County-scale land use/land cover simulation based on multiple models

doi:10.6046/zrzyyg.2022274

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Abstract

Land use/land cover (LULC) simulation is essential for research on changes in land use. Based on the Google Earth Engine (GEE) platform, this study extracted the high-precision LULC information of Luquan County from 1991 to 2021 and analyzed the spatio-temporal evolution pattern. Then, this study analyzed the factors driving LULC changes using a random forest model and compared the simulation results of Luquan County obtained using the cellular automata-Markov (CA-Markov), land change modeler (LCM), future land use simulation (FLUS), and patch-generating land use simulation (PLUS). Finally, this study forecast the LULC change scenario in Luquan County in 2027 using the optimal model. The results show that: ① From 1991 to 2021, the spatial LULC pattern of Luquan County was dominated by forestland, grassland, and farmland. The areas of farmland and waterbodies increased by 89.26 km² and 27.72 km², respectively, the areas of forestland, construction land, and bare land increased continuously by 724.25 km², 21.08 km², and 13.67 km², respectively, and the grassland decreased at an annual average rate of 29.20 km²; ② The LULC in Luquan County was primarily influenced by topographic conditions (elevation and slope); ③ The simulation effects of the four LULC models were in the order of PLUS > FLUS > CA-Markov > LCM, with Kappa coefficient of 0.63, 0.58, 0.46 and 0.35, respectively and the overall accuracy of 0.78, 0.75, 0.66 and 0.58, respectively; ④ The spatial LULC pattern in Luquan County in 2027 will share similarities with that in 2021. From 2021 to 2027, the areas of farmland land, grassland, and water bodies will decrease at a rate of 40.21 km²/a, 4.51 km²/a, and 0.70 km²/a, respectively, while the forestland, construction land, and bare land will expand by 265.52 km², 4.85 km², and 2.08 km², respectively.

Keywords LULC random forest multi-model Luquan County

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Issue Date: 21 December 2023

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	Suling HE
	Zenghong HE
	Jiya PAN
	Jinliang WANG

Cite this article:

Suling HE,Zenghong HE,Jiya PAN, et al. County-scale land use/land cover simulation based on multiple models[J]. Remote Sensing for Natural Resources, 2023, 35(4): 201-213.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022274 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/201

Fig.1 Location of the study area

Fig.2 Comparison of different LULC products

Fig.3 Drivers of land use/land cover change

Tab.1 Transfer rules between different land types

景观指数	公式	指标描述与意义
聚集度(AI)	$A I = g i j m a x → g i j$	式中g_ij为景观类型的相似邻接斑块数目。AI反映景观要素斑块的聚集程度,值越大,景观破碎度越低
平均斑块面积(AREA_MN)	$A R E A_M N = A N$	式中A为景观总面积; N为所有斑块数量。AREA_MN为反映景观结构的有效指标,值越大,景观破碎度越低
蔓延度(CONTAG)	$C O N T A G = 1 + ∫ i = 1 n ∫ j = 1 n p i f i j ∫ i = 1 n f i j l n p i f i j ∫ i = 1 n f i j 2 l n n × 100$	式中p_i为第i类斑块所占的面积百分比; f_ij为第i类和第j类斑块毗邻的数量; n为斑块总数。CONTAG反映景观斑块类型的团聚程度或延展趋势,值越大,景观破碎度越低
边缘密度(ED)	$E D = ∫ i n E i A × 10 - 6$	式中E_i为第i类斑块边界总长度。ED越大,景观破碎度越高
景观形状指数(LSI)	$L S I = 0.25 ∫ i n e j i * A$	式中 $e i j $ 为景观斑块类型j和i之间的边缘总长度。LSI反映景观斑块要素的形状。LSI*越大,景观破碎度越高
斑块密度(PD)	$P D = ∫ i n N i A$	式中N_i为第i类景观斑块数量。PD值越高,景观破碎度越高

Tab.2 Typical landscape indices for measuring landscape fragmentation

Fig.4 Temporal and spatial changes of LULC in Luquan from 1991 to 2021

Tab.3 Importance of different drivers on LULC changes

Tab.4 Accuracy of different land use/land cover simulation models

Fig.5 The area of different LULC types in Luquan County in 2021 simulated by the four models

Fig.6 The spatial pattern of LULC in Luquan County in 2021 simulated by 4 models

Fig.7 The LULC landscape fragmentation index simulated by the 4 models

Fig.8 Spatial distribution pattern and area of LULC in Luquan County in 2027

Fig.9 Landscape indices of different LULCs in Luquan County in 2021

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