Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively.     

Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques

This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and ?6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC.     

面向土地规划管理的"多规合一"信息平台建设应用初探

近年来,许多城市以开展"多规合一"信息平台建设为抓手推进"多规合一"工作,以期建立统一的空间规划体系.但是,大部分地区实践的"多规合一"信息管理平台建设侧重于多规空间数据展示和协同审批等方面,而对面向土地规划管理方面并无相关应用.本文首先尝试对土地规划管理与多规合一的关系进行探讨;进而梳理出面向土地规划管理的多规合一的信息平台建设思路;最后重点研究解决了土地集约利用管理、建设用地选址预审、土地规划实施效果评价等功能的关键技术问题.     

利用小基线集技术(SBAS)监测泉州地区地表形变

差分干涉测量(DInSAR)技术可用于监测厘米级甚至毫米级的地表形变。文中选用2007—2011年期间19景日本ALOS1/PALSAR数据,采用短基线集技术(SBAS)获取了该地区的形变时间序列和平均沉降速率。研究结果表明:泉州东南部地区整体上呈抬升态势,上升速率为5mm/a左右,主要是受到亚欧板块和西太平洋板块互相挤压作用的结果;泉州东南部有多处出现地表沉降,沉降的结果与泉州市统计的地区地下水开采数据相当吻合,因此沉降主要与地下水过度开采相关。     

时空约束下的沙化土地遥感提取方法研究

针对辽西北中轻度沙化土地的区域特点和特殊性,通过分析其沙化土地的光谱特征、空间分布及时间变迁特征,研究了一种时空约束下的多光谱遥感影像沙化土地识别方法。在对沙化土地进行光谱特征分析的基础上,加入时-空分布特征进行约束修正,有效提高了沙化土地监测的精度和效率。     

基于SWAT模型的细河流域土地利用/覆被变化对径流影响的研究

土地利用/覆被变化对细河流域的水文过程影响显著。为研究不同土地利用/覆被情景对流域水文要素的影响情况,本文构建了适用于细河流域的SWAT分布式水文模型,并拟算出不同情景下的流域多年平均月径流量、多年平均地表径流深度、多年平均蒸发量以及多年平均土壤侧流。模拟结果显示:当流域农林用地增加时,平均月径流量增加了8.40%;当建设用地增加时,平均月径流量减少了4.11%;当旱地及其他未利用地增加时,平均月径流量减少了1.93%。综上所述,细河流域农林用地变化对径流产量的影响相对最大,其增加导致径流量增加;旱地及其他未利用对径流产量的影响相对最小,建设用地和旱地及其他未利用地的增加导致径流量减少。     

“一带一路”倡议下老挝沙湾区土地适宜性评价

为了对老挝沙湾区土地适宜性进行评价,本文选择了地形、土壤、水利、区位和生态5类13项指标,建立了老挝沙湾区土地适宜性评价指标体系;将层次分析法(AHP)与GIS技术相结合,对老挝沙湾区农业用地、林业用地和建设用地进行适宜性评价。研究结果表明:宜农用地占土地面积的30.16%,主要分布在海拔较低、坡度较小的沙湾拿吉大平原以及湄公河沿岸;宜林用地占土地总面积的60.07%,主要分布在东部和北部山区;宜建用地占土地总面积的4.62%,主要分布在平原地区,湄公河沿岸,以及交通较发达地区;不适宜用地占总面积的2.32%,主要分布在甘蒙省西北部以及东部。研究方法和研究结果可以为老挝沙湾拿吉省土地利用规划和相关研究提供科学依据与支持,同时为老挝今后的土地资源开发奠定基础。     

Management Challenges for Aboriginal Cultural Heritage in Peri-urban Queensland

It is often assumed that places of cultural significance to Aboriginal and Torres Strait Islander peoples are protected under cultural heritage legislation such as the Aboriginal Cultural Heritage Act 2003 in Queensland. Such Acts are improvements on previous policies, which all but neglected Aboriginal cultural heritage. Nevertheless, the aims of policies developed at wider geographic scales, such as States within the Australian system, continue to be disconnected from the experiences of some local Traditional Owners. In this paper, we examine conflicts between non-local policy and on-ground management decisions for Aboriginal cultural heritage in peri-urban Queensland. We focus on the challenges of local Traditional Owners in peri-urban landscapes, basing our discussion on recent experiences conducting research on Indigenous land management in southeast Queensland. We examine three case studies: one in which colonial heritage values were prioritised over existing Aboriginal cultural heritage values, a second where local government failed to support a private landholder’s attempt to identify and protect a cultural heritage site, and a third where a cultural heritage site was protected but in a way that restricts the continuation of cultural practices. Developing more productive and equitable relationships between Traditional Owners and non-Indigenous decision makers, with regards to Aboriginal cultural heritage, requires new locally developed processes for engagement and we suggest how this could be achieved.     

Oil and gas development exposure and conservation scenarios for Greater sage-grouse: Combining spatially explicit modeling with GIS visualization provides critical information for management decisions

During the last quarter-century, global demand for energy has increased by more than 60%, and a similar increase is anticipated to occur by 2030 (Raymond, Deming, & Nichols, 2007). In the U.S., oil and gas development is projected to continue across western states within sage-grouse habitat. Greater sage-grouse, recently a candidate species for protection under the Endangered Species Act (ESA), have well documented negative responses to oil and gas disturbance. In this study, we create spatially-explicit oil and gas future development scenarios, baseline and high, and link them to sage-grouse population and habitat maps to quantify future exposure risk within Western Association of Fish and Wildlife Agencies (WAFWA) sage-grouse management zones (MZ) I and II. We then analyze recent land use decisions from the Bureau of Land Management (BLM) along with enacted policy from the State of Wyoming to estimate how these management actions might minimize the exposure risk of sage-grouse to oil and gas development into the future. Our results show that BLM and Wyoming conservation plans could reduce the exposure of sage-grouse to oil and gas development from 15-27% to 11–17% (31–37% reduction) in MZ I and from 15-27% to 5–9% (64–68% reduction) in MZ II. Our estimates of exposure to future oil and gas development, and conservation measures designed to ameliorate those threats, represent the upper and lower extents of potential impacts within scenarios. Our work demonstrates how spatial modeling and GIS visualization can be used by managers to assess likely outcomes of conservation decisions.     

Optimal rural land use allocation in central China: Linking the effect of spatiotemporal patterns and policy interventions

Rural land use development is experiencing a transition stage of socioeconomic and land use development in China. Historic land use transition process and policy interventions have key influence on the applicability of land use allocation solutions in future land use management. Strategic land use allocation is therefore required to possess a good adjustment capability to the transition process. Although heuristic optimization methods have been promising to solve land use allocation problems, most of them ignored the spatially explicit effect of historic land use transition and policies. To help resolve this issue, this study aims to optimize future land use pattern in the context of rural land use development. We took Yunmeng County, one of the typical major grain producing and rapidly urbanizing areas in central China, as a case study and solved the sustainable land use allocation problem by using an improved heuristic optimization model. The model was constructed based on the integration of a spatial discrete particle swarm optimization and cellular automata-Markov simulation approach. The spatiotemporal land use patterns and policy interventions were represented by the CA-Markov as in spatially explicit transition rules, and then incorporated into the discrete PSO for optimal land use solutions. We examined the influence of the joint effect of spatiotemporal land use patterns and policy interventions on the land use allocation outcome. Our results demonstrate the robustness and potential of the proposed model, and, more importantly, indicate the significance of incorporating the spatiotemporal land use patterns and policy interventions into rural land use allocation.