Effect of canal on land use/land cover using remote sensing and GIS

The monitoring of land use/land covers (LULCs) is an indispensable exercise for all those involved in executing policies to optimize the use of natural resources and minimize the ill impacts on the environment. The study here aims at analyzing the changes that occurred in LULC over a time span from 1990 to 2005 using multi date data of a part of Punjab. The digital data consisted of two sets of Landsat Thematic Mapper (TM) data and one set of IRS-1C data. Utilizing hybrid classification technique for interpretation and on field validation, it has been found that canal irrigation leads to changes in LULC as there is a change in cropping pattern as well as increase in water logged area.     

基于知识的分层综合分类法在土地利用/土地覆盖遥感信息提取中的应用

土地利用/土地覆盖数据的获取是研究LUCC的重要基础工作。随着遥感技术的飞速发展,通过遥感提取土地利用/土地覆盖专题信息已成为LUCC研究必不可少的一步。目前遥感专题信息提取水平相对滞后于遥感数据获取,为了提高遥感数据在土地利用/土地覆盖的应用,寻找一种较好的、具有相对适用性的方法是目前遥感应用的一个迫切要求。本文比较了目前比较常用的几种土地利用/土地覆盖遥感信息提取方法,分别以西部干旱区(柴达木盆地)和东部地区(鄱阳湖地区)为例,提出在GIS支持下基于知识的分层综合分类方法,并通过和其他几种常用方法进行比较分析,得到如下结果:在自然环境相差较大的柴达木盆地和鄱阳湖地区,采用了GIS支持下基于知识的分层综合分类方法的提取精度均要比单独采用最大似然法、纹理分析法、神经网络分类法等方法的总体精度高出25%,Kappa系数高出0.2。由此可以说明了该方法对于土地利用/土地覆盖专题信息的提取是可行的,同时它也具有一定的适用性。     

武夷山市土地利用变化遥感监测分析

针对近20年来武夷山市土地利用变化信息相对不足的问题,提出运用现代遥感与GIS技术进行武夷山市土地利用变化动态监测的方法。在分析1996年、2005年和2014年3期Landsat影像的基础上,采用监督分类方法提取土地利用信息,然后采用土地利用转移矩阵分析法和动态度分析法,分析了土地利用类型的时空变化情况及发展趋势。结果表明:1996年以来,武夷山市土地利用变化的特点表现为耕地显著增加,而林地明显减少;20年间其他未利用地和城镇的空间变化幅度很大;经济快速发展,旅游业大力发展等是武夷山市土地利用变化的驱动因素。研究结果对武夷山市的生态环境保护以及探究旅游业对土地利用变化的影响有参考价值。     

高分遥感驱动的精准土地利用与土地覆盖变化信息智能计算模型与方法研究

通过对地观测可在影像空间上全面反映地表地理现象、格局及演化过程,基于遥感数据提取的土地利用与覆盖变化(LUCC)产品为分析地理要素空间分布规律及其变化机制提供全覆盖、定量化和快速更新的本底信息.本文以高分辨率遥感视觉特征(图)与多源多模态观测机理特征(谱)相互耦合的图谱认知理论研究为基础,提出了精细地理图斑空间结构(精...     

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

Data are increasingly spatio‐temporal—they are collected some‐where and at some‐time. The role of proximity in spatial process is well understood, but its value is much more uncertain for many temporal processes. Using the domain of land cover/land use (LCLU), this article asserts that analyses of big data should be grounded in understandings of underlying process. Processes exhibit behaviors over both space and time. Observations and measurements may or may not coincide with the process of interest. Identifying the presence or absence of a given process, for instance disentangling vegetation phenology from stress, requires data analysis to be informed by knowledge of the process characteristics and, critically, how these manifest themselves over the spatio‐temporal unit of analysis. Drawing from LCLU, we emphasize the need to identify process and consider process phase to quantify important signals associated with that process. The aim should be to link the seriality of the spatio‐temporal data to the phase of the process being considered. We elucidate on these points and opportunities for insights and leadership from the geographic community.     

遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km² in 1990 to 3 794.9 km² in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).     

Monitoring coastal zone land use and land cover changes of Abu Dhabi using remote sensing

Coastal zones are most vulnerable for landuse changes in this rapid industrialization and urbanization epoch. It is necessary to evaluate land use — land cover (LULC) changes to develop efficient management strategies. The main objective of this paper is to evaluate and quantity Abu Dhabi coastal zone LULC changes from 1972 to 2000 using multi-temporal LANDSAT satellite data and digital change detection techniques. Supervised classification coupled with expert visual interpretation techniques were used to produce LULC classified images with an accuracy of 88%. Change detection process was achieved by applying post-classification comparison techniques in ENVI software. From this study it has been observed that the important coastal landuse types of Abu Dhabi coast .i.e. wetlands and woody Vegetation (Mangrove, represented by a single species,Avicennia marina) have been reduced drastically in their extent due to reclamation, dredging, tipping and other anthropogenic activities along the coastal zone. However, it has been observed that there is rapid increase in the man-made plantation and managed vegetation from 1990 to 2000 due to the Abu Dhabi government initiation. This study has given good insight into Abu Dhabi coastal zone changes during last 3 decades.     

高分辨率卫星遥感影像在土地利用变化动态监测中的应用

20世纪80年代初以来,随着经济的快速发展,我国土地利用结构发生了明显的变化,耕地资源数量减少,非农业用地大量增加。及时、精确掌握土地资源的数量、质量分布及其变化趋势,关系着土地资源的持续发展与规划。本文选择地貌类型多样、社会经济发达、土地利用变化较大的北京市昌平区,在RS、G IS支持下对土地利用变化进行动态监测、制图与动态变化分析典型试验。为了进行土地利用的动态变化分析,获取了昌平地区俄罗斯KOCMOC卫星1986年与1998年SPIN-2 2m分辨率的遥感影像数据,同时获取了法国SPOT-5卫星2004年2.5m分辨率遥感影像数据,以及相应的分辨率略低的多波段遥感影像数据。完成了1986年、1998年、2004三个年分的土地利用图的编制,并完成了1986-1998年、1998-2004年以及1986-2004年三个时期昌平区土地利用变化图及土地利用动态变化分析。     

Open land cover from OpenStreetMap and remote sensing

OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017.     

Land use/land cover mapping and change detection in part of eastern ghats of tamil nadu using remote sensing and GIS

In the present study an attempt has been made to map land use/land cover and change detection analysis in Kolli hill, part of Eastern Ghats of Tamil Nadu, using remote sensing and GIS. About 467 ha increase has been observed in single crop category and about 434 ha decrease has been observed in land with or without scrub category. Majority of the area (13639 ha) is under scrubland. Lesser changes could be noticed in double crop, plantation and barren/rocky categories. Necessary measures should be taken to utilize the scrubland and to prevent the conversion of cropland into scrubland. The identified wastelands, which are suitable for agriculture, have to be utilized optimally to improve the economy of the people.     

Assessing different remote sensing techniques to detect land use/cover changes in the eastern Mediterranean

This paper describes different change detection techniques, including image differencing, image rationing, image regression and change vector analysis (CVA) to assess their effectiveness for detecting land use/cover change in a Mediterranean environment. Three Landsat TM scenes recorded on 7 July 1985, 27 July 1993 and 21 July 2005 were used to minimize change detection error introduced by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. The four change detection techniques were applied and an object-based supervised classification was used as a crossclassification to determine ‘from–to’ change which enabled assessment of the four techniques. The change vector analysis resulted in the largest overall accuracy of 75.25 and 75.55% for the 1985–1993 and 1993–2005 image pairs, respectively. The ratio yielded the least accurate results with an overall accuracy of 59.10 and 61.05% for the 1985–1993 and 1993–2005 image pairs, respectively. Different change detection algorithms have their own merits and advantages. However, the change vector analysis change detection technique was the most accurate model for handling the variability present in Mediterranean land use/cover.     

Land use/land cover changes near HAzira Region, Gujarat using remote sensing satellite data

Human activities have great influence on fragile coastal ecosystem. For sustainable use of coastal resources it is very important to understand land use/land cover changes and its implications on coastal systems. Remote sensing data because of its synoptic, multispectral and multi temporal nature can be a very good source for mapping, monitoring and understanding these changes. IRS LISS III sensor data were used to find out the rate of land use/land cover changes in Hazira area near Surat, Gujarat. Because of major industrial activities it has become a hot spot area which requires regular monitoring. In the present study, land cover information of the period 1970–1972 from the Survey of India topographical maps, and satellite data of the year 1989 and 1999–2002 have been used and visual analysis has been carried out to measure the land use/land cover changes. Erosion and deposition has been observed around the newly constructed jetty. Forest area and agriculture area is found to decreased, whereas built-up area has increased.     

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.     

Spatio-temporal pattern analysis of land use/cover change trajectories in Xihe watershed

Human-induced land use/cover change has been considered to be one of the most important parts of global environmental changes. In loess hilly and gully regions, to prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades. The main objective of this study is to quantify the spatio-temporal variability of land use/cover change spatial patterns and make preliminary estimation of the role of human activity in the environmental change in Xihe watershed, Gansu Province, China. To achieve this objective, the methodology was developed in two different aspects, that is, (1) analysis of change patterns by binary image of change trajectories overlaid with different natural geographic factors, in which Relative Change Intensity (RCI) metric was established and used to make comparisons, and (2) analysis based on pattern metrics of main trajectories in the study area. Multi-source and multi-temporal Remote Sensing (RS) images (including Landsat ETM+ (30 June 2001), SPOT imagery (21 November 2003 and 5 May 2008) and CBERS02 CCD (5 June 2006)) were used due to the constraints of the availability of remotely sensed data. First, they were used to extract land use/cover types of each time node by object-oriented classification method. Classification results were then utilized in the trajectory analysis of land use/cover changes through the given four time nodes. Trajectories at every pixel were acquired to trace the history of land use/cover change for every location in the study area. Landscape metrics of trajectories were then analyzed to detect the change characteristics in time and space through the given time series. Analysis showed that most land use/cover changes were caused by human activities, most of which, under the direction of local government, had mainly led to virtuous change on the ecological environments. While, on the contrary, about one quarter of human-induced changes were vicious ones. Analysis through overlaying binary image of change trajectories with natural factors can efficiently show the spatio-temporal distribution characteristics of land use/cover change patterns. It is found that in the study area RCI of land use/cover changes is related to the distance to the river line. And there is a certain correlation between RCI and slope grades. However, no obvious correlation exists between RCI and aspect grades.     

土地覆盖遥感制图方法研究现状与展望

区域尺度土地利用/覆盖分类研究是当今国际上开展土地利用/覆盖变化研究的重要领域之一。及时、准确地获取地球表面特性对于掌握人类和自然现象之间的关系和相互作用至关重要。本文根据土地覆盖遥感分类方法特点,从硬分类方法、软分类方法以及最新的软硬分类方法出发,总结了国内外的各研究成果,分析了各种方法的分类策略与特点及其方法适用性。研究结果表明：软硬分类方法能够灵活适用于遥感图像上纯净、混合像元并存的特点,可以有效解决光谱的异质性,在土地覆盖遥感监测中具有广阔的应用潜力。文中提出了基于变端元的软硬分类土地覆盖制图方法框架,并指出了今后的研究重点。     

遥感图像土地覆盖分类中多源特征数据选择研究

多源特征数据可以提高遥感图像的分类精度,选择合适的特征数据十分重要。利用基尼指数对多尺度纹理信息、主成分变换前三分量、地形数据等特征进行选择,选出最佳特征子集。利用支持向量机、神经网络分类法、最大似然法分别对全部特征数据和最佳特征子集结合多光谱数据进行分类。实验结果表明:基尼指数可以有效地对多源特征数据进行选择,特征选择可以提高分类器效率,提高分类精度。     

面向对象的高分辨率遥感影像土地覆盖信息提取

利用高分辨率影象提取土地覆盖信息的关键技术在于如何利用丰富的纹理信息来弥补光谱信息的不足。面向对象的图像分类技术改变了传统的面向像素的分类技术:(1)用来解译图像的信息并不在单个像元中,而是在图像对象和其相互关系中;采用多分辨率对象分割方法生成图像对象,提高了分类信息的信噪比;基于对象的分类技术不同于纯粹的光谱信息分类,图像对象还包含了许多的可用于分类的一些其他特征:形状、纹理、相互关系、上下关系等信息。面向对象的土地覆盖分类结果与传统分类方法相比,其特征提取算子更加地适合于几何信息和结构信息丰富的高分辨率图像的自动识别分类。     

迁西县土地利用空间变化分析

针对迁西县土地利用的分布特征和空间变化问题,该文运用面向对象的分类方法对迁西县2009、2013和2017年3期遥感影像进行土地利用分类,再运用转移矩阵、变化图谱和信息熵对研究区土地利用类型的数量、空间分布特征和系统结构有序性逐一进行分析。结果表明:2009—2017年,迁西县居民地、工矿用地和其他用地面积增加,林地、耕地、园地和水体面积减少,但是林地和耕地依然是研究区最主要的土地利用类型;林地和耕地的减少主要是由居民地和工矿用地引起的,且林地和耕地之间的互相转化最为频繁;总体来说,迁西县土地利用朝稳中向好的趋势发展。该研究可以为迁西县的土地利用结构调整、土地利用规划和土地的可持续发展提供一定的理论参考。