Land use Dynamics and Landscape Patterns Assessment using Geoinformatics Techniques in Raniganj Coalfield, India

Anthropogenic land-use change is one of the main drivers of global environmental change. China has been on a fast track of land-use change since the Reform and Opening-up policy in 1978. In view of the situation, this study aims to optimize land use and provide a way to effectively coordinate the development and ecological protection in China. We took East Guangdong (EGD), an underdeveloped but populous region, as a case study. We used land-use changes indexes to demonstrate the land-use dynamics in EGD from 2000 to 2020, then identified the hot spots for fast-growing areas of built-up land and simulated land use in 2030 using the future land-use simulation (FLUS) model. The results indicated that the cropland and the built-up land changed in a large proportion during the study period. Then we established the ecological security pattern (ESP) according to the minimal cumulative resistance model (MCRM) based on the natural and socioeconomic factors. Corridors, buffer zones, and the key nodes were extracted by the MCRM to maintain landscape connectivity and key ecological processes of the study area. Moreover, the study showed the way to identify the conflict zones between future built-up land expansion with the corridors and buffer zones, which will be critical areas of consideration for future land-use management. Finally, some relevant policy recommendations are proposed based on the research result.

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Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China

Cities ◽

10.1016/j.cities.2021.103202 ◽

2021 ◽

Vol 114 ◽

pp. 103202

Author(s):

Rong Wu ◽

Jieyu Wang ◽

Dachuan Zhang ◽

Shaojian Wang

Keyword(s):

Land Use ◽

Random Forest ◽

Urban Land ◽

Urban Land Use ◽

Random Forest Algorithm ◽

Point Of Interest ◽

Land Use Dynamics ◽

Different Types

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How Can We Represent Seasonal Land Use Dynamics in SWAT and SWAT+ Models for African Cultivated Catchments?

Water ◽

10.3390/w12061541 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1541

Author(s):

Albert Nkwasa ◽

Celray James Chawanda ◽

Anna Msigwa ◽

Hans C. Komakech ◽

Boud Verbeiren ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Agricultural Land ◽

Swat Model ◽

Agricultural Practices ◽

Area Index ◽

Land Use Dynamics ◽

Cropping Seasons ◽

Decision Tables ◽

Land Cover Maps

In SWAT and SWAT+ models, the variations in hydrological processes are represented by Hydrological Response Units (HRUs). In the default models, agricultural land cover is represented by a single growing cycle. However, agricultural land use, especially in African cultivated catchments, typically consists of several cropping seasons, following dry and wet seasonal patterns, and are hence incorrectly represented in SWAT and SWAT+ default models. In this paper, we propose a procedure to incorporate agricultural seasonal land-use dynamics by (1) mapping land-use trajectories instead of static land-cover maps and (2) linking these trajectories to agricultural management settings. This approach was tested in SWAT and SWAT+ models of Usa catchment in Tanzania that is intensively cultivated by implementing dominant dynamic trajectories. Our results were evaluated with remote-sensing observations for Leaf Area Index (LAI), which showed that a single growing cycle did not well represent vegetation dynamics. A better agreement was obtained after implementing seasonal land-use dynamics for cultivated HRUs. It was concluded that the representation of seasonal land-use dynamics through trajectory implementation can lead to improved temporal patterns of LAI in default models. The SWAT+ model had higher flexibility in representing agricultural practices, using decision tables, and by being able to represent mixed cropping cultivations.

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The consequences for stream water quality of long-term changes in landscape patterns: Implications for land use management and policies

Land Use Policy ◽

10.1016/j.landusepol.2021.105679 ◽

2021 ◽

Vol 109 ◽

pp. 105679

Author(s):

António Carlos Pinheiro Fernandes ◽

Lisa Maria de Oliveira Martins ◽

Fernando António Leal Pacheco ◽

Luís Filipe Sanches Fernandes

Keyword(s):

Water Quality ◽

Land Use ◽

Stream Water ◽

Landscape Patterns ◽

Land Use Management ◽

Stream Water Quality ◽

Long Term Changes

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Assessment of Land-Cover/Land-Use Change and Landscape Patterns in the Two National Nature Reserves of Ebinur Lake Watershed, Xinjiang, China

Sustainability ◽

10.3390/su9050724 ◽

2017 ◽

Vol 9 (5) ◽

pp. 724 ◽

Cited By ~ 19

Author(s):

Fei Zhang ◽

Hsiang-te Kung ◽

Verner Johnson

Keyword(s):

Land Use ◽

Land Use Change ◽

Land Cover ◽

Nature Reserves ◽

Landscape Patterns ◽

Ebinur Lake ◽

National Nature Reserves

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Current and Future Land Use Characters of a National Central City in Eco-Fragile Region—A Case Study in Xi’an City Based on FLUS Model

Land ◽

10.3390/land10030286 ◽

2021 ◽

Vol 10 (3) ◽

pp. 286

Author(s):

Dingrao Feng ◽

Wenkai Bao ◽

Meichen Fu ◽

Min Zhang ◽

Yiyu Sun

Keyword(s):

Land Use ◽

Land Use Change ◽

Urban Development ◽

Urban Expansion ◽

Land Use Patterns ◽

Use Patterns ◽

Pattern Change ◽

Land Use Dynamics ◽

The Future ◽

Xi’An City

Land use change plays a key role in terrestrial systems and drives the process of ecological pattern change. It is important to investigate the process of land use change, predict land use patterns, and reveal the characteristics of land use dynamics. In this study, we adopted the Markov model and future land use (FLUS) model to predict the future land use conditions in Xi’an city. Furthermore, we investigated the characteristics of land use change from a novel perspective, i.e., via establishment of a complex network model. This model captured the characteristics of the land use system during different periods. The results indicated that urban expansion and cropland loss played an important role in land use pattern change. The future gravity center of urban development moved along the opposite direction to that from 2000 to 2015 in Xi’an city. Although the rate of urban expansion declined in the future, urban expansion remained the primary driver of land use change. The primary urban development directions were east-southeast (ENE), north-northeast (NNE) and west-southwest (WSW) from 1990 to 2000, 2000 to 2015, and 2015 to 2030, respectively. In fact, cropland played a vital role in land use dynamics regarding all land use types, and the stability of the land use system decreased in the future. Our study provides future land use patterns and a novel perspective to better understand land use change.

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