scholarly journals Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

2019 ◽  
Vol 11 (9) ◽  
pp. 1104 ◽  
Author(s):  
Cláudia M. Viana ◽  
Inês Girão ◽  
Jorge Rocha
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Use Land Cover ◽  
Rural Region ◽  
Lulc Changes ◽  
Open Source Data ◽  
Training Samples ◽  
Source Data

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, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.

scholarly journals Mapping Land-Use/Land-Cover Change in a Critical Biodiversity Area of South Africa

2021 ◽  
Vol 18 (19) ◽  
pp. 10164
Author(s):  
Khangwelo Desmond Musetsho ◽  
Munyaradzi Chitakira ◽  
Willem Nel
Keyword(s):  
South Africa ◽  
Land Use ◽  
Land Cover ◽  
Limpopo Province ◽  
Water Bodies ◽  
Subsistence Agriculture ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Gravel Mining

Land-use/land-cover (LULC) changes have implications for the long-term outlook of environmental processes, especially in the face of factors such as climate change. These changes can have serious consequences for humans. In this study, remote sensing and geographic information system methods were used to investigate LULC changes in a critical biodiversity area (CBA) in the northern sections of Limpopo Province in South Africa from 1990 to 2018 using data obtained from the South African National Land Cover project. In 1990, the dominant land cover comprised thickets and dense bush, followed by woodland and built-up areas, covering proportions of 40, 24 and 18% of the total land-cover area, respectively. Bare and forest areas were the least dominant classes during this time. In 2018, the dominant land cover was woodland, followed by built-up areas, comprising 71 and 20% of the total area, respectively. Subsistence agriculture is a land-cover class with a relatively higher area compared to water bodies, wetlands and other classes. Between 1990 and 2018, significant changes in land-cover were noted for thickets and dense bush, woodland, water bodies, subsistence agriculture and built-up areas. Woodland increased by over 1000 hectares (ha) per year, while thickets decreased by over 900 ha per year. Interviews were conducted with local residents to determine what they thought were the drivers behind the observed changes. According to these interviews, the drivers included deforestation, agricultural activities in wetlands, sand and gravel mining, among others. The study’s outcomes are critical for future land-use planning exercises and the long-term conservation of this CBA, an area rich in biodiversity and a strategic water source for the communities.

scholarly journals Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method

2020 ◽  
Vol 12 (10) ◽  
pp. 4332 ◽  
Author(s):  
Cláudia M. Viana ◽  
Jorge Rocha
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Arable Land ◽  
Land Use Land Cover ◽  
Landscape Changes ◽  
Transition Matrices ◽  
Future Scenario ◽  
Discrete Time Markov Chain ◽  
Rural Region ◽  
Lulc Changes

The present study used the official Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal, which is experiencing marked landscape changes. Here, we computed the conventional transition matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018, providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing on the dominant signals of change. We also investigated how the LULC is going to move in the future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC) model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly different between periods, and explicitly revealed that arable land, pastures, and forest were the most dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures (2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results showed that about 25% of the territory is predicted to experience major LULC changes from arable land (−3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.

scholarly journals Effects of Climate and Land Use/Land Cover Changes on Water Yield Services in the Dongjiang Lake Basin

2021 ◽  
Vol 10 (7) ◽  
pp. 466
Author(s):  
Wenbo Mo ◽  
Yunlin Zhao ◽  
Nan Yang ◽  
Zhenggang Xu ◽  
Weiping Zhao ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Water Resource Management ◽  
Water Source ◽  
Water Yield ◽  
Lake Basin ◽  
Lake Area ◽  
Land Use Land Cover ◽  
Lulc Changes ◽  
Regional Water

Spatial and quantitative assessments of water yield services in watershed ecosystems are necessary for water resource management and improved water ecological protection. In this study, we used the InVEST model to estimate regional water yield in the Dongjiang Lake Basin in China. Moreover, we designed six scenarios to explore the impacts of climate and land use/land cover (LULC) changes on regional water yield and quantitatively determined the dominant mechanisms of water yield services. The results are expected to provide an important theoretical reference for future spatial planning and improvements of ecological service functions at the water source site. We found that (1) under the time series analysis, the water yield changes of the Dongjiang Lake Basin showed an initial decrease followed by an increase. Spatially, water yield also decreased from the lake area to the surrounding region. (2) Climate change exerted a more significant impact on water yield changes, contributing more than 98.26% to the water yield variability in the basin. In contrast, LULC had a much smaller influence, contributing only 1.74 %. (3) The spatial distribution pattern of water yield services in the watershed was more vulnerable to LULC changes. In particular, the expansion of built-up land is expected to increase the depth of regional water yield and alter its distribution, but it also increases the risk of waterlogging. Therefore, future development in the basin must consider the protection of ecological spaces and maintain the stability of the regional water yield function.

scholarly journals Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Hamza Islam ◽  
Habibuulah Abbasi ◽  
Ahmed Karam ◽  
Ali Hassan Chughtai ◽  
Mansoor Ahmed Jiskani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Kappa Statistics ◽  
Natural Ecosystem ◽  
Land Use Land Cover ◽  
Dry Land ◽  
Lulc Changes ◽  
Manchar Lake

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

scholarly journals Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

2013 ◽  
Vol 8 (1) ◽  
pp. 084596 ◽  
Author(s):  
Zhongchang Sun ◽  
Xinwu Li ◽  
Wenxue Fu ◽  
Yingkui Li ◽  
Dongsheng Tang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Surface Runoff ◽  
Urban Areas ◽  
Land Use Land Cover ◽  
Long Term Effects ◽  
Effects Of Land Use ◽  
Beijing China
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