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.     

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.     

Land cover and land use change related to shrimp farming in coastal areas of Quang Ninh,Vietnam using remotely sensed data

Rapid development of shrimp farming may lead to unrecognized and undesirable changes of land cover/land use patterns in coastal areas. Of special concern is the loss of mangrove forest in coastal areas such as Quang Ninh, Vietnam, which is adjacent to the World Heritage-listed Ha Long Bay. Understanding the status and changes of land cover/land use for coastal shrimp farms and mangrove forests can support environmental protection and decision-making for sustainable development in coastal areas. Within this context, this paper uses the 1999/2001 Landsat ETM+ and the 2008 ALOS AVNIR-2 imagery to investigate the contraction and expansion of shrimp farms and mangrove forests in coastal areas of Ha Long and Mong Cai, which now have a high concentration of intensive and semi-intensive shrimp farms. Images were separately analyzed and classified before using post-classification comparisons to detect land cover/land use changes in the study area. The results of this study found that the area of mangrove forest has been reduced by an estimated 927.5 ha in Ha Long and 1,144.4 ha in Mong Cai, while shrimp farming areas increased by an estimated 1,195.9 and 1,702.5 ha, respectively, over the same period. The majority of shrimp farms in Mong Cai were established at the expense of mangrove forest (49.4 %) while shrimp farms in Ha Long were mainly constructed on areas previously occupied by bare ground (46.5 %) and a significant proportion also replaced mangroves (23.9 %). The remarkable rate of mangrove loss and shrimp farming expansion detected in this study, over a relatively short time scale indicate that greater awareness of environmental impacts of shrimp farm expansion is required if this industry is to be sustainable, the important estuarine and coastal marine ecosystems are to be protected over the long term, and the capturing and storing of carbon in mangrove systems are to be enhanced for global climate change mitigation and for use as carbon offsets.     

Interstadial records of the last glacial period at Pantai Remis,Malaysia

Two eustatic high sea stands during the last glacial period are recognised at Pantai Remis. These highstands, lower than present-day sea-level, are tropical manifestations of the ameliorating interstadial climate during the Weichselian/Devensian/Wisconsin glaciation in the Northern Hemisphere. The earlier highstand corresponds to a sea-level of 14.6 m below mean sea-level (MSL). It is interpreted as synchronous with Oyxgen Isotope Stage 5a and is correlated with other known sea-level curves in other parts of the world. The younger high sea stand, dated 55810 ± 1140 to 53870 ± 1400 yr BP, indicates sea-level of 4.3 m below MSL. It represents an interstadial equivalent that lasted for at least 2000 yr, whereas the earlier interstadial period indicates a minimum duration of twice this amount or very likely even longer, as reflected from the thickness of the accumulated deposits. The palynological records indicate that during interstadial times, climatic stability in the tropics is attained and was sufficiently long for vegetation to thrive and develop. The palynofloral constituents of the earlier interstadial phase at Pantai Remis showed the establishment of vegetation in a coastal setting, initiated by Pandanus swamp forests. Simultaneously, mangrove swamp flourished in the lower lying parts of the area, hence the presence of direct tidal influence is evident. Both the Pandanus and mangrove swamps were succeeded by mixed freshwater swamp forests of a Campnosperma–Calophyllum assemblage. Subsequently a slightly open and somewhat drier mixed swamp forest prevailed, marked by the increase in fern spore representation. The later interstadial phase showed shorter vegetation successions, which commenced on the landward edge of a mangrove swamp forest. The mangrove was successively replaced by strand forest, as indicated by domination of Casuarina equisetifolia. The palynological assemblages in both the interstadial periods indicate similarity to the present-day coastal vegetation. This implies that during the interstadials the climate in the lowlands of Peninsular Malaysia and presumably throughout the equatorial region, was as that prevailing today. © 1997 John Wiley & Sons, Ltd.     

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.     

Relationship in metastasis of coal fire and land use/cover using thermal imagery and support vector machine classifier

The objective of the present study was to delineate temporal and spatial changes in the coal fire and land use/cover within Bastacolla area of Jharia coal field. Studying this variation helped to decipher interconnection among the dynamics of the coal fire and concomitant changes in land use/cover. The detection of coal fires during a span of 14 years along with transitioning land use/cover was cost-effective and enabled planning for management of coal resources and environment. Landsat series of satellite data of 2002, 2009, 2013, and 2016 were processed for generating land surface temperature profiles vis-a-vis classified land use/cover of the study area. A single cut-off temperature was derived for mapping of coal fires using land surface temperature profile from 2002 to 2016. The satellite images were classified using support vector machines, and for depicting land use/cover change, post-classification change detection was done. Classification accuracy obtained was excellent with kappa coefficient ranging from 0.897 for classified image of 2002 to 0.799 for classified image of 2016. Results revealed that coal fires had shifted to the central west part of the area. Furthermore, pockets of coal fire from northern and eastern part of the study area have diminished. OB dumps and coal quarry/coal dump may be attributed towards the spatial change in coal fire while; OB dumps showed connotation with the highest temperature zones. Ground verifications for temperature profiles and coal fires were carried out using thermal camera which enunciated good agreement with results.     

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.     

吉林西部多时相遥感数据分类方案的构建及应用

为了深化遥感监测方法在生态环境调查中的应用,本文以吉林西部为试验区,设计了一种多时相遥感数据分类方案。该方案以物候信息为主,结合地物特征变量(植被、水体和土地信息)构建的多维特征空间数据集用于土地覆被分类。该遥感分类方案提取了9种地表覆被类型,结果表明:地表植被季节变化信息和土地利用信息的引入能明显改善土地覆被的分类精度;与基于原始波段的分类方案相比,多时相遥感数据分类方案的分类精度最好,总体分类精度为95.50%,Kappa系数为95.04%。     

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.     

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.     

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.     

Quarrying and its effect on vegetation cover for a sustainable development using high-resolution satellite image and GIS

Huge vegetal losses caused by unsustainable quarrying practices have limited the role played by vegetation cover in mitigating the global impact of climate change. There is a need for a holistic study that will employ remotely sensed data in GIS domain to determine the extent of the effect of quarrying activities on vegetation cover in the study area. The need for extraordinary details with high accuracy and enhanced imagery has necessitated the use of Geo-Eye-1 satellite imagery with 1-m resolution for 2015. The study was carried out in Ebonyi State where 27 quarry sites were studied in six local government areas where mining activities were operated. The study employed geographical information system, Garmin eTrex GPS receiver and global positioning system. Geographical information was processed using ArcGIS to map the patterns and extent of land use. The findings from the study showed that the visual interpretation of the satellite image confirmed that white patches on the imagery were areas affected by intensified quarrying activities. The very dark green colours were interpreted as forest with no observed human activities. The supervised classification of land use also showed that quarrying activities occupied 0.1% of the study area with a total of 402.855 ha of green cover lost in Ebonyi State due to quarrying operations. Quarrying practices have destroyed arable lands, economic trees and forests in the area. This implies that there is a need for policies to be enforced with strict adherence to sustainable quarrying guidelines and consistent monitoring.     

Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–Markov algorithms to monitor and predict future change

Mangrove forest stores large organic carbon stocks in a setting that is highly vulnerable to climate change and direct anthropogenic influences. As such there is a need to elucidate the causes and consequences of land use change on these ecosystems that have high value in terms of ecosystem services. We examine the areal pattern of land types in a coastal region located in southern Iran over a period of 14 years to predict future loss and gain in land types to the year 2025. We applied a CA–Markov model to simulate and predict mangrove forest change. Landsat satellite images from 2000 to 2014 were used to analyze the land cover changes between soil, open water and mangroves. Major changes during this period were observed in soil and water which could be attributed to rising sea level. Furthermore, the mangrove area in the more seaward position was converted to open water due to sea-level rise. A cellular automata model was then used to predict the land cover changes that would occur by the year 2025. Results demonstrated that approximately 21 ha of mangrove area will be converted to open water, while mangroves are projected to expand by approximately 28 ha in landward direction. These changes need to be delineated to better inform precise mitigation and adaptation measures.     

Vegetation recovery and landscape change assessment at Chiufenershan landslide area caused by Chichi earthquake in central Taiwan

This study discusses vegetation recovery and land cover change with reference to the Chiufenershan landslide, a major disaster caused by the Chichi earthquake, 21 September 1999. Image classification technology, landscape indicators from multi-temporal remotely sensed data and a field survey provide the data. Image differencing methods and threshold values coupled with pre- and post-quake satellite images were used. Multi-temporal images in combination with various vegetation indices were drawn on to classify land cover patterns and discuss differences and suitability of indices. Landscape indicators and field investigations fed into an investigation of vegetation recovery and landscape change. The study results show that the best image classification system is original wavebands coupled with a cropping management factor index (CMFI). The land cover analysis shows that areas of forest and grass are increasing and areas of landslide are decreasing. From the field investigation, because the left and right sides of the landslide area were not disturbed by the earthquake, their calculated similarity index is the highest (30.08%). Miscanthus floridulus is the most dominant pioneer plant at the landslide collapse area with an importance value index (IVI) of 63.6%.     

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.     

Effects of land use changes on soil erosion in a fast developing area

Land use changes extensively affect soil erosion, which is a great environmental concern. To evaluate the effect of land use change on soil erosion in fast economic developing areas, we studied land use changes of Guangdong, China, from 2002 to 2009 using remote sensing and estimated soil erosion using the Universal Soil Loss Equation. We calculated the areas and percentage of each land use type under different erosion intensity and analyzed soil erosion changes caused by transitions of land use types. In addition, the impact of land use change on soil erosion in different river catchments was studied. Our results show that forest and wasteland land conversions induce substantial soil erosion, while transition from wasteland to forest retards soil loss. This suggests that vegetation cover changes significantly influence soil erosion. Any conversion to wasteland causes soil erosion, whereas expansion of forests and orchards mitigates it. The most significant increase in soil erosion from 2002 to 2009 was found in the Beijiang catchment corresponding to the transition from forest/orchard to built-up and wasteland. Soil erosion in the Xijiang catchment accelerated in this period due to the enormous reduction in orchard land. In Hanjiang catchment, erosion was alleviated and vegetation coverage greatly expanded owing to considerable transitions from wasteland and cropland to orchards. Field investigations validated our estimations and proved the applicability of this method. Measures including protecting vegetation, strict control of mining as well as reasonable urban planning should be taken to prevent successive soil erosion.     

基于多源遥感影像的长春市城市建成区提取

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。     

Assessing land use and land cover change in the Wassa West District of Ghana using remote sensing

Vast tracts of forests are lost globally every year especially in the developing countries of the tropics due to various human activities such as lumbering, farming, bush fires, surface mining and urbanization. The rainforest in Ghana has experienced rapid depletion since the 1980s. The impact of deforestation is widespread, affecting the livelihoods of local people and disrupting the tropical ecosystem. There is a serious concern in the study area about climatic change, soil erosion, siltation of rivers and loss in biodiversity which have an adverse impact on traditional medicinal plants of the local people. The study examined the extent of land cover change through image differencing of Landsat TM 1986 and 2002. The image classification indicated that, vegetative cover from 1986 to 2002 has been reducing whiles land use activities have been increasing. Closed canopy, open canopy and plantation have significantly diminished and land use activities especially built ups, farms, mining and openfields are more than doubled. The driving forces for the change in land cover are population growth, lumbering, socio-economic and cultural practices of the people. Lumbering and mining have been some of the major causes of the changing landscape in primary forest. Also the reliance on wood for domestic energy and the need to increase food productivity to feed growing population have also contributed greatly to the rapid depletion of the vegetative cover.     

Estimation of land coverage from a land cover classification derived from remotely sensed data

Remotely sensed data are an attractive source of land cover information. In many applications the required information relates to the extent or coverage of land cover class(es) in a region, which is generally derived from a count of the pixels allocated to the class(es) of interest in a classification. A highly accurate classification is not required for the derivation of accurate estimates of class coverage, provided the classification is accompanied by appropriate information on its quality. For instance, the information on classification quality contained in the classification confusion matrix can be used to significantly increase the accuracy of the estimates of land coverage. This is illustrated with reference to a case study focused on the estimation of despoiled land coverage in administratively defined local district in industrial South Wales from Landsat TM data. The accuracy of the investigation was assessed relative to a map of despoiled land cover for this region produced by conventional methods. From an image classification of moderate accuracy, the classification accuracy ranged form 57–83% between the districts investigated, a pixel count provided estimated of despoiled land coverage that were only poorly correlated to the mapped coverage;r = 0.27. Using the information on the pattern of error in the class allocation contained in the classification confusion matrix the estimation accuracy was increased significantly, with a correlation ofr = 0.81 observed between the remote sensing based estimate and the mapped land coverage. Furthermore, the r.m.s. error in despoiled land coverage estimation was reduced by approximately half, to less than 1% district area, when the classification was used in conjunction with information on the pattern of classification error.