基于GIS的东川区生态环境敏感性分析

利用地理信息系统技术与层次分析法对东川区生态环境进行敏感性分析,本文共选取5个评价指标,首先对其进行单因子评价;然后基于GIS空间分析功能,将东川区综合生态敏感性分为5个等级;最后构建东川区综合生态环境敏感性分布图。研究结果显示:①5个生态评价因子中,坡度因子对东川区生态环境敏感性影响最大,权重值为0.36。根据对生态敏感性影响程度从大到小排序为:坡度、高程、土地利用、NDVI和水域缓冲区。②东川区生态环境敏感性总体偏高,极高敏感区与高敏感区共占区域总面积的44.17%;中敏感区占比最高,为26.1%;低敏感区和极低敏感区两者占比之和为29.73%。③东川区生态环境极高敏感区和高敏感区主要分布在西北部;极低敏感区和低敏感区主要分布在东北部及中部河谷地区。     

基于GIS的喀斯特地区生态环境敏感性综合评价

以毕节市为研究区,利用2020年Landsat遥感数据,基于地理信息系统（GIS）技术通过单因子评价与多因子综合评价的方法,选取了坡度、坡向、高程、水系和植被指数5个评价因子,对毕节市生态环境敏感性进行分析。结果显示：毕节市生态敏感性具有明显差异,其中极度敏感区域主要分布在北部和东南部地区,占总面积的2.77%;高度敏感区主要分布在毕节市的西部和北部地区,占总面积的9.12%;毕节市境内有河流的区域主要为中度敏感区,占总面积的12.86%;低度敏感区和不敏感区所占面积差异较小,分别占总面积的38.12%和37.12%。喀斯特地区生态环境敏感性的综合评价结果,可为区域生态环境保护和建设提出相关建议。     

GIS支持下土地生态环境敏感性评价——以金坛市为例

针对传统的生态环境敏感性评价指标体系差异较大,尚未形成统一体系,并且各个指标之间的关系及相互影响的情况并没有得到体现的问题,该文在地理信息系统的支持下,选取土地质量尺度的土地利用类型、土壤质地,及生态尺度的水环境、植被覆盖度、人口密度、坡度作为生态环境敏感性因子,同时建立了以水环境为主要因子的土地生态敏感性指数模型,对金坛市生态环境敏感性进行单因子评价和多因子综合评价。评价结果显示,金坛市生态高敏感区和中敏感区占到全市总面积的78%,低敏感区和不敏感区占到全市总面积的22%。针对不同土地生态敏感性分区因地制宜提出用地策略,为土地利用总体规划和城市生态保护提供参考。     

榆林地区景观生态敏感性时空特征

景观生态敏感性分析是生态环境研究的重要内容之一。为了分析榆林地区景观生态敏感性时空特征,用10 km×10 km敏感性分析网格,把榆林地区划分为527个单元样地,采用加权和法,计算每个单元的2000年、2005年和2010年3个时期的景观生态敏感性指数,并在ArcGIS中进行Kriging空间插值,得到3个时期的景观生态敏感性图。结果表明:2000—2005年,轻度敏感区和极度敏感区的面积增大,中度和高度敏感区的面积减小,景观生态敏感性呈减弱趋势;2005—2010年,轻度敏感区的面积大大减小,中度敏感区和极度敏感区的面积均有增加,而高度敏感区的面积显著增加,整体上景观生态敏感性呈增大趋势。     

基于OLI数据的云南禄劝县石漠化敏感性评价

石漠化敏感性指的是区域在自然状况下发生石漠化现象的可能性大小,开展石漠化敏感性评价对区域生态环境的建设和可持续发展具有重要意义。本文以OLI影像为数据源,选取植被覆盖率、裸岩率和坡度作为评价指标,以地理信息技术为支撑开展禄劝县石漠化敏感性评价。评价结果显示:禄劝县轻度敏感面积为2 091 km~2,占总面积的57.492%;中度敏感面积为1 470 km~2,占总面积的40.418%;重度敏感面积为75.46 km~2,占总面积的2.075%;极度敏感面积为0.533 km~2,占总面积的0.015%。从空间分布上看,轻度敏感区主要分布于中西部地区;中度敏感集中分布于北部及南部地区;重度敏感主要分布于北部金沙江流域、东南部普渡河流域和云龙水库内流河沿线区域;极度敏感区主要分布在普渡河下游地区。总体而言,禄劝县石漠化敏感性相对较高,在区域开发与保护过程中应引起高度重视。     

基于生态敏感性的区域环境时空变化及其驱动因素探究——以定安县为例

生态敏感性是分析区域生态环境稳定性的主要方法之一,其评价结果对于区域生态保护和决策制定具有重要意义。本文以定安县为研究区域,从土壤侵蚀、生境、地形、水资源4个方面选取了9个敏感指标构建生态敏感性评价体系,结合层次分析法(AHP)和熵权法进行组合赋权,分析2013和2021年两个时期生态敏感性时空分布与变化,以及空间集聚性、生态敏感性区域变化,并利用地理探测器分析其主要驱动因素。结果表明：2013—2021年,定安县生态敏感性均是南高北低的分布格局,且整体生态敏感性程度呈下降趋势;生态敏感性空间集聚效应显著,但随着时间推移,空间聚集性降低,高值聚集区和低值聚集区均有收缩的趋势;生态敏感区转移主要发生在中敏感区和高敏感区,变化等级以一级递增、递减为主;土地利用和植被覆盖度较其他敏感指标对生态敏感性的驱动力较大,且随着时间推移,土地利用与植被覆盖度对生态敏感性的驱动力呈主导作用趋势。     

基于GIS的关中-天水经济区生态敏感性分析

选取坡度、高程、土地利用类型、水域、植被覆盖指数(NDVI)5个敏感性因子,利用层次分析法(AHP)确定因子权重,以像元、行政区划、流域为单元,得到不同尺度上关中-天水经济区生态敏感性的空间分布。结果表明:关中-天水经济区高度、中度、低度和不敏感区面积占比分别为18.81%、31.61%、44.47%和5.11%;行政区划尺度中,生态敏感性最高的为商洛市,最低的为杨凌区;流域尺度中,生态敏感性最高的为乾佑河流域,最低的为渭河流域龙门至三门峡段和咸阳至潼关段。对研究区域生态环境提出分级保护与开发建议,为相关部门制定发展方略提供参考依据。     

基于GIS的西宁市生态敏感性分析

国土优化开发和土地集约型利用是生态文明建设的重要一环,生态敏感性分析为优化国土空间和土地的开发利用提供参考基础,也可促进区域生态环境的可持续发展。借助GIS技术,选取高程、坡度、水域、NDVI和土地利用类型5个生态敏感性因子,利用层次分析法(AHP)确定因子权重,采用空间叠加分析对生态敏感度定量分析,将所得到的生态敏感的高低分为高度敏感区、中度敏感区、低度敏感区和非敏感区,对各个生态敏感区进行综合分析,得到西宁市单因子和综合生态敏感性分析结果,并对西宁城市群的开发提出分区保护与建设的对策建议。     

基于RS和GIS东北农业主产区水土流失敏感性评价——以黑龙江省宾县为例

基于RS和GIS技术,从土壤侵蚀、土地利用和地质灾害3个方面出发,选取土地覆盖类型、高程、地质灾害、土壤侵蚀和坡度等5个因子,基于AHP确定因子权重,借助于ArcGIS的空间叠加分析,通过多因子叠加计算完成研究区水土流失敏感的综合评价,并探究其空间分布格局。研究结果表明:4个敏感级的面积比分别为15.20%,50.00%,20.45%和14.35%,其中,极敏感区的面积比例最小,低敏感区面积的最大;不敏感区主要分布在城市化进程相对较快的区域;低敏感区主要分布在研究区中部的丘陵地带和北部沿江河谷平原区,敏感区主要分布于南部和西南部的前山和深丘地区,极敏感区集中分布南部山区;在地域分异规律上,敏感程度由北向南逐渐递增。该结果可为区域水土保持规划的制定和生态环境的建设提供重要的科学依据和技术支撑。     

基于ArcGIS区统计的陇南市生态敏感性评价

近年来,生态环境问题日益突出,生态环境敏感性分析变得非常重要。基于GIS技术的生态敏感性分析评价比传统的分析技术更精确、高效、智能化。本研究针对甘肃省陇南市实际情况,建立了生态环境敏感性指标体系并进行分级,利用DEM、现有地图资料及ENVI、ArcGIS软件计算植被覆盖度、坡度、降水量、高程、土地利用类型等敏感性因子。研究了将GIS空间分析技术用于区域生态环境评价中的理论方法和技术流程,并对影响该区域的主要生态因子进行了单因子评级和综合性评价,得出图例结果。     

基于格网单元的土地利用总体规划生态环境影响评价

开展土地利用总体规划生态环境影响评价实证研究,能够为不同城市的土地利用总体规划修编和生态环境保护提供依据。本文从土地利用结构和布局的角度,以格网为评价单元,运用生态系统服务功能价值评估法,对土地利用总体规划的结构调整所带来的生态服务价值的变化做了空间上的定量预测;并运用生态环境敏感性评价和GIS叠置法从土地利用布局上评价建设用地布局的生态合理性。以鄂城区为例,划分了702个格网,规划实施后生态系统服务功能总价值减少了414.54万元;建设用地布局具有较好的生态适宜性,只有0.23%分布在高度敏感区内。因此,鄂城区土地利用总体规划从生态环境保护角度是可行的,但还应注重对森林和水域等重要生态用地的保护。     

基于生态重要性评价与最小累积阻力模型的重庆市生态安全格局构建

生态安全格局构建是保障生态系统稳定运行,实现区域生态安全的重要途径。本文以重庆市为例,基于生态服务功能和生态敏感性对重庆市进行生态重要性评价,将生态极重要区域与自然保护地共同作为生态源地。首先从生态本底和生态胁迫两个角度选取高程、坡度、土地利用类型、距道路距离、距水体距离、夜间灯光指数6个阻力因子构建了典型山地动物的生态阻力面,然后利用最小累积阻力模型（MCR）模拟潜在生态廊道,完成市域生态安全格局的构建。研究表明,重庆市生态源地30 751.11 km²,占全市总面积的37.32%,主要分布在渝东北与渝东南的山林地。将识别的73条潜在生态廊道、138个生态节点与生态源地进行叠加,结合重庆的山水格局,构建了"两区、三带、四核、多廊、多点"的多层次生态安全格局,为重庆市生态保护和修复工作提供科学参考。     

Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories. The water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear together in processes for identifying problems and analyzing, synthesizng, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning.     

Soil Loss Mapping for Sustainable Development and Management of Land Resources in Warora Tehsil of Chandrapur District of Maharashtra: An Integrated Approach Using Remote Sensing and GIS

The study area is characterized by low and fluctuating rainfall pattern, thin soil cover, predominantly rain-fed farming with low productivity coupled with intensive mining activities, urbanization, deforestation, wastelands and unwise utilization of natural resources causing human induced environmental degradation and ecological imbalances, that warrant sustainable development and optimum management of land resources. Spatial information related to existing geology, land use/land cover, physiography, slope and soils has been derived through remote sensing, collateral data and field survey and used as inputs in a widely used erosion model (Universal Soil Loss Equation) in India to compute soil loss (t/ha/yr) in GIS. The study area has been delineated into very slight (<5 t/ha/yr), slight (5–10 t/ha/yr), moderate (10–15 t/ha/yr), moderately severe (15–20 t/ha/yr), severe (20–40 t/ha/yr) and very severe (>40 t/ha/yr) soil erosion classes. The study indicate that 45.4 thousand ha. (13.7% of TGA) is under moderate, moderately severe, severe and very severe soil erosion categories. The physiographic unit wise analysis of soil loss in different landscapes have indicated the sensitive areas, that has helped to prioritize development and management plans for soil and water conservation measures and suitable interventions like afforestation, agro-forestry, agri-horticulture, silvipasture systems which will result in the improvement of productivity of these lands, protect the environment from further degradation and for the ecological sustenance.     

基于Landsat ETM+数据的银川城市热岛研究

利用1999年8月12日的Landsat-7 ETM+数据,经辐射定标、NDVI及地表比辐射率计算,通过简化大气辐射传输模型,对银川市的地表温度进行了反演。在此基础上,对银川市的城市热岛分布特征和热场变异指数进行了研究。结果表明,银川市具有典型城市热岛效应,城区的地表温度比市郊引黄灌区高出10℃左右;银川市生态环境质量出现了两个极端,即市区生态评价指标为极差,而市郊引黄灌区的生态评价指标为优。     

滇东南岩溶区土壤侵蚀敏感性分析及其空间分异特征

以滇东南岩溶区独特的地理环境作为研究对象,根据《生态功能区划暂行规定》和国内外已有的土壤侵蚀敏感性的研究成果,选择地形起伏度(LS)、植被覆盖度(C)、土壤质地(K)3个自然因子作为滇东南岩溶区土壤侵蚀敏感性评价的指标,在GIS时空分析功能支持下,以《生态功能区划暂行规定》的敏感性等级标准为依据,分析各个因子对土壤侵蚀敏感性的影响,并结合研究区域实地情况对各个因子分别进行土壤侵蚀敏感性的评价。在各因子单独分析的基础上运用GIS的空间分析功能,根据这些敏感因子对侵蚀敏感性影响程度的不同分配、不同权重,进行综合分析评价,得到不同敏感程度区域的面积和空间分异特征。     

生态红线划定的技术方法研究——以鄂州市为例

在分析鄂州市生态环境矛盾和生态服务需求的基础上,根据生态红线原理和目标,提出较为完整的生态红线划定方法,保障生态系统完整性及连续性,维护区域生态系统安全。从生态环境敏感性评价、生态服务功能重要性评价、生态灾害危险评价等3方面构建生态适宜性评价体系,展开生态适宜性分析。在GIS与RS支持下,创建了鄂州市网格空间属性数据库,采用层次分析法确定指标权重,运用叠加分析、空间分析等技术方法最终完成生态适宜性评价,将鄂州市划分为红线、黄线、蓝线、绿线等4个区域,同时,叠加整合已有的重要生态功能区,并将红线区划分为一级管控区和二级管控区,明确重点保护方向,确定生态红线管控体系。     

Burnt area mapping of Bandipur National Park,India using IRS 1C/1D LISS III data

The Bandipur National Park situated in the Western Ghats of Karnataka State, is one of the biodiversity hotspots of the world. During recent years, this park has witnessed repeated fires, affecting considerable areas under vegetation. The temporal satellite data from 1997 to 2006 have been analyzed to map the burnt areas using Remote Sensing (RS) and Geographic Information System (GIS) techniques. The vegetation cover is moist deciduous, dry deciduous, scrub forests and teak plantation. Information on extent of the burnt areas and the type of vegetation affected were derived forest range-wise. The fire prone regions have been identified by integrating vegetation type/density, road and settlement network and past history of forest fire occurrence, by assigning subjective weightage according to their fire-inducing capability or their sensitivity to fire. Comparison between each temporal dataset in terms of the extent of burnt area was also carried out to interpret fire incidence pattern. Three categories of fire risk regions such as Low, Moderate and High fire intensity zones were identified and it was found that almost 40% of the study area falls under low risk zone. An evaluation of the existing fire management systems and the implication of fire prevention programmes has been discussed, besides an assessment of causal factors for fire incidence in the park.