Research on applicability of remote sensing classification methods for land use in hilly area of southern Jiangsu

doi:10.14088/j.cnki.issn0439-8114.2021.05.027

Abstract

Abstract: Accurate, timely and effective land use information is of great significance to urban development planning as well as resources development and utilization. Jurong river basin was taken as the study area in order to evaluate the applicability of different remote sensing image classification methods in obtaining land cover information. With multi-temporal Landsat 8 OLI images, the land cover of Jurong river basin was obtained by supervised classification methods and decision tree method based on time-series characteristic indices. Comparison between the land cover information obtained from different extraction methods was carried on according to the overall classification accuracy and spatial distribution of ground objects. The results show that the overall classification accuracy and Kappa coefficient of support vector machine was the highest （96.4% and 0.948）, but the decision tree method based on time-series characteristic indices tends to be the closest to reality considering the spatial distribution of different ground objects;Maximum likelihood method and decision tree method were more applicable to land use classification in hilly region of southern Jiangsu province.

Key words: land cover, remote sensing image classification, supervised classification, decision tree, characteristic index, hilly area of southern Jiangsu

CLC Number:

P237

ZHANG Ya-xin, WU Zhi-yong, HE Hai. Research on applicability of remote sensing classification methods for land use in hilly area of southern Jiangsu[J]. HUBEI AGRICULTURAL SCIENCES, 2021, 60(5): 138-143.

References

[1] 张卫春,刘洪斌,武伟.基于随机森林和Sentinel-2影像数据的低山丘陵区土地利用分类——以重庆市江津区李市镇为例[J].长江流域资源与环境,2019,28(6):1334-1343.
[2] 唐华俊,吴文斌,杨鹏,等.土地利用/土地覆被变化(LUCC)模型研究进展[J].地理学报,2009,64(4):456-468.
[3] CHEN Y H, SU W, LI J, et al.Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas[J]. Advances in space research, 2009, 43(7): 1101-1110.
[4] 翟天林,金贵,邓祥征,等.基于多源遥感影像融合的武汉市土地利用分类方法研究[J].长江流域资源与环境,2016,25(10):1594-1602.
[5] 赵亚杰,王立辉,孔祥兵,等.基于Sentinel-2和Landsat8 OLI数据融合的土地利用分类研究[J].福建农林大学学报(自然科学版),2020,49(2):248-255.
[6] 李亚平,董增川,李庆航,等.苏南丘陵地区土地利用变化对水量水质影响研究[J].水利水电技术,2007(11):5-8.
[7] 李丽娟,姜德娟,李九一,等.土地利用/覆被变化的水文效应研究进展[J].自然资源学报,2007(2):211-224.
[8] EL-HAMES A S, AL-AHMADI F S. Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia[J]. Journal of king abdulaziz university, 2009,20(1): 167-191.
[9] YANG C, WU G F, DING K, et al.Improving land use/land cover classification by integrating pixel unmixing and decision tree methods[J]. Remote sensing, 2017, 9(12):1222.
[10] 张宏涛,黄宏胜,魏康宁,等.基于面向对象与规则的Sentinel-2A影像土地覆被分类——以江西省都昌县为例[J].测绘通报,2020(6):111-117.
[11] 成淑艳,曹生奎,曹广超,等.基于高分辨率遥感影像的青海湖沙柳河流域土地覆盖监督分类方法对比[J].水土保持通报,2018,38(5):261-268,353.
[12] 徐晓桃,韩涛,颉耀文.基于单时相MODIS数据的土地覆盖三种分类方法对比研究[J].干旱地区农业研究,2008(3):253-258.
[13] 周丽红,吴灏.基于Landsat8遥感影像的南昌县土地利用分类分析[J].人民长江,2017,48(S2):65-67,132.
[14] 陈波,胡玉福,喻攀,等.基于纹理和地形辅助的山区土地利用信息提取研究[J].地理与地理信息科学,2017,33(1):1-8.
[15] 谷晓天,高小红,马慧娟,等.复杂地形区土地利用/土地覆被分类机器学习方法比较研究[J].遥感技术与应用,2019,34(1):57-67.
[16] 邓书斌. ENVI 遥感图像处理方法[M].北京:高等教育出版社,2014.
[17] GHOGGALI N, MELGANI F.Automatic ground-truth validation with genetic algorithms for multispectral image classification[J]. IEEE transactions on geoscience and remote sensing, 2009, 47(7): 2172-2181.
[18] DEFRIES R S, TOWNSHEND J R G. NDVI-derived land cover classifications at a global scale[J]. International journal of remote sensing, 1994, 15(17): 3567-3586.
[19] 杨山. 发达地区城乡聚落形态的信息提取与分形研究——以无锡市为例[J]. 地理学报, 2000,55(6): 671-678.
[20] 徐涵秋. 利用改进的归一化差异水体指数(MNDWI)提取水体信息的研究[J]. 遥感学报, 2005(5): 589-595.