基于InVEST与ANN-CA模型的环洞庭湖区土地利用碳储量情景模拟

杨宇萍, 胡文敏, 贾冠宇, 李果, 李毅

南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (4) : 175-184.

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南京林业大学学报（自然科学版）
PDF(3694 KB)
南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (4) : 175-184. DOI: 10.12302/j.issn.1000-2006.202110020
研究论文

基于InVEST与ANN-CA模型的环洞庭湖区土地利用碳储量情景模拟

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Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area

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摘要

【目的】基于多模型模拟预测土地利用碳存储,为权衡环洞庭湖区的土地利用发展模式和提升区域固碳能力提供科学依据。【方法】以环洞庭湖区2005、2010、2015年土地利用变化为切入点,基于InVEST模型与人工神经网络CA(ANN-CA)模型,对碳存储进行量化和空间情景模拟,探讨环洞庭湖区水环境保护情景、生物多样性保护情景以及碳中和情景下的土地利用及其碳储量变化模式。【结果】①到2035年,环洞庭湖区在水环境保护情景下的主要土地利用变化为水域面积增长1.50 km2(增加0.02%),带来的碳储量变化程度极小;在生物多样性保护情景下的主要地类变化为林地增长10.78 km2(增加0.05%),可使碳储量增长27.10×106 t(增长44.28%);在碳中和情景下主要地类变化为耕地缩减432.02 km2(减少1.63%),导致碳储量减少0.81×106 t(减少1.64%);土地利用面积与碳储量变化呈正相关关系。②环洞庭湖区高程、坡度、到公路距离与碳储量呈负相关,向阳坡碳储量高于阴坡;影响环洞庭湖区土地利用变化的主要驱动因子为坡度、高程和人口密度。【结论】基于InVEST与ANN-CA模型可模拟多情景下的土地利用及碳储量,可为环洞庭湖区土地利用结构的优化配置以及生态保护和区域发展的权衡提供情景参考。

Abstract

【Objective】Multi-model simulation was utilized to explore land-use associated carbon storage, providing scientific reference for development in the Dongting Lake area.【Method】Land-use changes from 2005, 2010 and 2015 were taken as the research object and InVEST and ANN-CA were used to quantitatively and spatially simulate the changing land-use patterns and the associated carbon storage around Dongting Lake under protection of the water environment and biodiversity. Different carbon neutralization scenarios are discussed.【Result】In terms of land use area and carbon stock changes in the Dongting Lake area, the main land type change under water protection is a 1.50 km2 (increase 0.02%) increase in the water area by 2035, which will lead to minimal carbon stock changes; the main land type change under biodiversity protection is a 10.78 km2 (increase 0.05%) increase in woodland, which will result in an increase of 27.10×106 t (increase 44.28%) of carbon stock; and the main land type change under carbon neutralization is a decrease of 432.02 km2 (reduce 1.63%) in the amount of arable land, resulting in a decrease of 0.81×106 t (reduce 1.64%) carbon stock. Positive correlation was observed between land use area and carbon stock changes. DEM, slope and distance to the highway were negatively correlated with carbon storage and more carbon stored on sunny slopes than shady slopes. The main driving factors affecting land-use changes in Dongting Lake area are slope, DEM and population density.【Conclusion】The use of InVEST and ANN-CA was beneficial in simulating land use and carbon storage under multiple scenarios and provided a reference for the optimal allocation of land to obtain balance between ecological protection and regional development in the Dongting Lake area.

关键词

环洞庭湖区 / 碳储量 / 人工神经网络CA模型 / InVEST模型

Key words

Dongting Lake area / carbon storage / artificial neural network CA model / InVEST model

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杨宇萍, 胡文敏, 贾冠宇, 等. 基于InVEST与ANN-CA模型的环洞庭湖区土地利用碳储量情景模拟[J]. 南京林业大学学报（自然科学版）. 2023, 47(4): 175-184 https://doi.org/10.12302/j.issn.1000-2006.202110020
YANG Yuping, HU Wenmin, JIA Guanyu, et al. Scenario simulation integrating the ANN-CA model with the InVEST model to investigate land-based carbon storage in the Dongting Lake area[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2023, 47(4): 175-184 https://doi.org/10.12302/j.issn.1000-2006.202110020
中图分类号： X321   

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基金

湖南省教育厅科学研究项目重点项目(21A0153)
湖南省自然科学基金青年基金项目(2022JJ40862)

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