Land Use / Cover Change Detection In Arid And Semi - Arid Areas Of Northeastern Jordan Using Landsat Images

2017 ◽  
pp. 265
Author(s):  
حسام هشام البلبيسي
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Arid Areas ◽  
Landsat Images ◽  
Semi Arid

scholarly journals Change Detection Analysis Using Surveying and Geoinformatics Techniques

2018 ◽  
Vol 2 (1) ◽  
pp. 28-38
Author(s):  
I. C. Onuigbo ◽  
J. Y. Jwat
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Research Work ◽  
Temporal Data ◽  
Landsat Images ◽  
Detection Analysis ◽  
Ground Truthing ◽  
Landsat Satellite ◽  
Geoinformatics Techniques

The study was on change detection using Surveying and Geoinformatics techniques. For effective research study, Landsat satellite images and Quickbird imagery of Minna were acquired for three periods, 2000, 2005 and 2012. The research work demonstrated the possibility of using Surveying and Geoinformatics in capturing spatial-temporal data. The result of the research work shows a rapid growth in built-up land between 2000 and 2005, while the periods between 2005 and 2012 witnessed a reduction in this class. It was also observed that change by 2020 may likely follow the trend in 2005 – 2012 all things being equal. Built up area may increase to 11026.456 hectares, which represent 11% change. The study has shown clearly the extent to which MSS imagery and Landsat images together with extensive ground- truthing can provide information necessary for land use and land cover mapping. Attempt was made to capture as accurate as possible four land use and land cover classes as they change through time.

scholarly journals Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

2018 ◽  
Vol 10 (8) ◽  
pp. 1248 ◽  
Author(s):  
Hua Sun ◽  
Qing Wang ◽  
Guangxing Wang ◽  
Hui Lin ◽  
Peng Luo ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Good Alternative ◽  
Landsat 8 ◽  
Arid Areas ◽  
Landsat Images ◽  
Infrared Band ◽  
K Value ◽  
Mean Square Errors ◽  
Semi Arid

Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

scholarly journals When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

2017 ◽  
Author(s):  
Kaisheng Luo ◽  
Fu-lu Tao ◽  
Juana P. Moiwo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Change Detection ◽  
Coniferous Forest ◽  
Detection Methods ◽  
Landsat Images ◽  
Two Stage ◽  
Smoothing Effects ◽  
Stage Classification ◽  
Elevation Model

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

scholarly journals DETECTING LAND USE/COVER DYNAMICS AND LAND SUITABILITY MAPPING FOR IRBID GOVERNORATE USING AN INTEGRATED APPROACH

2021 ◽  
Vol 29 (3) ◽  
pp. 263-272
Author(s):  
Alsharifa Hind Mohammad ◽  
Taleb Odeh ◽  
Maha Halalsheh ◽  
Khaldoun Shatanawi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Change Detection ◽  
Integrated Approach ◽  
Remote Sensing And Gis ◽  
Accelerated Expansion ◽  
Agricultural Activities ◽  
Land Uses ◽  
Landsat Images ◽  
Suitability Map

This research proposes to design an approach recognizing land use/cover change for Irbid governorate from 1985 to 2015 in 10 years period bases, with an agriculture suitability map using remote sensing and GIS. In this paper, ENVI6 was used to analyse Landsat images, which helps to understand the land uses’ classes. LULC Changes results showed an increase in urban land, from 2% in 1985 reached to 11% in 2015; soil and agricultural classes had declined, in 1985 they were 74% of the total area, and reduced to 67% in 2015. Irbid Governorate’s change detection results revealed that the decline of agriculture and rock land areas is due to the accelerated expansion of urbanization, which negatively affects agricultural lands. Modelling the area showed high suitability for agricultural activities, which should be considered for the upcoming plans.

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

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