Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China

Chen, Liping; Sun, Yong; Saeed, Sajjad

doi:10.1371/journal.pone.0200493

Cited by 402 publications

(236 citation statements)

References 54 publications

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“…According to the classification results, the shrimp farm area increased notably in the first 13 years and then decreased rapidly in 2018: the area was 2246. 67 Table 4 illustrates the differences in net land use of each class in 2000-2007, 2007-2013, 2013-2018, and 2000-2018. Dynamic land-use changes were observed in Rampal over the study period.…”

Section: Image Classification and Accuracy Assessment Resultsmentioning

confidence: 99%

“…However, to understand the changing pattern of shrimp yield from a quantitative perspective, we used k-means classification, aiming to divide the yield observation data into cluster groups. Repeated segmentation and testing of previews of each land cover/use class helped to improve the accuracy [67]. The same band set-shortwave infrared-1 (SWIR-1), near infrared (NIR), and red (bands 6, 5, 4, and bands 5, 4, 3 for Landsat-8 Operational Land Imager (OLI) and Landsat-5 Thematic Mapper (TM), respectively)-were used for maximum likelihood classification [68,69], in order to minimize the bias caused by using different band combinations.…”

Section: Temporal Differences In Shrimp Yield From 2002 To 2017 Usingmentioning

confidence: 99%

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Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Karim

Zhang

2019

Sustainability

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The shrimp-farming area and shrimp yield are continuously changing in the southwestern coastal districts of Bangladesh. The three southwestern coastal districts, Bagerhat, Satkhira, and Khulna, along with Rampal, a subdistrict of Bagerhat, contribute 75% of the total shrimp yield of Bangladesh. However, the shrimp yield and farming area have declined in Bagerhat district, and the cause of this decline is uncertain. In this research, the differences in the shrimp yield were quantified using a shrimp yield dataset (SYD) and k-means classification. A supervised image classification approach was applied to quantify the spatiotemporal changes and identify the influencing factors behind the declining shrimp-farming area and yield in Rampal, Bagerhat district, using Landsat satellite archives. K-means classification reveals that, between 2015 and 2017, the shrimp yield in Bagerhat district declined significantly compared to Satkhira and Khulna. The satellite-based monitoring results affirm that the shrimp-farming area of Rampal also decreased rapidly, from 21.82% in 2013 to 6.52% in 2018. This research estimates that approximately 70% of the shrimp-farming area was lost in Rampal since December 2013. Hence, the findings of this research might motivate the responsible bodies to declare the shrimp-farming coastal area as a "shrimp zone" and implement an active policy to protect the vulnerable shrimp-farming industry and shrimp farmers, considering it is the second-largest export earning source in Bangladesh after ready-made garments.

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Section: Image Classification and Accuracy Assessment Resultsmentioning

confidence: 99%

Section: Temporal Differences In Shrimp Yield From 2002 To 2017 Usingmentioning

confidence: 99%

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Karim

Zhang

2019

Sustainability

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“…Furthermore, these LULC changes occurring rapidly within the Western Ghats pose severe implications for the region's biodiversity. In this scenario, both changes in landscape and biodiversity distribution within the region have a strong spatial correlation (Menon and Bawa, 1997;Liping et al, 2018;Ramachandra et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Long-Term Spatio-Temporal Trends in Land Use/Land Cover in Devikulam Taluk, Kerala Using Geospatial Techniques

et al. 2019

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This study was undertaken to evaluate the spatial as well as the temporal changes in land use/ land cover in Devikulam Taluk, Idukki District, Kerala, and to assess the effects of increasing anthropogenic pressure on the fragile ecosystem of this area. For analysis, land use/ land cover maps of four different years, i.e., 1988, 1999, 2008 and 2017, were generated using LANDSAT TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus) and OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) satellite imagery. The results of the study suggested that there has been a drastic increase in the built-up area and a continuous decline in the forest area in Devikulam from 35.31 km 2 built-up in 1988 to 73.92 km 2 in 2017, and 1374.52 km 2 forest in 1988 to 1247.24 km 2 in 2017, respectively. Over this period of approximately 40 years, around 47.85 km 2 area of the forest got converted to built-up. This could be due to the increasing anthropogenic pressure in terms of migration or booming tourism contributing to the increased demand for infrastructures. Therefore, appropriate land use planning is a fundamental step towards the sustainable development of this biogeographically rich and unique area of Devikulam Taluk.

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“…The SDG framework provides a comprehensive agenda through which to mainstream policies and derive targeted actions for addressing core sustainability challenges. However, the ability to target policies and actions to address conservation issues, while pursuing economic development and prosperity, leaving no one behind, is hampered by lacking scientific evidence and data to direct and support informed decision making.In order to derive targeted policies and actions to support effective land use planning, management and ecological restoration conforming to the requirements of the SDG's, it is imperative to understand the underlying processes of change [7]. Up-to-date information on current land cover and land use provides critical information that can be used to underpin decision-making processes, while modelled predictions about plausible future land use/land cover (LULC) scenarios provide indications of potential trajectories and thus a platform for identifying interventions.…”

mentioning

confidence: 99%

“…In order to derive targeted policies and actions to support effective land use planning, management and ecological restoration conforming to the requirements of the SDG's, it is imperative to understand the underlying processes of change [7]. Up-to-date information on current land cover and land use provides critical information that can be used to underpin decision-making processes, while modelled predictions about plausible future land use/land cover (LULC) scenarios provide indications of potential trajectories and thus a platform for identifying interventions.…”

mentioning

confidence: 99%

Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo

Christensen

Arsanjani

2020

Sustainability

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The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG's) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased urbanisation, deforestation, and rapid economic development has decidedly modified the surface of the earth, resulting in dramatic land cover changes, which continue to cause significant degradation of environmental attributes. In order to reshape policies and management frameworks conforming to the objectives of the SDG's, it is paramount to understand the driving mechanisms of land use changes and determine future patterns of change. This study aims to assess and quantify future land cover changes in Virunga National Park in the Democratic Republic of the Congo by simulating a future landscape for the SDG target year of 2030 in order to provide evidence to support data-driven decision-making processes conforming to the requirements of the SDG's. The study follows six sequential steps: (a) creation of three land cover maps from 2010, 2015 and 2019 derived from satellite images; (b) land change analysis by cross-tabulation of land cover maps; (c) submodel creation and identification of explanatory variables and dataset creation for each variable; (d) calculation of transition potentials of major transitions within the case study area using machine learning algorithms; (e) change quantification and prediction using Markov chain analysis; and (f) prediction of a 2030 land cover. The model was successfully able to simulate future land cover and land use changes and the dynamics conclude that agricultural expansion and urban development is expected to significantly reduce Virunga's forest and open land areas in the next 11 years. Accessibility in terms of landscape topography and proximity to existing human activities are concluded to be primary drivers of these changes. Drawing on these conclusions, the discussion provides recommendations and reflections on how the predicted future land cover changes can be used to support and underpin policy frameworks towards achieving the SDG's and the 2030 Agenda for Sustainable Development.Sustainability 2020, 12, 1570 2 of 28 protect the critically endangered mountain gorilla (Gorilla Beringei Beringei) [2]. The park covers an area of 790,000 ha [3], and hosts the majority of the fragments of the last remaining habitat suitable for the mountain gorilla. Furthermore, the multitude of variety in nature and climate variables, including large lakes, open land savannah, vast forest areas, snow-covered mountain tops and erupting volcanoes, provides critical habitats for a great variety of the other large species of mammals we associate with Africa [1]. For this reason, the park was inscribed as a United Nations (UN) Educational, Scientific and Cultural Organization (UNESCO) World Heritage site in 1979. However, the NP is located in one of...

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Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China

Cited by 402 publications

References 54 publications

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Dynamics of Shrimp Farming in the Southwestern Coastal Districts of Bangladesh Using a Shrimp Yield Dataset (SYD) and Landsat Satellite Archives

Analysis of Long-Term Spatio-Temporal Trends in Land Use/Land Cover in Devikulam Taluk, Kerala Using Geospatial Techniques

Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo

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