Integrating land use/land cover change with change in functional zones’ boundary of the East Dongting Lake National Nature Reserve, China

Xiao, Juan; Watanabe, Teiji; Lu, Xixi; Chand, Mohan Bahadur; Umarhadi, Deha Agus; Chen, Xinyu; Avtar, Ram

doi:10.1016/j.pce.2021.103041

Cited by 7 publications

(3 citation statements)

References 76 publications

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“…The transition matrix describes the pixel ratio that changes from one land cover class to another (Muhammad et al 2022). The transition matrix presents the probability value of land cover change at any given time (Xiao et al 2022). The diagonal values represent a measure of class stabilization, and each off-diagonal value represents a transition from one class to a different class (Muhammad et al 2022).…”

Section: Area Change and Transition Matrixmentioning

confidence: 99%

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

Iskandar,

Saidah,

Kurnia

et al. 2024

J. Sylva Lestari

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A management unit-based land cover change analysis was examined in Kahayan Tengah Forest Management Unit (FMU) to understand past, present, and future land cover to assist forest management planning in Kahayan Tengah FMU. This study aims to model land cover change in 2011 and 2016, predict 2021, and simulate land cover in 2026 in Kahayan Tengah FMU. Modeling land cover prediction and simulation using MOLUSCE from the QGIS plugin. The results revealed that agricultural land experienced significant increase in total area during 2011–2016. Modeling potential land cover transitions in 2011 and 2016 with the Artificial Neural Network method showed a Kappa coefficient of 0.701 in the good category, and simulation of land cover in 2021 with the Cellular Automata method showed a Kappa coefficient of 0.672 in the good category. By 2026, the agricultural land will continue to increase while forest land tends to remain stable in its total area. This study managed to predict land cover in 2021 and simulated 2026 with good accuracy. Thus, this data and information can support forest management planning in Kahayan Tengah FMU. Keywords: forest management unit, Kahayan Tengah, land cover change, land cover prediction, MOLUSCE

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Section: Area Change and Transition Matrixmentioning

confidence: 99%

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

Iskandar,

Saidah,

Kurnia

et al. 2024

J. Sylva Lestari

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“…The core area is prohibited from development, the test area is moderately developed, and the buffer area is allowed to be fully developed. However, the test area and buffer area do not strictly restrict the expansion of RRL, which leads to the rapid growth of RRL in these areas to a certain extent (Xiao et al, 2021). Although the area of RRL in the natural reserve shows a trend of growth, through the analysis of the ordinary least-squares model, it is found that the presence of natural reserves has a significant negative correlation with the area of RRL, which also shows that the establishment of natural reserves still limits the growth rate of RRL to a certain extent.…”

Section: The Distribution and Change In Rural Residential Land Show D...mentioning

confidence: 99%

What Is the Impact of the Establishment of Natural Reserves on Rural Residential Land? An Empirical Study From Hunan Province, China

Tang

Jiang

et al. 2022

Front. Environ. Sci.

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The rural residential land (RRL) in natural reserves has been deeply transformed due to the disturbance constrained by ecological protection policies. Exploring the distribution characteristics and driving factors of RRL in natural reserves and non-natural reserves will help to promote the governance of land space and alleviate the contradiction of land use. Therefore, taking 122 county-level administrative regions in Hunan Province as an example, this article analyzes and compares the spatiotemporal distribution characteristics of RRL in natural reserves and non-natural reserves by using land use change dynamics, nuclear density analysis, the transfer matrix model, and the ordinary least-squares model and explores how the establishment of natural reserves affects the RRL area change. The results show that (1) the overall RRL area in Hunan changed from 171,162.27 hm2 in 2000 to 169,914.6 hm2 in 2020, with a total reduction of 1,247.67 hm2 and a decrease of 0.73%, and the distribution of the RRL area change presented a hot trend in the northeast and a cold trend in the southwest. (2) The occupation of urban construction land is the main reason for the reduction in RRL area, and the transformation of cultivated land and forestland into RRL is the main source of the increase in RRL area. (3) During 2000–2020, the overall RRL in natural reserves increased by 1,538.37 hm2, with an increase of 0.11%, while the overall RRL in non-natural reserves decreased by 2,786.04 hm2, with a decrease of 0.14%. (4) The establishment of natural reserves has a significant negative correlation with the area of RRL in 2000, 2010, and 2020, indicating that the establishment of natural reserves can limit the growth speed of the RRL area to a certain extent, but is affected by factors such as economic development and rural population growth; it cannot directly promote the overall reduction of RRL area. The results of this study can provide a reference for decision-making related to the spatial structure optimization of natural reserves and non-natural protected RRL and the coordinated development of urban and rural areas.

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“…Regarding the monitoring and management of landscape resources, the identification of LULC is often indispensable and used as the basis for landscape heterogeneity indices [28]. For example, Xiao et al [44] combined land use derived from image classification with landscape indicators to analyze changes in the functional zone boundaries of wetland landscapes in lake reserves. As for landscape features, numerous studies have shown that the contextual feature of imagery is beneficial for landscape functional zoning as it can effectively measure the morphology and spatial features of terrestrial landscapes [45].…”

Section: Introductionmentioning

confidence: 99%

A Functional Zoning Method in Rural Landscape Based on High-Resolution Satellite Imagery

Zheng,

Dian,

Guo

et al. 2023

Remote Sensing

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Mapping functional zones for rural landscapes is the foundational work for rural land use planning and plays a very important role in the economic development and resource management utilization of rural areas. However, the traditional manual delineation of functional zone boundaries empirically in rural areas is labor-intensive, time-consuming, and lacks the consideration of spatial landscape patterns. The emergence of high-resolution remote sensing imagery and image segmentation has facilitated the analysis of ground landscape information and patterns, but there is still a lack of functional zone boundary mapping methods applicable to rural landscapes. To address this, we propose a functional zoning method called multiscale merging of landscape contextual and shape characteristics with heterogeneity indices (M2LHI) for mapping geographic boundaries for rural landscapes based on high-resolution remote sensing imagery. The landscape contextual features were first constructed based on the geospatial distances of landscape types, and then, the dominance index and shape index were introduced to quantify the landscape heterogeneity by object-oriented image analysis. Then, the automated merging of adjacent landscape units based on the thresholds of the landscape heterogeneity indices was performed to map the initial zones. The final rural functional zones were defined based on the main function in the zone. The study was carried out in three typical rural landscapes (hilly countryside, flat countryside, and grassland countryside) located in Fujian, Xinjiang, and Inner Mongolia, China, and the freely available Gaofen-2 (GF-2) satellite imagery was used as the data source. We compared the boundaries of mapped functional zones and reference functional zones, and the matching and inclusion ratios of the final functional zones mapped in each case were bigger than 78%, indicating that the M2LHI method has a high ability to map the functional spatial patterns. The overall accuracies of mapping functional zones with different functions were 95.9%, 89.0%, and 92.1% for the respective cases. The results demonstrated that the M2LHI method effectively quantifies landscape heterogeneity and accurately delineates functional zones with different landscape patterns. It can provide a scientific basis for rural planning and management and efficiently draw reasonable geographic boundaries for rural functional zones.

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Integrating land use/land cover change with change in functional zones’ boundary of the East Dongting Lake National Nature Reserve, China

Cited by 7 publications

References 76 publications

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

Modeling Land Cover Change Using MOLUSCE in Kahayan Tengah Forest Management Unit, Kalimantan Tengah

What Is the Impact of the Establishment of Natural Reserves on Rural Residential Land? An Empirical Study From Hunan Province, China

A Functional Zoning Method in Rural Landscape Based on High-Resolution Satellite Imagery

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