstrates that the analysis of SRTM elevation data and IRS P6–IV data in Geographic Information System (GIS) with adequate field surveys helps in characterization of landforms and soils in analysis of landscape-soil relationship.     

Entropy Approach for Assessment of Urban Growth: A Case Study of Jaipur,INDIA

In India the last 30 years had witnessed a radical transformation of urban scene. In particular, during this period, the one lakh cities and million plus cities began to grow rapidly. In the present study, urban growth of Jaipur city in the last 31 years (1975–2006) was assessed. Jaipur ranks 11th in India with a total population of 2.3 million and has shown a consistent increase in the past 50 years. It is one of the fastest growing mega cities of the country with an annual average growth rate of 4.5% which is quite high from the national urban growth rate. Remote sensing and GIS have been used to extract the information related to urban growth-built up area and its spatial and temporal variation. The Shannon’s entropy at two levels (city as a whole and ward wise) is computed in order to quantify the form and patterns of urban growth using built up area as spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative and determining factors. Results of this study reveal that the growth rate of built up in Jaipur has outstripped the rate of population growth. Shannon’s entropy quantifies as dispersed form of urban growth till 2000 and after it, there is relative compactness in 2006.     

测绘资料数字档案馆建设——以北京市为例

本文完整阐述了测绘资料数字档案馆的建设方法及过程。介绍了测绘资料数字档案的内容并分析了其特点,确定了测绘资料数字档案馆建设的主要工作:明确了标准规范建设的主要工作内容,描述了馆藏资料档案扫描和整理的流程和质量控制方法,提出了三库分离的档案数据库物理设计思想和逻辑关系。在北京市进行了典型开发和应用实践,并对该项目进行了总结分析,指出了档案信息化是今后档案工作的重点。     

永久散射体与短基线雷达干涉测量在城市地表形变中的应用

在介绍永久散射体与短基线集雷达干涉测量方法的基础上,以辽宁省盘锦市为例,运用两种方法研究了其市区地表形变特征。对比升降轨永久散射体方法与短基线方法所得到的形变平均速率和时序分析可知,两种方法所得的研究区域的地表形变趋势较为可靠,且以垂直沉降为主,新城区是最大沉降区域,最大下沉速率达20 mm/a,其他区域较为稳定。     

Mapping Wetland Areas Using Landsat-Derived NDVI and LSWI: A Case Study of West Songnen Plain,Northeast China

Increasing interest in wetlands for environmental management requires an understanding of the location, spatial extent, and configuration of the resource. The National Wetlands Inventory is the most commonly used data source for this information. However, its accuracy is limited in some contexts, such as agricultural and forested wetlands. An large number of studies have mapped wetlands worldwide from the perspective of land use and land cover change. However, information on the actual wetland planting areas annually is limited, which greatly impacts ongoing research. In this case study of the West Songnen Plain, we developed a simple algorithm for the quick mapping of wetlands by utilizing their unique physical features, such as annual display of phenological land-cover change of exposed soils, shallow flooding water, and plants from multi-temporal Landsat images. Temporal variations of the Normalized Difference Vegetation Index (NDVI) and Land Surface Water Index (LSWI) derived from Landsat images in 2010 for wetlands at different growth stages were analyzed. Results show that during the ante-tillering phase, the NDVI value (above zero) is lower than the LSWI value of paddies because of flooding of shallow water; during the reproductive and ripening phases, the NDVI value is higher than the LSWI value (above zero); and during the post-harvest wetland planting phase, the NDVI value is still higher than the LSWI value, but the LSWI value is negative. Wetland areas can be detected using one or two images in the optimum time window. The algorithm based on the difference of NDVI and LSWI values derived from Landsat images was used to extract the actual wetland planting area. Validated alongside statistical data, the algorithm showed high accuracy. Therefore, this algorithm highlights the unique features of wetlands and can help in mapping the actual wetland area annually on a regional scale. Results further indicate that the new method has a classification accuracy of 92 %. In comparison, two traditional methods based on Landsat-7/ETM registered accuracy rates of only 83 % and 87 % respectively.     

Collaborative Web‐GIS Design: A Case Study for Road Risk Analysis and Monitoring

This article presents a methodology for designing a WebGIS framework intended for automatically analyzing spatial data and updating statistics of interest with new information inserted daily by multiple users via a Web portal. A practical example is used on vehicle accident data for assessing risk in specific road segments. Two main blocks integrated together will be described: the collaborative block and the data‐analysis block. The former gives end‐users computer‐aided tools to view, insert, modify and manage data related to accidents and traffic monitoring sensors, whereas the latter is developed to automatically analyze the accident data coming from user's collaboration. Because different agencies can survey accident sites, a collaborative environment is necessary – and a Web‐based solution is ideal – for permitting multi‐user access and data insertion. A centralized approach to process the data in real time is described in all its components. Server‐side Structured Query Language functions optimize performance by using dedicated libraries for spatial processing and re‐structuring the attributes associated with elements which are consequently re‐classified for correct color‐scaling. The end‐product is a system that provides a seamless integration of front‐end tools for user collaboration and back‐end tools to update accident risk statistics in real time and provide them to stakeholders.     

Application of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak,Kerman, south of Iran

Abstract

The Shahr-e-Babak region located in the Kerman metallogenic belt is one of the high potential segments of Urumieh–Dokhtar magmatic arc for porphyry copper and epithermal gold mineralization in the south of Iran. This high potential zone encompasses several porphyry copper deposits under exploitation, development and exploration stages. The aim of this study is to evaluate Landsat-8 data and comparison with the Advanced Spaceborne Thermal Emission and Reflection Radiometer data-sets for mapping hydrothermal alteration zones related to Cenozoic magmatic intrusions in Shahr-e-Babak region. Previous studies have proven the robust application of ASTER in lithological mapping and mineral exploration; nonetheless, the Landsat-8 data have high capability to map and detect hydrothermal alteration zones associated with porphyry copper and epithermal gold mineralization. In this investigation, several band combinations and multiplications, developed selective principal component analysis and image transformations were developed for discriminating hydrothermal alteration zones associated with porphyry copper mineralization using Landsat-8 data.     

基于GIS和RS的区域陆地植被NPP估算——以中国内蒙古为例

在GIS的支持下,利用地面气象数据和MODIS数据,考虑到最大光利用率在不同植被类型中的差异,构建了一个区域陆地植被NPP估算模型,并以2002年的内蒙古植被为例,研究了植被净初级生产力及其时空分布。结果表明:(1)该模型在数据获取了上比较容易,仅利用地面气象数据和遥感数据就可以对陆地植被NPP进行估算,使其实际的可操作性得到加强。(2)通过与NPP的实测资料及其他模型的对比研究表明:该模型对区域陆地值被NPP的模拟效果较好,与Chikugo模型相比,它更能反应森林NPP的实际情况。(3)2002年内蒙古植被净初级生产力为390.8MtC/a,总体分布趋势是由东北向西南递减,其季节变化也非常明显,在6月中旬至9月中旬的3个月时间里,NPP占了全年的72.7%,而1—2月份植物基本停止生长,净初级生产力极低,每月仅为3.59MtC。     

基于GIS和遥感的水库面积计算方法比较研究——以石梁河水库为例

水库作为水利设施的重要组成部分,在蓄水、供水、防洪、泄洪等方面起着极为重要的作用。计算水库面积,对于水库的蓄水量,影响的流域范围等有着重要的参考价值。本文以石梁河水库为例,选取了较为常见的利用GIS技术和遥感技术计算水库面积的方法,并科学地比较了两种方法的优缺点,为选择水库面积计算方法提供有力依据。     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.