scholarly journals Object-Based Land Use and Land Cover Change Detection in Multi Temporal Remote-Sensing Images

2021 ◽  
Author(s):  
Dan Li ◽  
Runjie Jin ◽  
Jiali Gu ◽  
Runqiu Huang ◽  
Jiaping Wu
Keyword(s):  
Decision Making ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Eastern China ◽  
Management Decision ◽  
Lulc Changes ◽  
Object Based ◽  
Management Decision Making ◽  
Multi Temporal

The changing of land use and land cover (LULC) are both affected by climate and human activity and affect climate, biological diversity, and human well-being. Accurate and timely information about the LULC pattern and change is crucial for land management decision-making, ecosystem monitoring, and urban planning, especially in developing economies undergoing industrialization, urbanization, and globalization. Biodiversity degradation and urban expansion in eastern China are research hot-spots. However, the influence of LULC changes on the region remains largely unexplored. Here, an object-based and multi-temporal image analysis approach was developed to detect how LULC changes during 1985-2015 in the Tiaoxi watershed (Zhejiang province, eastern China) using Landsat TM and OLI data. The main objective of this study is to improve the accuracy of unsupervised change detection from object-based and multi-temporal images. To this end, a total of seven LULC maps are generated with multi-temporal images. A random stratified sample design was used for assessing change detection accuracy. The proposed method achieved an overall accuracy of 91.86%, 92.14%, 92.00%, and 93.86% for 2000, 2005, 2010, and 2015, respectively. Nevertheless, the proposed method, in conjunction with object-oriented and multi-temporal satellite images, offers a robust and flexible approach to LULC changes mapping that helps with emergency response and government management. Urbanization and agriculture efficiency are the main reasons for LULC changes in the region. We anticipate that this freely available data will improve the modeling for surface forcing, provide evidence of changes in LULC, and inform water-management decision-making.

scholarly journals Land-Use and Land-Cover (LULC) Change Detection in Wami River Basin, Tanzania

Land ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 136 ◽  
Author(s):  
Sekela Twisa ◽  
Manfred F. Buchroithner
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Population Growth ◽  
Change Detection ◽  
River Basin ◽  
Land Use Planning ◽  
Management Practice ◽  
Cultivated Land ◽  
Lulc Change ◽  
Lulc Changes

Anthropogenic activities have substantially changed natural landscapes, especially in regions which are extremely affected by population growth and climate change such as East African countries. Understanding the patterns of land-use and land-cover (LULC) change is important for efficient environmental management, including effective water management practice. Using remote sensing techniques and geographic information systems (GIS), this study focused on changes in LULC patterns of the upstream and downstream Wami River Basin over 16 years. Multitemporal satellite imagery of the Landsat series was used to map LULC changes and was divided into three stages (2000–2006, 2006–2011, and 2011–2016). The results for the change-detection analysis and the change matrix table from 2000 to 2016 show the extent of LULC changes occurring in different LULC classes, while most of the grassland, bushland, and woodland were intensively changed to cultivated land both upstream and downstream. These changes indicate that the increase of cultivated land was the result of population growth, especially downstream, while the primary socioeconomic activity remains agriculture both upstream and downstream. In general, net gain and net loss were observed downstream, which indicate that it was more affected compared to upstream. Hence, proper management of the basin, including land use planning, is required to avoid resources-use conflict between upstream and downstream users.

scholarly journals Land-Use and Land-Cover (LULC) Change Detection and the Implications for Coastal Water Resource Management in the Wami–Ruvu Basin, Tanzania

2021 ◽  
Vol 13 (8) ◽  
pp. 4092
Author(s):  
Jamila Ngondo ◽  
Joseph Mango ◽  
Ruiqing Liu ◽  
Joel Nobert ◽  
Alfonse Dubi ◽  
...  
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Land Cover ◽  
Change Detection ◽  
Water Resource Management ◽  
Water Discharge ◽  
Bare Soil ◽  
Training Data ◽  
Lulc Change ◽  
Lulc Changes

Evaluation of river basins requires land-use and land-cover (LULC) change detection to determine hydrological and ecological conditions for sustainable use of their resources. This study assessed LULC changes over 28 years (1990–2018) in the Wami–Ruvu Basin, located in Tanzania, Africa. Six pairs of images acquired using Landsat 5 TM and 8 OLI sensors in 1990 and 2018, respectively, were mosaicked into a single composite image of the basin. A supervised classification using the Neural Network classifier and training data was used to create LULC maps for 1990 and 2018, and targeted the following eight classes of agriculture, forest, grassland, bushland, built-up, bare soil, water, and wetland. The results show that over the past three decades, water and wetland areas have decreased by 0.3%, forest areas by 15.4%, and grassland by 6.7%, while agricultural, bushland, bare soil, and the built-up areas have increased by 11.6%, 8.2%, 1.6%, and 0.8%, respectively. LULC transformations were assessed with water discharge, precipitation, and temperature, and the population from 1990 to 2018. The results revealed decreases in precipitation, water discharge by 4130 m3, temperature rise by 1 °C, and an increase in population from 5.4 to 10 million. For proper management of water-resources, we propose three strategies for water-use efficiency-techniques, a review legal frameworks, and time-based LULC monitoring. This study provides a reference for water resources sustainability for other countries with basins threatened by LULC changes.

scholarly journals New Change Detection Techniques to monitor land cover dynamics in mine environment

2014 ◽  
Vol XL-8 ◽  
pp. 875-879 ◽  
Author(s):  
S. Pathak
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Environmental Changes ◽  
Time Interval ◽  
Land Use Patterns ◽  
Detection Techniques ◽  
Use Patterns ◽  
Object Based ◽  
Ideal Situation

Land use and land cover are dynamic and is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to simulate environmental changes. Land use/cover (LU/LC) change detection is very essential for better understanding of landuse dynamic during a known period of time for sustainable management. Mining is one of the most dynamic processes with direct as well as indirect impact on the environment. Hence, mine area provides ideal situation for evaluating the chronological changes in land-use patterns. Digital change detection of satellite data at different time interval helps in analyzing the changes in the spatial extent of mine along with the associated activities. In present study, various algorithms Iteratively Re-weighted Multivariate Alteration Detection (MAD) on raw data where class wise comparison becomes a difficult proposition and object based segmentation and change detection as post classification comparison were assessed.

scholarly journals A GEOBIA Approach for Multitemporal Land-Cover and Land-Use Change Analysis in a Tropical Watershed in the Southeastern Amazon

2018 ◽  
Vol 10 (11) ◽  
pp. 1683 ◽  
Author(s):  
Pedro Souza-Filho ◽  
Wilson Nascimento ◽  
Diogo Santos ◽  
Eliseu Weber ◽  
Renato Silva ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Landsat Images ◽  
Change Analysis ◽  
Human Settlements ◽  
Object Based ◽  
Detection Approach ◽  
Tropical Watershed

The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km2) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km2) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.

scholarly journals Land use land cover change detection in the mining areas of V. D. Yalevsky coal mine-Russia

2020 ◽  
Vol 192 ◽  
pp. 04021
Author(s):  
Аl-shateri Hoshmand Ahmed Azeez ◽  
Shuchrat Mukhitdinov
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Coal Mine ◽  
Mining Area ◽  
Land Use Land Cover ◽  
Coal Field ◽  
Lulc Changes ◽  
Field Area ◽  
Classified Image

The dynamics of land use/land cover (LULC) changes, the effect of coal mining on the LULC changes, and the regional environmental impact are discussed in this study. The different land use classes mainly Forest, Water Bodies, Road, Mining Area, Agriculture and Grass in the study area of V. D. Yalevsky coal field area in Prokorvisk city in Kamerovo region of Russia are identified. On the other hand the impact of V. D. Yalevsky coal mine activities on LULC change on the environment and teritory are discussed. The LULC changes in the V. D. Yalevsky coal field area were analyzed for a period of 27 years e.g., from the year 1992 to 2019. The changes were detected on a 13-years time interval using Landsat-4 TM, Landsat-8 OLI. Furthermore supervised classification techniques using maximum likelihood method through ENVI (Environment for Visualizing Images) 5.1software was utilized. In addition post classification change detection method through ENVI was used to investigate the changes. The study reveals decrecment in LULC cotogories of forest to 25.35km², water bodies to -0.94km², agriculture to -98.48km², road to -10.80km². However increment in the rate of mining area to 100.72km² and grass cover 34.86 km² during the study period. Meanwhile 90.18% overall accuracy and (0.87) kappa coefitient for 1992 classified image, 93.41% overall accuracy and (0.91) Kappa koefitient for 2006 classified image and 88.69% overall accuracy and (0.85) kappa coefitient for 2019 classified image were obtained.

scholarly journals Land use/Land Cover and Change Detection in Chikodi Taluk, Belagavi District, Karnataka using Object based Image Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 1222-1227
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Image Classification ◽  
Water Body ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Google Earth ◽  
Land Use Land Cover ◽  
Object Based

The study examines land use land cover and change detection in Chikodi taluk, Belagavi district, Karnataka. Land use land cover plays an important role in the study of global change. Due to fast urbanization there is variation in natural resources such as water body, agriculture, wasteland land etc. These environment problems are related to land use land cover changes. And for the sustainable development it is mandatory to know the interaction of human activities with the environment and to monitor the change detection. In present study for image classification Object Based Image Analysis (OBIA) method was adapted using multi-resolution segmentation for the year 1992, 1999 and 2019 imagery and classified into four different classes such as agriculture, built-up, wasteland and water-body. Random points (200) were generated in ArcGIS environment and converted points into KML layer in order to open in Google Earth. For the accuracy assessment confusion matrix was generated and result shows that overall accuracy of land use land cover for 2019 is 83% and Kappa coefficient is 0.74 which is acceptable. These outcomes of the result can provide critical input to decision making environmental management and planning the future.

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