Using Landsat data to determine land use changes in Datong basin,China

The aim of this study was to determine land use changes in Datong basin using multitemporal Landsat data for the period of 1977–2006. Four dates of Landsat images from 1977, 1990, 2000, and 2006 were selected to classify the study area. Based on the supervised classification method of maximum likelihood algorithm, images were classified into six classes: water, urban, forest, agriculture, wetland, and barren land. A multidate postclassification comparison change detection algorithm was used to determine changes in land use in four intervals. It is found that (1) urban land area increased 213% due to urbanization that resulted from rapid increase of urban population and high-speed economic development, (2) agriculture area increased 34.0% due to land reclamation that resulted from rapid increase of rural population and improvement of irrigation capacity, (3) forest area decreased 20.9% due to deforestation for urban area and agricultural use, (4) barren land area decreased 78.2% due to cultivation for agricultural use, and (5) water and wetland decreased 39.1 and 67.1%, respectively, due to exploitation of surface water and decrease of recharge from groundwater to surface water that resulted from over exploitation of groundwater.     

Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine

Human activities in many parts of the world have greatly changed the natural land cover. This study has been conducted on Pichavaram forest, south east coast of India, famous for its unique mangrove bio-diversity. The main objectives of this study were focused on monitoring land cover changes particularly for the mangrove forest in the Pichavaram area using multi-temporal Landsat images captured in the 1991, 2000, and 2009. The land use/land cover (LULC) estimation was done by a unique hybrid classification approach consisting of unsupervised and support vector machine (SVM)-based supervised classification. Once the vegetation and non-vegetation classes were separated, training site-based classification technology i.e., SVM-based supervised classification technique was used. The agricultural area, forest/plantation, degraded mangrove and mangrove forest layers were separated from the vegetation layer. Mud flat, sand/beach, swamp, sea water/sea, aquaculture pond, and fallow land were separated from non-vegetation layer. Water logged areas were delineated from the area initially considered under swamp and sea water-drowned areas. In this study, the object-based post-classification comparison method was employed for detecting changes. In order to evaluate the performance, an accuracy assessment was carried out using the randomly stratified sampling method, assuring distribution in a rational pattern so that a specific number of observations were assigned to each category on the classified image. The Kappa accuracy of SVM classified image was highest (94.53 %) for the 2000 image and about 94.14 and 89.45 % for the 2009 and 1991 images, respectively. The results indicated that the increased anthropogenic activities in Pichavaram have caused an irreversible loss of forest vegetation. These findings can be used both as a strategic planning tool to address the broad-scale mangrove ecosystem conservation projects and also as a tactical guide to help managers in designing effective restoration measures.     

Forecasting future changes in Manzala Lake surface area by considering variations in land use and land cover using remote sensing approach

This study assesses the changes in surface area of Manzala Lake, the largest coastal lake in Egypt, with respect to changes in land use and land cover based on a multi-temporal classification process. A regression model is provided to predict the temporal changes in the different detected classes and to assess the sustainability of the lake waterbody. Remote sensing is an effective method for detecting the impact of anthropogenic activities on the surface area of a lagoon such as Manzala Lake. The techniques used in this study include unsupervised classification, Mahalanobis distance supervised classification, minimum distance supervised classification, maximum likelihood supervised classification, and normalized difference water index. Data extracted from satellite images are used to predict the future temporal change in each class, using a statistical regression model and considering calibration, validation, and prediction phases. It was found that the maximum likelihood classification technique has the highest overall accuracy of 93.33%. This technique is selected to observe the changes in the surface area of the lake for the period from 1984 to 2015. Study results show that the waterbody surface area of the lake declined by 46% and the area of floating vegetation, islands, and land agriculture increased by 153.52, 42.86, and 42.35% respectively during the study period. Linear regression model prediction indicates that the waterbody surface area of the lake will decrease by 25.24% during the period from 2015 to 2030, which reflects the negative impact of human activities on lake sustainability represented by a severe reduction of the waterbody area.     

2000-2014年蒙古高原植被覆盖时空变化特征及其对地表水热因子的响应

利用MODIS NDVI数据、同期地表水热组合数据和植被类型数据,对2000-2014年蒙古高原生长季和三季（春、夏、秋季）植被覆盖时空演变特征及其对地表水热因子响应模式进行分析。研究表明：这15 a来,蒙古高原生长季及三季归一化植被指数NDVI均呈增加趋势,且呈显著增加趋势区域主要集中在内蒙古地区,一定程度上反映了该地区生态恢复工程的有效性。研究区植被覆盖变化与地表水分指数LSWI有密切的关系,因此证明研究区植被覆盖的增加归因于自然和人为因素的共同作用。不同类型植被NDVI均呈增加趋势,其中荒漠植被NDVI增加最明显,森林植被增加平缓,且存在季节性差异。此外,不同类型植被NDVI受水热因子影响也存在季节性差异。     

三江平原土地利用变化对水量平衡的影响

三江平原土地利用（LU）变化及其引发的大规模水资源开发利用活动对当地水文循环产生了明显扰动。为明确三江平原水量平衡特征及其存在的问题,基于2000—2014年土地利用连续变化情景的水文循环仿真模型,在其他数据和参数不变的前提下,建立假设模型和预测模型,分别模拟2000年起已控制LU变化和2014年后控制LU变化2种假设LU管控情景的水文过程,对比3种情景下的水量平衡。结果显示:如果2000年开始就采取积极的LU管控措施,此后的15a将有效减少三江平原对外调水的依赖,改善地下水超采的现状,甚至使平原区地下水储量年均增加3.08亿m3;如果2014年才实施土地利用管控措施,巨大的水资源开发利用惯性将使平原区地下水年均亏空量达到14.26亿m3,甚至使全区域的蓄水量由盈转亏,年均亏损4.25亿m3。三江平原要实现各层次的水量收支平衡,除了尽快控制高耗水LU类型的过快增长,更加积极的水资源调配及相关工程的建设势在必行。     

定量评估我国西北干旱区土地利用变化对植被指数的影响

在全球变化的背景下,定量区分人类活动和气候波动对干旱区植被的影响具有重要意义。采用多种统计学方法,分析了我国西北干旱区1990-2010年土地利用/覆被变化（LUCC）和1982-2010年归一化植被指数（NDVI）的时空变化特征,并定量评估了LUCC对NDVI变化的影响。结果表明：1990-2010年,西北干旱区耕地增加量最多,高达13 476 km²,其次是林地和水域,各地类增加的面积主要来自草地（12 590 km²）和未利用地（6 025 km²）。各土地类型变化速度快慢依次为：耕地 > 建设用地 > 水域 > 林地 > 草地 > 未利用地。2000-2010年,研究区土地利用程度综合指数（0.79）明显高于1990-2000年（0.23）,表明近年来人类活动对土地利用变化的影响程度显著增强。1982-2010年,西北干旱区NDVI呈增加态势,但近年来（2002-2010年）NDVI略有下降。其中,1990-2000年,LUCC对西北干旱区NDVI总变化的贡献率较低,仅为2.9%;而1990-2010,LUCC的贡献率为26.7%,表明气候变化对植被指数变化的贡献率高达73.3%。     

典型喀斯特石漠化地区植被动态监测与土地利用变化的影响分析

基于2001至2014年MOD13Q1数据集、数字地面高程数据以及中梁山地区多期土地覆盖数据,进行植被覆盖度（FVC）估算及其变化趋势模拟、多期土地利用转移矩阵分析,探讨中梁山地区植被覆盖度动态变化特征、土地利用的时空变化特征以及土地利用和地形同植被覆盖度间的响应机制。研究结果表明：中梁山76.69%的区域为植被改善区,退化区面积占总面积的10.12%,存在明显的改善趋势,生态情况得到良好恢复;人类活动对中梁山区域影响方式主要表现为耕地向林地和建设用地转化的特点;植被生长趋势的空间异质性与坡度有关,坡陡区植被改善面积约为退化面积的14倍,缓坡区仅为7倍;植被退化现象受人览活动的影响较大,而人类晃动对植被改善影响较小,植被改善主要与植物的自然生长演替有关。     

Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island,Malaysia

Land surface temperature (LST) plays an important role in local, regional and global climate studies. LST controls the distribution of the budget for radiation heat between the atmosphere and the earth’s surface. Therefore, it is important to evaluate abrupt changes in land use/land cover (LULC). Penang Island, Malaysia has been experiencing a rapid and drastic change in urban expansion over the past two decades due to growth in industrial and residential areas. The aim of this study was to investigate and evaluate the impact of LST with respect to land use changes in Penang Island, Malaysia. Three supervised classification techniques known as maximum likelihood, minimum distance-to-mean and parallelepiped were applied to the images to extract thematic information from the acquired scene by using PCI Geomatica 10.1 image processing software. These remote sensing classification techniques help to examine land-use changes in Penang Island using multi-temporal Landsat data for the period of 1999–2007. Training sites were selected within each scene and seven land cover classes were assigned to each classifier. The relative performance of each technique was evaluated. The accuracy of each classification map was assessed using a reference data set consisting of a large number of samples collected per category. Two Landsat satellite images captured in 1999 and 2007 were chosen to classify the LULC types using the maximum likelihood classification method, determined from visible and near-infrared bands. The study revealed that the maximum likelihood classifier produced superior results and achieved a high degree of accuracy. The LST and normalised difference vegetation index (NDVI) were computed based on changes in LULC. The results showed that the urban (highly built-up) area increased dramatically, and grassland area increased moderately. Inversely, barren land decreased obviously, and forest area decreased moderately. While urban (minimally built-up) area decreased slightly. These changes in LULC caused at significant difference in LST between urban and rural areas. Strong correlation values were observed between LST and NDVI for all LULC classes. The remote sensing technique used in this study was found to be efficient; it reduced the time for the analysis of the urban expansion, and it was found to be a useful tool to evaluate the impact of urbanisation with LST.     

A monitoring framework for land use around kaolin mining areas through Landsat TM images

Sustainable management of land requires regular acquisition of qualitative information regarding the status of its use. It is especially important to track the changes relating to the land’s competitive development needs such as mining. The field-based monitoring of a mine with a wide footprint is expensive and time-consuming. Remote sensing techniques have been developed and demonstrated as cost-effective alternatives for the conventional methods of land use/land cover (LULC) monitoring. In this study, the land cover changes that occurred between the year of 2000 and 2009 in a kaolin mining and processing area in the Kutch region of India are mapped using two Landsat-5 Thematic Mapper (TM) images. For this purpose, the spectral signature of the land covers including vegetation cover and kaolin were determined and matched filtering (MF) method was applied to classify the images. The overall accuracy of the classified 2009 image was estimated for the kaolin and the vegetation cover to 89.5 and 86.0 % respectively. The change in the land use which occurred from 2000 to 2009 were quantified and analysed for both classes. This study provided a practical framework for rapid mapping of the land cover changes around open-cut kaolin mining area using freely available Landsat data.     

黑河中游金塔地区生态环境变化遥感监测

利用1990年和2000年的TM遥感资料,在经过定标、几何校正和图像增强等图像处理的基础上对土地覆盖类型进行解译,从而实现黑河中游金塔地区的生态环境遥感动态监测.从监测结果计算出黑河中游金塔地区2000年的生态环境相对于1990年的生态环境变化如下:人工用地增加34.17%,农业用地增加19.47%,水体增加8%,湿地增加6.3%,未利用土地增加了1.73%,自然/半自然植被减少42.78%.并对这些生态环境变化的原因进行了初步探讨。     

Riverine flood mapping and impact assessment using remote sensing technique: a case study of Chenab flood-2014 in Multan district,Punjab, Pakistan

The Multan district is mainly prone to riverine floods but has remained understudied. Chenab flood-2014 was the worst flood that this district experienced in recorded history. This study applies remote sensing (RS) techniques to estimate the extent, calculate duration, assess the major causes and resulting impacts of the flood-2014, using Landsat-8 OLI images. These images were obtained for pre-flood, during-flood and post-flood instances. Secondary data of flood causing factors were obtained for comprehensive analysis. Spatially trained and validated datasets were obtained through Google Earth platform and Global positioning system. The supervised classification with maximum likelihood algorithm was used to classify land use and land cover of the study area. The Modified Normalized Difference Water Index was utilized to detect flood inundation extent and duration, and Normalized Difference Vegetation Index was utilized to monitor vegetation coverage and changes. The analysis allowed us to assess flood causes, and calculate the extent of the flooded areas with duration and recession, as well as damages to standing crops and built-up areas. The results revealed that the flood-2014 occurred due to heavy rains in early September in upper Chenab catchment. The flood inundation continued for around two months, which heavily affected agriculture and built-up areas. The present study introduces practical use of RS techniques to provide basis for effective flood inundation mapping and impact assessment, as an application for early flood response and recovery in the world.

     

SAR polarimetric decomposition with ALOS PALSAR-1 for agricultural land and other land use/cover classification: case study in Rajasthan,India

Synthetic aperture radar (SAR) has operational applications in crop mapping and monitoring in many countries due to the distinct backscatter signature at various stages of crop growth. Polarimetric analysis of SAR data from different satellites was used for information extraction from different types of scatters in imaged terrain. The scattering processes were analyzed through the received scatter matrix derived from the target decomposition of SAR data. Three decomposition techniques, namely Freeman–Durden, Cloude–Pottier and Touzi decomposition of the ALOS PALSAR-1 data, were used in this study to extract land use/cover information with a specific emphasis on agriculture. The decomposed output parameters from these techniques were classified with supervised classifier of support vector machine (SVM) using region of interest (ROI) selected land use/cover classes. An accuracy assessment for the classified output was carried out using the ROI. The Ramgarh village in Jaisalmer district of Rajasthan with the predominance of agricultural land, sand dunes and settlements was chosen as the study area. Freeman–Durden decomposition resulted in the highest overall accuracy of about 85% in the land use/cover classification among the three decomposition techniques adopted in the study. It was also observed that the accuracy of land use/cover mapping derived from Cloude–Pottier and Touzi decompositions improved with the use of eigenvalues in the SVM classification. Higher accuracies in the classification of agriculture land were noted with all the three decomposition techniques. The four parameters of Cloude–Pottier (H, A, α, β) and Touzi (α _s, Φ _s, ψ, τ) decompositions improved the classification accuracy for all the classes due to eigenvalues. The overall classification accuracy was above 88% for both the decomposition techniques with four parameters. The soil moisture values for agriculture land and sand dunes were validated through soil moisture maps generated using Oh 1992 and 2004 models.     

Geospatial tools for assessing land degradation in Budgam district,Kashmir Himalaya,India

Land degradation reduces the ability of the land to perform many biophysical and chemical functions. The main aim of this study was to determine the status of land degradation in the Budgam area of Kashmir Himalaya using remote sensing and geographic information system. The satellite data together with other geospatial datasets were used to quantify different categories of land degradation. The results were validated in the field and an accuracy of 85% was observed. Land use/land cover of the study area was determined in order to know the effect of land use on the rate of land degradation. Normalized differential vegetation index (NDVI) and slope of the area were determined using LANDSAT-enhanced thematic mapper plus (ETM+) data, advanced space borne thermal emission and reflection radiometer, and digital elevation model along with other secondary data were analysed to create various thematic maps, viz., land use/land cover, geology, NDVI and slopes used in modelling land degradation in the Kashmir Himalayan region. The vegetation condition, elevation and land use/land cover information of the area were integrated to assess the land degradation scenario in the area using the ArcGIS ‘Spatial Analyst Module’. The results reveal that about 13.19% of the study area has undergone moderate to high degradation, whereas about 44.12% of the area has undergone slight degradation.     

1985-2014年漓江流域景观格局动态变化研究

文章以漓江流域为研究对象,应用1985年、2000年和2014年3期遥感影像,提取土地利用数据,采用土地利用动态度、土地利用转移矩阵等方法,分析1985-2014年漓江流域土地利用的演变趋势;结合Fragstats4.2景观分析工具,识别了该流域景观格局的动态变化。结果表明：1985-2014年,漓江流域土地利用整体变化幅度不大,主要表现为林地、耕地和草地面积减小,湿地、居民及城乡建设用地和未利用土地面积增加,且变化集中发生在2000-2014年。同时景观水平上破碎化和复杂化程度加剧,聚集程度和斑块连续程度有待提高;类型水平上林地景观破碎化层度增加,耕地和草地斑块面积增加,湿地和居民及城乡建设用地的聚集度较高,分布集中。     

Multi-temporal landsat images based on eco-environmental change analysis in and around Chah Nimeh reservoir,Balochestan (Iran)

Chah Nimeh reservoirs have served as a water storage facility, especially during droughts over the last three decades. It is also an important wintering site for migratory birds. In this study, thematic mapper time-series data were derived from Landsat images for prolonged droughts that occurred in two satellite images (2002 and 2011). The data derived from these images were used for the detection of changes in land cover and water storage in the reservoirs. First, a vegetation cover map was produced using soil-adjusted vegetation index and field sampling. Subsequently, land use/cover maps were generated using supervised and hybrid image classification method. Using the spatial change detector (SCD v1.0) software extension, the layers were combined and the change map was generated. The overall accuracy of the produced thematic images was assessed in regards to quantity and allocation disagreements. A total of five classes were defined in this investigation: deep water, shallow water, vegetation, salt land and bare land. The results showed that during the period of study, water volume reduced and vegetation cover increased, especially around the reservoirs that are important as shelter for wintering migratory birds. Comparison of land use/cover maps showed the increase in total available surface of shallow water, which indicated an increase in the habitats for surface feeding and diving birds.     

21世纪前十年的中国土地覆盖变化

土地覆盖变化是陆地生态系统变化的重要组成部分与驱动因素。在全球变化、生态环境建设、经济高速发展等因素的影响下,21世纪前十年中国土地覆盖发生了显著变化,对此变化的监测和分析不但能支持中国碳源/汇的评估和碳收支估算,还可为生态环境变化评估提供基础数据。本研究在面向对象（object-based）的分类技术支持下,利用LandsatTM/ETM数据和HJ-1卫星数据,结合大量外业调查数据生产了30m分辨率的2000年、2010年中国土地覆盖数据（ChinaCover）;采用像元二分法生产了植被覆盖度数据。利用这两个数据集对中国土地覆盖10年的变化特点进行了分析。结果表明,人工表面和林地呈增加趋势,而耕地、湿地和草地面积呈减少的趋势;人工表面的快速增加和耕地面积的大规模减少是这一时期中国土地覆盖变化的最主要特点;土地覆盖类型转换中,耕地转换为人工表面的区域主要集中在我国中东部地区,耕地转换为林地和草地的区域主要分布在退耕还林还草的重点区域,耕地的扩张主要来自三江平原和新疆绿洲的农业开发。以植被覆盖度为评估指标显示森林、灌丛和草地质量总体呈上升趋势,但在汶川地震重灾区、横断山以及武夷山等局部地区的森林质量呈退化趋势;塔里木盆地周围、青藏高原东部、太行山、吕梁山等地区的灌丛植被覆盖度有所下降;内蒙古中部、青藏高原西南部、新疆天山南部、呼伦贝尔等地区的草地出现退化现象。     

Characterizing spatiotemporal dynamics of land cover with multi-temporal remotely sensed imagery in Beijing during 1978–2010

To facilitate urban planning and management in fast-growing metropolitan areas, it is highly necessary to detect the spatiotemporal changes of different land cover types. This study aimed at identifying Beijing’s land cover types and detecting the characteristics of their spatiotemporal changes using time series remote sensing and GIS techniques from 1978 to 2010. A total of 16 Landsat MSS/TM/ETM+ images were collected during the spring and late summer seasons. After preprocessing the dataset, artificial neural network was used to perform the land cover classification. Consequently, four maps were generated for 1978, 1992, 2000, and 2010, with six classes (agriculture, woodland, grassland, water, urban, and barren land) according to the level I classification scheme. Three transition matrices were constructed to represent all possible changes that occur in the landscape. The results showed that agriculture, barren land, and grassland had an increase in area, while urban, water, and woodland had a reduction within the study area. A total of 2,032.341 km² agriculture was reduced and 2,359.146 km² woodland was increased. In the three periods for 1978–1992, 1992–2000, 2000–2010, agriculture had the largest amount of transfer out primarily to urban class around central urban areas and woodland had the most transfer in mainly from barren land in mountainous areas. More importantly, the driving forces analysis including economic development, growth of population and construction areas, and institutional policies was conducted to find out the primary factors inducing the land cover change.     

Fine classification of construction land using high-resolution remote sensing images: a case study in planning restricted zone of nuclear power plant

Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68).     

Analyzing spatiotemporal land use/cover dynamic using remote sensing imagery and GIS techniques case: Kan basin of Iran

Land use/land cover (LU/LC) that are significant elements for the interconnection of human activities and environment monitoring can be useful to find out the deviations of saving a maintainable environment. Remote sensing is a very useful tool for the affair of land use or land cover monitoring, which can be helpful to decide the allocation of land use and land cover. Supervised classification-maximum likelihood algorithm in GIS was applied in this study to detect land use/land cover changes observed in Kan basin using multispectral satellite data obtained from Landsat 5 (TM) and 8 (OLI) for the years 2000 and 2016, respectively. The main aim of this study was to gain a quantitative understanding of land use and land cover changes in Kan basin of Tehran over the period 2000–2016. For this purpose, firstly supervised classification technique was applied to Landsat images acquired in 2000 and 2016. The Kan basin was classified into five major LU/LC classes including: Built up areas, garden, pasture, water and bare-land. Change detection analysis was performed to compare the quantities of land cover class conversions between time intervals. The results revealed both increase and decrease of the different LU/LC classes from 2000 to 2016. The results indicate that during the study period, built-up land, and pastures have increased by 0.2% (76.4 km²) and 0.3% (86.03 km²) while water, garden and bare land have decreased by 0, 0.01% (3.62 km²) and 0.4% (117.168 km²), respectively. Information obtained from change detection of LU/LC can aid in providing optimal solutions for the selection, planning, implementation and monitoring of development schemes to meet the increasing demands of human needs in land management.     

Land use/land cover classification of the vicinity of Lake Chad using NigeriaSat-1 and Landsat data

Lake Chad in Africa experienced severe droughts in the 1970s and 1980s and overexploitations of water resulting in a decline of water level in the Lake and surrounding rivers. Such droughts and overexploitation of water caused a significant change of land use and water management practices over the last 50 years. Understanding the change of land use and land cover is, therefore, crucial to understand disturbance of the water cycle around the Lake. The present study analyzed satellite images of Lake Chad from Landsat-MSS, Landsat-TM, and NigeriaSat-1 to investigate the change of land cover during three time periods: the 1970s, 1991, and 2006. Unsupervised and supervised classifications were performed for the land cover analysis. The overall accuracies of the classification of Landsat-TM and NigeriaSat-1 are 93.33 and 95.24 %, respectively. It is evident that a 35 % decrease of waterbodies occurred from the 1970s to 1991, but a slight increase of 0.9 % occurred between 1991 and 2006. The Shrubland has overtaken most of the waterlog areas, as much as seven times of what it was in the 1970s. The interpretation of NigeriaSat-1 images indicates that NigeriaSat-1 has similar capabilities to Landsat-TM and Landsat-MSS for the detection of various land cover types because land cover and land use features are discernible on the processed images, especially depletion of waterbodies and vegetation. These are similarities justify the quality of the NigeriaSat-1 images for land cover and land use analysis.