Time Series Analysis Using Digital Classification of Data on Land Use and Land Cover in Dohuk Governorate

Abstract This study deals with the analysis and detection of changes in land cover patterns and land uses, especially forests in Amadiya district in Dohuk Governorate. It carred out in northern of Iraq by area is (2775.21) km2 and the district is located astronomically between longitudes (01/04 ° 43), (17/08 ° 44), it extends between two circles of latitude, which are (16/50 ° 36) and ('30.'21 ° 37) north, during the periods (1999-2006-2013-2019). Application of the Supervised Classification and the detection of change over time in a comparative manner and by relying on the satellite images of the Land sat ETM satellite were used. The Landsat OLI satellite with a distinctive capacity of 30 meters in the Arc map 10.6.1 program, and one of the indicators of environmental degradation in the land cover patterns, which is the NDVI index for all study periods, was used to reveal the role of natural and human factors that lead to changes in the land cover patterns in the study area. The classification revealed the existence of five types of common land cover, which included dense forests, open forests, urban areas, bare soil and water, which showed clear changes in these land coverings during the period from 1999 to 2019, which were represented by a decrease in forests, bare soil and water by a percentage of (54.76601%), (5.212329%), (2.149469%) respectively, while the Dense and urban areas by (16.35919%) and (21.51301%) in 2019, respectively. The classification accuracy of the Spatial indication was estimated based on the error matrix from there we found that the accuracy was (93.29%) this indicates that the classification accuracy is very good It is acceptable and can relied upon and recommended for classification.

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Accuracy Evaluation and Consistency Analysis of Four Global Land Cover Products in the Arctic Region

Remote Sensing ◽

10.3390/rs11121396 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1396 ◽

Cited By ~ 4

Author(s):

Li Liang ◽

Qingsheng Liu ◽

Gaohuan Liu ◽

He Li ◽

Chong Huang

Keyword(s):

Climate Change ◽

Land Cover ◽

Classification Accuracy ◽

Urban Areas ◽

Arctic Region ◽

The Arctic ◽

Global Land Cover ◽

Global Land ◽

The Arctic Region

Land cover is a fundamental component of crucial importance in the earth sciences. To date, many excellent international teams have created a variety of land cover products covering the entire globe. To provide a reference for researchers studying the Arctic, this paper evaluates four commonly used land cover products. First, we compare and analyze the four land cover products from the perspectives of land cover type, distribution and spatial heterogeneity. Second, we evaluate the accuracy of such products by using two sets of sample points collected from the Arctic region. Finally, we obtain the spatial consistency distribution of the products by means of superposition analysis. The results show the following: (a) among the four land cover products, Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy (63.5%) in the Arctic region, GlobeLand30 has an overall accuracy of 62.2% and the overall accuracy of the Global Land Cover by the National Mapping Organization (GLCNMO) is only 48.8%. When applied in the Arctic region, the overall accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) is only 29.5% due to significant variances. Therefore, MODIS and GLCNMO are not recommended in Arctic-related research as their use may lead to major errors. (b) An evaluation of the consistency of the four products indicates that the classification of the large-scale homogeneous regions in the Arctic yields satisfactory results, whereas the classification results in the forest–tundra ecotone are unsatisfactory. The results serve as a reference for future research. (c) Among the four products, GlobeLand30 is the best choice for analyzing finely divided and unevenly distributed surface features such as waters, urban areas and cropland. Climate Change Initiative Land Cover (CCI-LC) has the highest overall accuracy, and its classification accuracy is relatively higher for forests, shrubs, sparse vegetation, snow/ice and water. GlobeLand30 and CCI-LC do not vary much from each other in terms of overall accuracy. They differ the most in the classification accuracy of shrub-covered land; CCI-LC performed better than GlobeLand30 in the classification of shrub-covered land, whereas the latter obtained higher accuracy than that of the former in the classification of urban areas and cropland.

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Assessment of the X- and C-Band Polarimetric SAR Data for Plastic-Mulched Farmland Classification

Remote Sensing ◽

10.3390/rs11060660 ◽

2019 ◽

Vol 11 (6) ◽

pp. 660 ◽

Cited By ~ 2

Author(s):

Chang-An Liu ◽

Zhongxin Chen ◽

Di Wang ◽

Dandan Li

Keyword(s):

Land Cover ◽

Urban Areas ◽

Agricultural Land ◽

Test Site ◽

Decomposition Methods ◽

Bare Soil ◽

Classification Error ◽

Land Cover Types ◽

Land Covers

We present a classification of plastic-mulched farmland (PMF) and other land cover types using full polarimetric RADARSAT-2 data and dual polarimetric (HH, VV) TerraSAR-X data, acquired from a test site in Hebei, China, where the main land covers include PMF, bare soil, winter wheat, urban areas and water. The main objectives were to evaluate the outcome of using high-resolution TerraSAR-X data for classifying PMF and other land covers and to compare classification accuracies based on different synthetic aperture radar bands and polarization parameters. Initially, different polarimetric indices were calculated, while polarimetric decomposition methods were used to obtain the polarimetric decomposition components. Using these polarimetric components as input, the random forest supervised classification algorithm was applied in the classification experiments. Our results show that in this study full-polarimetric RADARSAT-2 data produced the most accurate overall classification (94.81%), indicating that full polarization is vital to distinguishing PMF from other land cover types. Dual polarimetric data had similar levels of classification error for PMF and bare soil, yielding mapping accuracies of 53.28% and 59.48% (TerraSAR-X), and 59.56% and 57.1% (RADARSAT-2), respectively. We found that Shannon entropy made the greatest contribution to accuracy in all three experiments, suggesting that it has great potential to improve agricultural land use classifications based on remote sensing.

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Ecological Connectivity in Agricultural Green Infrastructure: Suggested Criteria for Fine Scale Assessment and Planning

Land ◽

10.3390/land10080807 ◽

2021 ◽

Vol 10 (8) ◽

pp. 807

Author(s):

Simone Valeri ◽

Laura Zavattero ◽

Giulia Capotorti

Keyword(s):

Land Cover ◽

Green Infrastructure ◽

Rural Areas ◽

Urban Areas ◽

Landscape Heterogeneity ◽

Agricultural Landscapes ◽

Spatial Pattern Analysis ◽

Fine Scale ◽

Unique Type

In promoting biodiversity conservation and ecosystem service capacity, landscape connectivity is considered a critical feature to counteract the negative effects of fragmentation. Under a Green Infrastructure (GI) perspective, this is especially true in rural and peri-urban areas where a high degree of connectivity may be associated with the enhancement of agriculture multifunctionality and sustainability. With respect to GI planning and connectivity assessment, the role of dispersal traits of tree species is gaining increasing attention. However, little evidence is available on how to select plant species to be primarily favored, as well as on the role of landscape heterogeneity and habitat quality in driving the dispersal success. The present work is aimed at suggesting a methodological approach for addressing these knowledge gaps, at fine scales and for peri-urban agricultural landscapes, by means of a case study in the Metropolitan City of Rome. The study area was stratified into Environmental Units, each supporting a unique type of Potential Natural Vegetation (PNV), and a multi-step procedure was designed for setting priorities aimed at enhancing connectivity. First, GI components were defined based on the selection of the target species to be supported, on a fine scale land cover mapping and on the assessment of land cover type naturalness. Second, the study area was characterized by a Morphological Spatial Pattern Analysis (MSPA) and connectivity was assessed by Number of Components (NC) and functional connectivity metrics. Third, conservation and restoration measures have been prioritized and statistically validated. Notwithstanding the recognized limits, the approach proved to be functional in the considered context and at the adopted level of detail. Therefore, it could give useful methodological hints for the requalification of transitional urban–rural areas and for the achievement of related sustainable development goals in metropolitan regions.

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A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8

Land ◽

10.3390/land10030231 ◽

2021 ◽

Vol 10 (3) ◽

pp. 231

Author(s):

Can Trong Nguyen ◽

Amnat Chidthaisong ◽

Phan Kieu Diem ◽

Lian-Zhi Huo

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Urban Areas ◽

Near Infrared ◽

Urban Landscape ◽

Bare Soil ◽

Critical Element ◽

Landsat 8 ◽

Climate Conditions ◽

Fallow Period

Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

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Classification of Urban Areas: Inferring Land Use from the Interpretation of Land Cover

Remote Sensing of Urban and Suburban Areas - Remote Sensing and Digital Image Processing ◽

10.1007/978-1-4020-4385-7_8 ◽

2010 ◽

pp. 141-164 ◽

Cited By ~ 8

Author(s):

Victor Mesev

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas

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Classification of Urban Area Using Multispectral Indices for Urban Planning

Remote Sensing ◽

10.3390/rs12152503 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2503 ◽

Cited By ~ 1

Author(s):

Philip Lynch ◽

Leonhard Blesius ◽

Ellen Hines

Keyword(s):

Land Cover ◽

Urban Area ◽

Urban Areas ◽

Unsupervised Classification ◽

Condition Index ◽

The United States ◽

United States Geological Survey ◽

Classification Schemes ◽

Florida Department

An accelerating trend of global urbanization accompanying population growth makes frequently updated land use and land cover (LULC) maps critical. LULC maps have been widely created through the classification of remotely sensed imagery. Maps of urban areas have been both dichotomous (urban or non-urban) and entailing of discrete urban types. This study incorporated multispectral built-up indices, designed to enhance satellite imagery, for introducing new urban classification schemes. The indices examined are the new built-up index (NBI), the built-up area extraction index (BAEI), and the normalized difference concrete condition index (NDCCI). Landsat Level-2 data covering the city of Miami, FL, USA was leveraged with geographic data from the Florida Geospatial Data Library and Florida Department of Environmental Protection to develop and validate new methods of supervised and unsupervised classification of urban area. NBI was used to extract discrete urban features through object-oriented image analysis. BAEI was found to possess properties for visualizing and tracking urban development as a low-high gradient. NDCCI was composited with NBI and BAEI as the basis for a robust urban intensity classification scheme superior to that of the United States Geological Survey National Land Cover Database 2016. BAEI, implemented as a shadow index, was incorporated in a novel infill geosimulation of high-rise construction. The findings suggest that the proposed classification schemes are advantageous to the process of creating more detailed cartography in response to the increasing global demand.

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Isolation Forests to Evaluate Class Separability and the Representativeness of Training and Validation Areas in Land Cover Classification

Remote Sensing ◽

10.3390/rs11243000 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3000 ◽

Cited By ~ 2

Author(s):

Francisco Alonso-Sarria ◽

Carmen Valdivieso-Ros ◽

Francisco Gomariz-Castillo

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Land Cover ◽

Classification Accuracy ◽

Land Cover Classification ◽

Landsat 8 ◽

Commission Errors ◽

Class Separability ◽

Isolation Forest

Supervised land cover classification from remote sensing imagery is based on gathering a set of training areas to characterise each of the classes and to train a predictive model that is then used to predict land cover in the rest of the image. This procedure relies mainly on the assumptions of statistical separability of the classes and the representativeness of the training areas. This paper uses isolation forests, a type of random tree ensembles, to analyse both assumptions and to easily correct lack of representativeness by digitising new training areas where needed to improve the classification of a Landsat-8 set of images with Random Forest. The results show that the improved set of training areas after the isolation forest analysis is more representative of the whole image and increases classification accuracy. Besides, the distribution of isolation values can be useful to estimate class separability. A class separability parameter that summarises such distributions is proposed. This parameter is more correlated to omission and commission errors than other separability measures such as the Jeffries–Matusita distance.

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