LULC Change Detection Studies and Its Possible Linkages with Increased Visitations in the Sagar Island, W.B.

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.

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Change Detection Studies of Sagar Island, India, using Indian Remote Sensing Satellite 1C Linear Imaging Self-Scan Sensor III Data

Journal of Coastal Research ◽

10.2112/05-0599.1 ◽

2007 ◽

Vol 236 ◽

pp. 1498-1502 ◽

Cited By ~ 24

Author(s):

P. K. Dinesh Kumar ◽

Girish Gopinath ◽

C. M. Laluraj ◽

P. Seralathan ◽

D. Mitra

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Indian Remote Sensing ◽

Indian Remote Sensing Satellite ◽

Sagar Island ◽

Remote Sensing Satellite

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

Sustainability ◽

10.3390/su13084092 ◽

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.

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Anthropogenic Land Use/Cover Change Detection And Its Impacts On Hydrological Responses of Genale Catchment, Ethiopia

10.21203/rs.3.rs-938182/v1 ◽

2021 ◽

Author(s):

Tufa Feyissa Negewo ◽

Arup Kumar Sarma

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Water Body ◽

Land Development ◽

Geographical Information ◽

Hydrological Response ◽

Runoff Water ◽

Lulc Change ◽

Hydrological Responses

Abstract Change in land use land-cover (LULC) is a paramount dynamic present-day challenging landscape process capable of altering the hydrological responses in the catchment. As the land use planners require updated and high-resolution land resources information, understanding land cover change-induced status due to anthropogenic activities is significant. In this study, multitemporal cloud-free satellite imageries for periods (1990, 2002, and 2013) were used to quantify the spatiotemporal dynamics of land-use change detection and examine the effect on hydrological response using Geographical Information System (GIS) and Soil and Water Assessment Tool (SWAT) model in the Genale watershed, Ethiopia. The model performance was evaluated through sensitivity, uncertainty analysis, calibration, and validation process. The analysis of LULC change patterns for the area under study over 24 years showed that most parts of the green forest, barren land, and range shrubs were changed into agriculture, built up, wetlands, and water body with an increase of agriculture by 60%, built up 68%, pasture 37%, range shrubs 9%, and water body 57% over (1990 to 2013), which increased surface runoff, water yield, and sediment yield in the catchment. Significant changes in hydrological elements were observed at the sub-basins scale, mainly associated with the uneven spatial distribution of LULC changes compared to the whole watershed. The impacts of individual LULC change on hydrological response show a good correlation matrix. The regional government needs to modify land development policies and sustainable plans for examining LULC change detection using satellite imagery to avoid illegal land expansion activities.

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A Time Series Analysis of Satellite Imageries for Land Use & Land Cover (LULC) Change Detection of Gujranwala City, Pakistan from 1999–2019

Indian Journal of Science and Technology ◽

10.17485/ijst/2019/v12i46/148606 ◽

2019 ◽

Vol 12 (46) ◽

pp. 01-09

Author(s):

Faiza Mazhar ◽

Faiza Fadia

Keyword(s):

Land Use ◽

Time Series ◽

Land Cover ◽

Time Series Analysis ◽

Change Detection ◽

Land Use Land Cover ◽

Lulc Change ◽

Series Analysis

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Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis

Remote Sensing ◽

10.3390/rs12071057 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1057 ◽

Cited By ~ 3

Author(s):

Frank Thonfeld ◽

Stefanie Steinbach ◽

Javier Muro ◽

Fridah Kirimi

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Food Production ◽

Land Use Land Cover ◽

Spectral Change ◽

Lulc Change ◽

Spectral Changes ◽

Change Vector Analysis ◽

Change Vector

Information about land use/land cover (LULC) and their changes is useful for different stakeholders to assess future pathways of sustainable land use for food production as well as for nature conservation. In this study, we assess LULC changes in the Kilombero catchment in Tanzania, an important area of recent development in East Africa. LULC change is assessed in two ways: first, post-classification comparison (PCC) which allows us to directly assess changes from one LULC class to another, and second, spectral change detection. We perform LULC classification by applying random forests (RF) on sets of multitemporal metrics that account for seasonal within-class dynamics. For the spectral change detection, we make use of the robust change vector analysis (RCVA) and determine those changes that do not necessarily lead to another class. The combination of the two approaches enables us to distinguish areas that show (a) only PCC changes, (b) only spectral changes that do not affect the classification of a pixel, (c) both types of change, or (d) no changes at all. Our results reveal that only one-quarter of the catchment has not experienced any change. One-third shows both, spectral changes and LULC conversion. Changes detected with both methods predominantly occur in two major regions, one in the West of the catchment, one in the Kilombero floodplain. Both regions are important areas of food production and economic development in Tanzania. The Kilombero floodplain is a Ramsar protected area, half of which was converted to agricultural land in the past decades. Therefore, LULC monitoring is required to support sustainable land management. Relatively poor classification performances revealed several challenges during the classification process. The combined approach of PCC and RCVA allows us to detect spatial patterns of LULC change at distinct dimensions and intensities. With the assessment of additional classifier output, namely class-specific per-pixel classification probabilities and derived parameters, we account for classification uncertainty across space. We overlay the LULC change results and the spatial assessment of classification reliability to provide a thorough picture of the LULC changes taking place in the Kilombero catchment.

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