Please wait a minute...
 
Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 120-129     DOI: 10.6046/gtzyyg.2020.01.17
|
Spatio-temporal variation in the land ecological risk of Yan’an City
Yifang DUAN1,2, Zhiyuan REN1(), Xiao ZHOU1, Yijie SUN1
1. College of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China
2. College of Environment and Planning, Liaocheng University, Liaocheng 252000, China
Download: PDF(5439 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

As a key and demonstration area for the implementation of the national ecological project of returning farmland to forestry, Yan’an City is a concentrated area of ecological problems in China. It is of great significance to study the spatial and temporal differences of land ecological risks for the sustainable development of regional land and the formulation of differentiated land and resources development policies. According to the basic characteristics of land ecosystem, four risk indicators, namely vegetation coverage, percentage anomaly precipitation, land use structure risk index and soil erosion index, were selected to construct a comprehensive evaluation model of land ecological risk. Then on the basis of pixel scale, each factor index and comprehensive index of land ecological risk were calculated. In combination with exploratory spatial data analysis (ESDA), the spatial and temporal evolution of land ecological risk and spatial agglomeration effect in Yan’an City from 2000 to 2015 was analyzed. Then the corresponding suggestions for comprehensive management of land in different regions were put forward. The results are as follows: The land ecology of Yan’an City is in good condition as a whole, whereas the land comprehensive ecological risk and the risk of the four ecological factors temporally decrease on the whole; nevertheless, the area of Baota District and Luochuan County is higher in this aspect, and high comprehensive risk areas increase slightly. The land comprehensive ecological risk of Yan’an City shows a strong spatial agglomeration. Hot spots include urban hot spots located in urban construction areas and northern hot spots distributed in five districts and counties of Zichang County, Ansai District, Yanchuan County, and Wuqi County. The cold points are mainly located in Huanglong County, Yichuan County, Huangling County, Fuxian County and the southwest area of Ganquan County. Thanks to the implementation of national eco-engineering measures such as returning farmland to forestry (grassland) and closing hillsides for reforestation, the agglomeration degree of hot spots has been gradually weakening. However, the area of urban hot spots in Baota District continues to increase, and hence attention should be paid to strengthening ecological management. As cold spot areas in northwest Fuxian and western Yichuan County has been shrinking because of climate drought, attention should be paid to optimizing the allocation of water resources.

Keywords land ecological risk      exploratory spatial data analysis      spatial agglomeration      Yan’an City     
:  TP79  
Corresponding Authors: Zhiyuan REN     E-mail: renzhy@snnu.edu.cn
Issue Date: 14 March 2020
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Yifang DUAN
Zhiyuan REN
Xiao ZHOU
Yijie SUN
Cite this article:   
Yifang DUAN,Zhiyuan REN,Xiao ZHOU, et al. Spatio-temporal variation in the land ecological risk of Yan’an City[J]. Remote Sensing for Land & Resources, 2020, 32(1): 120-129.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.17     OR     https://www.gtzyyg.com/EN/Y2020/V32/I1/120
Fig.1  Overview of study area
风险指标 计算公式或模型 注释 标准化公式
植被覆盖度指数 VFC=NDVI-NDVIminNDVImax-NDVImin VFC为植被覆盖度指数[16,17],值越大,风险越小; NDVIminNDVImax分别为归一化植被指数的最小值和最大值 逆向标准化
PVFC=VFCmax-VFCVFCmax-VFCmin
降水距平百分比指数 Si=Ri-R-a-bR-a-b·100% Si为第i年降水距平百分比指数[18,19],值越大,风险越小; Ri为第i年年降水量; Rˉa-b为多年平均降水量 逆向标准化
PS=Smax-SSmax-Smin
土地利用结构风险指数 ERI=i=1nBiB·Wi·100% ERI为土地利用结构风险指数,值越大,风险越大; Bi为第i类土地利用类型面积; B为研究区总面积; Wi表示第i类土地利用类型生态风险权重,借鉴臧淑英[7]和马彩虹[8]的研究成果,采用AHP法确定。林地、草地、水体、耕地、建设用地和未利用地的风险权重分别为0.035,0.055,0.06,0.314,0.402和0.134 正向标准化
PERI=ERI-ERIminERImax-ERImin
土壤侵蚀风险指数 A=R·K·C·LS·T A为土壤侵蚀风险指数,t·hm-1,值越大,风险越大; R为降雨侵蚀力因子; K为土壤可蚀性因子; C为覆盖与管理因子; LS为坡长坡度因子; T为土壤保持措施因子,依据坡度范围: ≤6°,(6°,15°],(15°,25°],﹥25°分别取值为0.20,0.35,0.65和0.80; C,LST为无量纲因子,采用相应的经验公式模型计算得到[20,21,22] 正向标准化
PA=A-AminAmax-Amin
Tab.1  Selection of risk evaluation index and standardization method
等级 Ⅰ级 Ⅱ级 Ⅲ级 Ⅳ级 Ⅴ级
风险指数 ≤0.1 (0.1,0.25] (0.25,0.4] (0.4,0.55] >0.55
风险状态 较低 中等 较高
Tab.2  Classification standards of land ecological risk
Fig.2  Spatial distribution of vegetation coverage in different period
Fig.3  Spatial distribution of percentage of precipitation anomaly in different period
Fig.4  Distribution of land use structure risk grade in different period
Fig.5  Distribution of soil water erosion in different period
Fig.6  Spatio-temporal distribution of land comprehensive ecological risk from 2000 to 2015
区县 2000年 2010年 2015年
指数 等级 指数 等级 指数 等级
黄陵县 0.034 I 0.166 II 0.055 I
延长县 0.819 V 0.696 IV 0.533 III
安塞区 0.713 IV 0.551 IV 0.59 IV
志丹县 0.709 IV 0.498 III 0.505 III
吴起县 0.741 IV 0.546 III 0.66 IV
宝塔区 0.474 III 0.436 III 0.487 III
延川县 0.782 V 0.785 V 0.622 IV
子长县 0.694 IV 0.559 IV 0.625 IV
甘泉县 0.212 II 0.148 I 0.284 II
富县 0.073 I 0.034 I 0.159 II
洛川县 0.474 III 0.566 IV 0.523 III
宜川县 0.396 III 0.209 II 0.255 II
黄龙县 0.016 I 0.057 I 0.072 I
Tab.3  Land ecological risk index and its grades in every county
Fig.7  Spatial and temporal evolution of hot and cold spots of land comprehensive ecological risk in Yan’an City
[1] Fan J H, Wang Y, Zhou Z . Dynamic ecological risk assessment and management of land use in the middle reaches of the Heihe River based on landscape patterns and spatial statistics[J]. Sustainability, 2016,8(6):536-551.
[2] 刘勇, 邢育刚, 李晋昌 . 土地生态风险评价的理论基础及模型构建[J]. 中国土地科学, 2012,26(6):20-25.
[2] Liu Y, Xing Y G, Li J C . Theoretical basis and model development for land ecological risk assessment[J]. China Land Sciences, 2012,26(6):20-25.
[3] Hunsaker C T, Graham R L, Suter G W , et al. Assessing ecological risk on a regional scale[J]. Environmental Management, 1990,14(3):325-332.
[4] 陈辉, 刘劲松, 曹宇 , 等. 生态风险评价研究进展[J]. 生态学报, 2006,26(5):1558-1566.
[4] Chen H, Liu J S, Cao Y , et al. Progresses of ecological risk assessment[J]. Acta Ecologica Sinica, 2006,26(5):1558-1566.
[5] 傅伯杰 . 我国生态系统研究的发展趋势与优先领域[J]. 地理研究, 2010,29(3):383-396.
[5] Fu B J . Trends and priority areas in ecosystem research of China[J]. Geographical Research, 2010,29(3):383-396.
[6] 任志远, 刘焱序 . 基于价值量的区域生态安全评价方法探索——以陕北能源区为例[J]. 地理研究, 2013,32(10):1771-1781.
[6] Ren Z Y, Liu Y X . Exploring the regional ecological security evaluation methods based on values:A case study in the energy region of northern Shaanxi[J]. Geographical Research, 2013,32(10):1771-1781.
[7] 臧淑英, 梁欣, 张思冲 . 基于GIS的大庆市土地利用生态风险分析[J]. 自然灾害学报, 2005,14(4):141-145.
[7] Zang S Y, Liang X, Zhang S C . GIS-based analysis of ecological risk on land-use in Daqing City[J]. Journal of Natural Disasters, 2005,14(4):141-145.
[8] 马彩虹 . 陕西黄土台塬区土地生态风险时空差异性评价[J]. 水土保持研究, 2014,21(5):216-220.
[8] Ma C H . Assessment on spatio-temporal ecological risk in loess highland region of Shaanxi Province[J]. Research of Soil and Water Conservation, 2014,21(5):216-220.
[9] 莫宏伟, 任志远 . 风沙过渡区土地生态价值及生态风险动态研究——以陕北神木县为例[J]. 中国沙漠, 2010,30(2):357-362.
[9] Mo H W, Ren Z Y . Study on changes of land ecosystem value and ecological risk in sand blowing region:A case study over Shenmu County of Shaanxi Province[J]. Journal of Desert Research, 2010,30(2):357-362.
[10] 徐兰, 罗维, 周宝同 . 基于土地利用变化的农牧交错带典型流域生态风险评价——以洋河为例[J]. 自然资源学报, 2015,30(4):580-590.
[10] Xu L, Luo W, Zhou B T . Landscape ecological risk assessment of farming-pastoral ecozone based on land use change—a case study of the Yanghe Watershed,China[J]. Journal of Natural Resources, 2015,30(4):580-590.
[11] 周汝佳, 张永战, 何华春 . 基于土地利用变化的盐城海岸带生态风险评价[J]. 地理研究, 2016,35(6):1017-1028.
[11] Zhou R J, Zhang Y Z, He H C . Ecological risk assessment based on land use changes in the coastal area in Yancheng City[J]. Geographical Research, 2016,35(6):1017-1028.
[12] 孙贤斌, 刘红玉 . 江苏盐城市海滨土地利用对景观生态风险的影响[J]. 国土资源遥感, 2011,23(3):140-145.doi: 10.6046/gtzyyg.2011.03.25.
[12] Sun X B, Liu H Y . The effect of land use on landscape ecological risk in Yancheng Coastal Area,Jiangsu Province[J]. Remote Sensing for Land and Resources, 2011,23(3):140-145.doi: 10.6046/gtzyyg.2011.03.25.
[13] 彭文君, 舒英格 . 基于GIS的石漠化山区县域土地利用空间变化的生态风险测度[J]. 水土保持研究, 2018,25(1):342-348,355.
[13] Peng W J, Shu Y G . Assessment on ecological risk of land use spatial change at county level in the rocky desertification mountainous area based on GIS[J]. Research of Soil and Water Conservation, 2018,25(1):342-348,355.
[14] 孙洪波, 杨桂山, 苏伟忠 , 等. 沿江地区土地利用生态风险评价——以长江三角洲南京地区为例[J]. 生态学报, 2010,30(20):5616-5625.
[14] Sun H B, Yang G S, Su W Z , et al. Ecological risk assessment of land use in the area along Changjiang River:A case study of Nanjing,China[J]. Acta Ecologica Sinica, 2010,30(20):5616-5625.
[15] 虞燕娜, 朱江, 吴绍华 , 等. 多风险源驱动下的土地生态风险评价——以江苏省射阳县为例[J]. 自然资源学报, 2016,31(8):1264-1274.
[15] Yu Y N, Zhu J, Wu S H , et al. Assessment of land ecological risks driven by multi-sources:A case study of Sheyang County,Jiangsu Province[J]. Journal of Natural Resources, 2016,31(8):1264-1274.
[16] 王朗, 傅伯杰, 吕一河 , 等. 生态恢复背景下陕北地区植被覆盖的时空变化[J]. 应用生态学报, 2010,21(8):2109-2116.
[16] Wang L, Fu B J, Lyu Y H , et al. Spatio-temporal variation of vegetation cover in northern Shaanxi Province under the background of ecological restoration[J]. Chinese Journal of Applied Ecology, 2010,21(8):2109-2116.
[17] 赵舒怡, 宫兆宁, 刘旭颖 . 2001—2013年华北地区植被覆盖度与干旱条件的相关分析[J]. 地理学报, 2015,70(5):717-729.
[17] Zhao S Y, Gong Z N, Liu X Y . Correlation analysis between vegetation coverage and climate drought conditions in North China during 2001—2013[J]. Acta Geographica Sinica, 2015,70(5):717-729.
[18] 袁文平, 周广胜 . 干旱指标的理论分析与研究展望[J]. 地球科学进展, 2004,19(6):982-991.
[18] Yuan W P, Zhou G S . Theoratical study and research prospect on drought indices[J]. Advances in Earth Science, 2004,19(6):982-991.
[19] Yu M X, Li Q F, Hayes M J , et al. Are droughts becoming more frequent or severe in China based on the standardized precipitation evapotranspiration index:1951—2010?[J]. International Journal of Climatology, 2014,34(3):545-558.
[20] Renard K G, Foster R, Weesies G , et al. Predicting Soil Erosion by Water:A Guide to Conservation Planning with the Revised Universal Soil Loss equation(RUSLE)[M]. Agricultural Handbook.Washington,DC(USA):ARS; 1997.
[21] 冯强, 赵文武 . USLE/RUSLE中植被覆盖与管理因子研究进展[J]. 生态学报, 2014,34(16):4461-4472.
[21] Feng Q, Zhao W W . The study on cover-management factor in USLE and RUSLE:A review[J]. Acta Ecologica Sinica, 2014,34(16):4461-4472.
[22] 蒋欣阳, 贾志斌, 张雪峰 , 等. 内蒙古锡林郭勒盟景观尺度土壤保持功能的空间分布[J]. 地球环境学报, 2018,9(1):64-78.
[22] Jiang X Y, Jia Z B, Zhang X F , et al. Soil conservation function and its spatial distribution of different landscapes in Xilin Gol League,Inner Mongolia[J]. Journal of Earth Environment, 2018,9(1):64-78.
[23] 张广纳, 邵景安, 王金亮 , 等. 三峡库区重庆段农村面源污染时空格局演变特征[J]. 自然资源学报, 2015,30(7):1197-1209.
[23] Zhang G N, Shao J A, Wang J L , et al. Spatial and temporal variations of agricultural non-point source pollution in the Three Gorges Reservoir Area of Chongqing[J]. Journal of Natural Resources, 2015,30(7):1197-1209.
[24] 马世五, 谢德体, 张孝成 , 等. 三峡库区重庆段土地生态状况时空格局演变特征[J]. 生态学报, 2018,38(23):8512-8525.
[24] Ma S W, Xie D T, Zhang X C , et al. Spatiotemporal variation in the ecological status of the Three Gorges Reservoir area in Chongqing,China[J]. Acta Ecologica Sinica, 2018,38(23):8512-8525.
[25] 韩磊, 朱会利, 刘钊 . 延安市退耕还林前后土地利用动态变化分析[J]. 西北师范大学学报(自然科学版), 2017,53(5):101-108.
[25] Han L, Zhu H L, Liu Z . Analysis on land use dynamic changes of pre and post returning farmland to forestland in Yan’an City[J]. Journal of Northwest Normal University(Natural Science), 2017,53(5):101-108.
[26] 侯孟阳, 姚顺波, 邓元杰 , 等. 格网尺度下延安市生态服务价值时空演变格局与分异特征——基于退耕还林工程的实施背景[J]. 自然资源学报, 2019,34(3):539-552.
[26] Hou M Y, Yao S B, Deng Y J , et al. Spatial-temporal evolution pattern and differentiation of ecological service value in Yan’an City at the grid scale based on sloping land conversion program[J]. Journal of Natural Resources, 2019,34(3):539-552.
[1] SONG Qi, FENG Chunhui, MA Ziqiang, WANG Nan, JI Wenjun, PENG Jie. Simulation of land use change in oasis of arid areas based on Landsat images from 1990 to 2019[J]. Remote Sensing for Natural Resources, 2022, 34(1): 198-209.
[2] REN Chaofeng, PU Yuchi, ZHANG Fuqiang. A method for extracting match pairs of UAV images considering geospatial information[J]. Remote Sensing for Natural Resources, 2022, 34(1): 85-92.
[3] ZANG Liri, YANG Shuwen, SHEN Shunfa, XUE Qing, QIN Xiaowei. A registration algorithm of images with special textures coupling a watershed with mathematical morphology[J]. Remote Sensing for Natural Resources, 2022, 34(1): 76-84.
[4] PAN Jianping, XU Yongjie, LI Mingming, HU Yong, WANG Chunxiao. Research and development of automatic detection technologies for changes in vegetation regions based on correlation coefficients and feature analysis[J]. Remote Sensing for Natural Resources, 2022, 34(1): 67-75.
[5] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[6] JIANG Na, CHEN Chao, HAN Haifeng. An optimization method of DEM resolution for land type statistical model of coastal zones[J]. Remote Sensing for Natural Resources, 2022, 34(1): 34-42.
[7] WU Fang, LI Yu, JIN Dingjian, LI Tianqi, GUO Hua, ZHANG Qijie. Application of 3D information extraction technology of ground obstacles in the flight trajectory planning of UAV airborne geophysical exploration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 286-292.
[8] WANG Qian, REN Guangli. Application of hyperspectral remote sensing data-based anomaly extraction in copper-gold prospecting in the Solake area in the Altyn metallogenic belt, Xinjiang[J]. Remote Sensing for Natural Resources, 2022, 34(1): 277-285.
[9] LIU Wen, WANG Meng, SONG Ban, YU Tianbin, HUANG Xichao, JIANG Yu, SUN Yujiang. Surveys and chain structure study of potential hazards of ice avalanches based on optical remote sensing technology: A case study of southeast Tibet[J]. Remote Sensing for Natural Resources, 2022, 34(1): 265-276.
[10] YAO Jinxi, ZHANG Zhi, ZHANG Kun. An analysis of the characteristics, causes, and trends of spatio-temporal changes in vegetation in the Nuomuhong alluvial fan based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 249-256.
[11] WU Yijie, KONG Xuesong. Simulation and development mode suggestions of the spatial pattern of “ecology-agriculture-construction” land in Jiangsu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 238-248.
[12] ZHANG Qinrui, ZHAO Liangjun, LIN Guojun, WAN Honglin. Ecological environment assessment of three-river confluence in Yibin City using improved remote sensing ecological index[J]. Remote Sensing for Natural Resources, 2022, 34(1): 230-237.
[13] HU Yingying, DAI Shengpei, LUO Hongxia, LI Hailiang, LI Maofen, ZHENG Qian, YU Xuan, LI Ning. Spatio-temporal change characteristics of rubber forest phenology in Hainan Island during 2001—2015[J]. Remote Sensing for Natural Resources, 2022, 34(1): 210-217.
[14] SUN Yiming, ZHANG Baogang, WU Qizhong, LIU Aobo, GAO Chao, NIU Jing, HE Ping. Application of domestic low-cost micro-satellite images in urban bare land identification[J]. Remote Sensing for Natural Resources, 2022, 34(1): 189-197.
[15] ZHENG Xiucheng, ZHOU Bin, LEI Hui, HUANG Qiyu, YE Haolin. Extraction and spatio-temporal change analysis of the tidal flat in Cixi section of Hangzhou Bay based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2022, 34(1): 18-26.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech