Land use and land cover change detection by using principal component analysis and morphological operations in remote sensing applications (original) (raw)
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International Journal of Remote Sensing, 2018
The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l'Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM +) (2006) was high compared with that from the PC image of Landsat ETM + (2006). From 1986-2006 the area covered by settlements increased by 0.8% (230,380.00 km 2), agricultural land decreased by 7.5% (1009.40 km 2), vegetation cover decreased by 0.9% (114.00 km 2) while waterbody increased by 0.2% (25.91 km 2). Also, from 1986-2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale. The PCA technique has been used quite widely to study changes in land cover and land use in many 'developed' countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.
Land use, land cover change analysis with multitemporal remote sensing data
Proceedings of SPIE - The International Society for Optical Engineering
Presently, unplanned changes of land use have become a major problem. Most land use changes occur without a clear and logical planning with little attention to their environmental impacts. In last four-decade, urban growth in Delhi has occurred rapidly in some unwanted direction and destroyed valuable agriculture lands in its surround. Rapid changes in land use / cover occurring over large areas; remote sensing technology is an essential and useful tool in monitoring of this area. Monitoring of land use/cover change are increasingly reliant on information derived from remotely sensed data. Such information provides the data link to other techniques to understand the human processes behind these changes. Specially, in agricultural area in suburb (or countryside) of a metropolitan city like Delhi. In this paper different change detection approaches (such as Post classification comparison and spectral change detection techniques) were evaluated with available images of National Capital Territory of Delhi during 1973 to 2001. These techniques were analyzed independently, using the concept of well-known procedures to define the best approach/methodology for addressing the change detection issues in this study.
2009
Change detection analyze means that according to observations made in different times, the process of defining the change detection occurring in nature or in the state of any objects or the ability of defining the quantity of temporal effects by using multitemporal data sets. There are lots of change detection techniques met in literature. It is possible to group these techniques under two main topics as supervised and unsupervised change detection. In this study, the aim is to define the land cover changes occurring in specific area of Kayseri with unsupervised change detection techniques by using Landsat satellite images belonging to different years which are obtained by the technique of remote sensing. While that process is being made, image differencing method is going to be applied to the images by following the procedure of image enhancement. After that, the method of Principal Component Analysis is going to be applied to the difference image obtained. To determine the areas that have and don't have changes, the image is grouped as two parts by Fuzzy C-Means Clustering method. For achieving these processes, firstly the process of image to image registration is completed. As a result of this, the images are being referred to each other. After that, gray scale difference image obtained is partitioned into 3x3 nonoverlapping blocks. With the method of principal component analysis, eigenvector space is gained and from here, principal components are reached. Finally, feature vector space consisting principal component is partitioned into two clusters using Fuzzy C-Means Clustering and after that change detection process has been done.
Land use/cover change detection based on remote sensing data (A case study; Neka Basin)
Agriculture and Biology Journal of North America, 2010
Several regions around the world are currently under rapid, wide-ranging changes of land cover. Satellite remote sensing techniques have proven to be cost efficient in extensive land cover changes. This study illustrates the effect of land use/cover change in Neka river of Iran using topographic maps and multi-temporal remotely sensed data from 1975 to 2001. The Maximum likelihood supervised classification technique was used to extract information from satellite data, and post-classification change detection method was employed to detect land use/cover change. Post-classification change detection technique was used to produce an image through cross-tabulation. Changes among different land use/cover classes were assessed. The overall accuracy of land cover change maps, generated from Landsat data 1975 and 2001, ranged from 99.44% and 97.08% with Kappa statistics of 85% and 83%, respectively. The analysis indicated that the urban and agricultural land expansion of Neka river was increased resulted in the considerable reduction of forest area. The maps showed that between 1987 and 2001 the agricultural land and built-up areas increased approximately 59.86km2 (9.16%) and 7.35(1.13%), respectively. While forest decreased 67.91 km2 (10.29%). The study quantified the patterns of land use/cover change for the last 13 years for Neka river that forms valuable resources for urban planners and decision makers to devise sustainable land use and environmental planning.
The Use of Remote Sensing Indices for Land Cover Change Detection
Anuário do Instituto de Geociências - UFRJ, 2019
Remote sensing technology has been applied to monitor anthropogenic changes in the landscape that produce impacts on natural resources, such as environmental degradation, changes in the hydrological cycle and in ecosystems structure and functioning. As digital change detection may be a difficult task to perform, this study proposes a simple and logical technique to display land cover changes using Landsat imagery. Open source geoprocessing tools were used to acquire information for mapping changes on the land surface. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from satellite images of four dates between 1984 and 2016 were used in RGB composites. The method was used to map gains and losses of vegetation cover and liquid water content in a spatiotemporal scale. The results indicate that this change detection method can effectively reflect the variations occurred over the years. Although both indices have similar responses, NDWI may provide opposite information to NDVI in certain areas, such as in wetlands and riparian zones, presenting wetness losses even in places that exhibit gains in vegetation. This method has applicability to other regions for deriving historical changes.
ASSESSMENT OF ENVIRONMENTAL CHANGE AND LAND DEGRADATION USING TIME SERIES OF REMOTE SENSING IMAGES
Fresenius Environmental Bulletin, 2011
Remotely sensed images having different spectral, spatial, radiometric resolutions provide up-to-date and repetitive information about the Earth's surfaces. This valuable information is essential to determine and monitor environmental changes for decision makers dealing with environmental management. Uncontrolled and rapid urbanization has been one of the major problems faced in the development of the countries, and has caused land degradation resulting in serious and unexpected environmental problems. The aim of the study is to determine land use and land cover changes in a highly industrialized and urbanized region, Kocaeli province of Turkey, using multitemporal satellite images for the period of 1984-2009. Five dates of Landsat images from 1984, 1987, 1999, 2002 and 2009 were selected to conduct multitemporal change detection. Based on the supervised classification method of support vector machines algorithm, images were classified and then post classification comparison technique was used to analyze the changes occurred during the 25-year period. It was found that rapid urbanization occurred in the study area with an increase of 184% in urban lands, and forests in the region are threatened by the high-level of urbanization especially after the year of 2002.
Remote Sensing, 2019
The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007,...
Land Use and Land Cover Changes Detection using Multi-Temporal Satellite Data
Land use/cover change is one of the most sensitive factors that show the interactions between human activities and the ecological environment. This paper shows a GIS and remote sensing approach for modelling of Spatio-temporal pattern of land use and land cover change (LUCC) in a fastest growing towns / industrial region of Baddi town. The study area is particularly sensitive as it contains the natural resource which is subject to intensive anthropogenic pressure, and has experienced great changes in land use/cover. It's urgent to detect the land use/cover change pattern to provide more explicit information on the further development of the new industrial / urban area, which often requires to recover the history of land cover change and relates the spatiotemporal pattern of such change to other environmental and human factors, rather than merely relying on the change of areas or indices. Multi-temporal and multisource images i.e. LandSAT-TM for 1990 & ETM+ for 2003, IRS LISS-III for 2008 of about two and a half decades were acquired for change detection. A stochastic model was used to estimate and evaluate the change detection. The classification results were then utilized in the analysis of land use/cover change 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 change trajectories are also analysed to detect the categorical change of different classes.
Land Cover change detection by using Remote Sensing – A Case Study of Zlatibor (Serbia)
Change detection is a process of detecting differences with the objects or phenomena which are observed in the different time intervals. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1985 – 2013). The area observed in this study is the region of mountain Zlatibor (Serbia) with its surroundings. The methods represented in this study are vegetation indices differencing, Supervised classification and Object based classification. These methods gave different results in term of land cover area, and it is generally concluded that supervised classification gave the most accurate results with the images of medium spatial resolution. The results of this study can be used for urban and environmental planning. All information lead to conclusion that the surface under the forests is reduced for about 4% (or about 1000 ha) while the built up area has doubled (grown about 600 ha) during the examined period. The results also highlights the importance of change detection techniques in land cover for the areas that are developing rapidly, such as Zlatibor study area.
A Literature Review on Land Use Land Cover Changes Detection using Remote Sensing and GIS
International Journal for Research in Applied Science and Engineering Technology, 2021
The current paper addresses the phenomenon of land use and land cover (LULC), which has undergone continuous changes over the last few decades as a result of significant environmental variations caused by anthropogenic and natural factors. A detailed review of the studies conducted so far has revealed a declining trend in land use land coverage. It has also been discovered that land use has an effect on land cover and vice versa. Understanding the relationships and interactions between human and natural phenomena depends on the precision of change detection on the earth's surface. Remote sensing and Geographic Information Systems (GIS) have the potential to provide reliable data on changes in land use and land cover. We look at the most common methods for detecting changes in land use and land cover. Eleven methods for detecting changes are examined. The most commonly used tools, according to a review of the literature, are post-classification comparison and principle component analysis. The effects of atmospheric and sensor variations between two dates can be minimised using a post-classification comparison. Although image differencing and image ratioing are simple to use, they do not always produce accurate results. Hybrid change detection is a useful method that combines the advantages of many methods, but it is complicated and dependent on the features of the other techniques, such as supervised and unsupervised classifications. Change vector analysis is difficult to enforce, but it is helpful in determining the magnitude and direction of change. Artificial neural networks, chi-square, decision trees, and image fusion have all been used in change detection in recent years. Change detection research that incorporates remote sensing data and GIS has also risen.