高标准农田建后遥感监测方法
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摘要
高标准农田建后常会有违规利用情况存在,如何实现对高标准农田建后实时、精准的遥感监测成为土地整治部门亟待解决的问题。全国高标准农田监测面积大,监测精度要求高,迫切需要一套适应于全国范围推广的高标准农田自动监测方法。以广东省东莞地区为研究区,对比了面向对象和最大似然2种自动遥感分类监测方法,面向对象总体精度达到98. 684 7%,Kappa系数为0. 983 3;而最大似然分类方法总体精度则为78. 587 1%,Kappa系数为0. 718 0。研究表明面向对象分类方法能较好地满足高标准农田建后利用情况遥感监测工作的需求,此方法可以为全国高标准农田建成后的实时监管提供高效、精准的决策信息,为国家耕地保护、粮食安全工作提供技术支撑。
At present,the area of high standard farmland has reached a certain scale in China. In the remote sensing monitoring for the utilization of high standard farmland,illegal utilization has appeared frequently. How to realize real-time and accurate remote sensing monitoring for high standard farmland has become an urgent problem for the land regulation department of the government. The national high standard farmland monitoring area is large,and the monitoring precision requirements are high. It is urgent for the government to study a set of high standard farmland automatic monitoring methods adapted to the nationwide extension. In this paper,two automatic remote sensing classification monitoring methods,i. e.,object oriented and maximum likelihood,are compared. The overall precision of the object-oriented method is 98. 684 7%,and the Kappa coefficient is 0. 983 3. The overall accuracy of the maximum likelihood classification method is 78. 587 1%,and the Kappa coefficient is 0. 718 0.The research shows that the object-oriented classification method can better meet the requirements of the high standard farmland. By popularizing the method,it is the way to provide efficient and accurate decision-making information for real time supervision of high standard farmland,and can provide technical support for the national protection of cultivated land and food security.
At present,the area of high standard farmland has reached a certain scale in China. In the remote sensing monitoring for the utilization of high standard farmland,illegal utilization has appeared frequently. How to realize real-time and accurate remote sensing monitoring for high standard farmland has become an urgent problem for the land regulation department of the government. The national high standard farmland monitoring area is large,and the monitoring precision requirements are high. It is urgent for the government to study a set of high standard farmland automatic monitoring methods adapted to the nationwide extension. In this paper,two automatic remote sensing classification monitoring methods,i. e.,object oriented and maximum likelihood,are compared. The overall precision of the object-oriented method is 98. 684 7%,and the Kappa coefficient is 0. 983 3. The overall accuracy of the maximum likelihood classification method is 78. 587 1%,and the Kappa coefficient is 0. 718 0.The research shows that the object-oriented classification method can better meet the requirements of the high standard farmland. By popularizing the method,it is the way to provide efficient and accurate decision-making information for real time supervision of high standard farmland,and can provide technical support for the national protection of cultivated land and food security.
引文
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[7]王岩,王晓青,窦爱霞.面向对象遥感分类方法在汶川地震震害提取中的应用[J].地震,2009,29(3):54-60.Wang Y,Wang X Q,Dou A X. Building damage detection of the2008 Wenchuan,China earthquake based on object-oriented classification method[J]. Earthquake,2009,29(3):54-60.
[8] Hussain E,Shan J. Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multi-temporal data[J]. GIScience and Remote Sensing,2016,53(2):164-182.
[9] Liang J,Yang J Y,Zhang C,et al. A comparison of two object-oriented methods for land-use/cover change detection with SPOT5imagery[J]. Sensor Letters,2012,10:415-424.
[2]郭文娟,张佳华.利用ASTER遥感资料提取南京城郊土地利用信息的研究[J].农业工程学报,2005,21(9):62-66.Guo W J,Zhang J H. Extraction for land use classification information of suburb of Nanjing City using ASTER image[J]. Transactions of the Chinese Society of Agricultural Engineering,2005,21(9):62-66.
[3]张超,李智晓,李鹏山,等.基于高分辨率遥感影像分类的城镇土地利用规划监测[J].农业机械学报,2015,46(11):323-329.Zhang C,Li Z X,Li P S,et al. Urban-rural land use plan monitoring based on high spatial resolution remote sensing imagery classification[J]. Transactions of the Chinese Society for Agricultural Machinery,2015,46(11):323-329.
[4]陈凯,罗兆楠,王小松,等.特高压工程施工利用高分二号遥感影像进行水土保持远程监测研究[J].地理信息世界,2016,23(3):108-113.Chen K,Luo Z N,Wang X S,et al. The research on remote monitoring of soil and water conservation using Gaofen-2(GF-2)satellite in UHV engineering construction[J]. Geomatics World,2016,23(3):108-113.
[5]吴洪涛,刘晶东.几种基于高分辨率遥感影像分类技术的分析与探讨[J].测绘与空间地理信息,2008,31(2):47-51.Wu H T,Liu J D. Analysis and discussion on classification techniques based on high resolution remote sensing image[J]. Geomatics and Spatial Information Technology,2008,31(2):47-51.
[6]陈启浩,刘志敏,刘修国,等.面向基元的高空间分辨率矿区遥感影像土地利用分类[J].地球科学(中国地质大学学报),2010,35(3):453-458.Chen Q H,Liu Z M,Liu X G,et al. Element-oriented land-use classification of mining area by high spatial resolution remote sensing image[J]. Earth Science(Journal of China University of Geosciences),2010,35(3):453-458.
[7]王岩,王晓青,窦爱霞.面向对象遥感分类方法在汶川地震震害提取中的应用[J].地震,2009,29(3):54-60.Wang Y,Wang X Q,Dou A X. Building damage detection of the2008 Wenchuan,China earthquake based on object-oriented classification method[J]. Earthquake,2009,29(3):54-60.
[8] Hussain E,Shan J. Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multi-temporal data[J]. GIScience and Remote Sensing,2016,53(2):164-182.
[9] Liang J,Yang J Y,Zhang C,et al. A comparison of two object-oriented methods for land-use/cover change detection with SPOT5imagery[J]. Sensor Letters,2012,10:415-424.