青海湖环湖地区草地植被生物量遥感监测模型

doi:10.11821/xb200305007

地理学报 ›› 2003, Vol. 58 ›› Issue (5): 695-702.doi: 10.11821/xb200305007

青海湖环湖地区草地植被生物量遥感监测模型

牛志春¹, 倪绍祥²

1. 江苏省环境监测中心，南京 210036；
2. 南京师范大学地理科学学院，南京 210097

收稿日期:2002-11-18 修回日期:2003-04-28 出版日期:2003-09-25 发布日期:2003-09-25
作者简介:牛志春 (1978-), 女, 山西人, 硕士, 主要从事遥感与GIS及其应用研究。
基金资助:
国家自然科学基金资助项目(49971056)

Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing

NIU Zhichun¹, NI Shaoxiang²

ege of Geographical Sciences, Nanjing Normal University, Nanjing 210097, China

Received:2002-11-18 Revised:2003-04-28 Online:2003-09-25 Published:2003-09-25
Supported by:
National Natural Science Foundation of China, No.49971056

摘要/Abstract

摘要：

利用青海湖环湖地区2000年陆地卫星TM遥感图像数据和同期野外实测的34处样方产草量数据，分析了遥感植被指数与草地植被生物量之间的相关关系，进而分别建立了遥感植被指数与草地植被生物量的一元线性回归模型和非线性回归模型。研究表明，遥感植被指数与草地生物量之间存在较好的相关性，但不同遥感植被指数与草地植被生物量相关性程度存在一定差别。此外，所建遥感植被指数与草地植被生物量的非线性回归模型在拟合精度上优于一元线性回归模型，且由三次方程得到的非线性回归模型最适用于监测青海湖环湖地区的草地植被生物量。

关键词: 遥感, 植被指数, 草地, 生物量, 青海湖

Abstract:

Taking the region around the Qinghai Lake as the study area and using the Landsat Thematic Mapper data and the measured grass yield data, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between grassland biomass and the vegetation indices. There are two types of sampling site, i.e., the larger one is 30 m×30 m and the smaller one is 1 m×1 m. Each larger sampling site includes one smaller one which was randomly selected. The major conclusions from this study are: 1) the fitting accuracies of the non-linear regression models are much higher than those of the non-linear regression models, namely, the results obtained from the non-linear regression models are more accordant with the measured grassland biomass data in comparison with those from the monadic linear regression models; 2) the comparison of different forms of the non-linear regression analysis on the relations between the vegetation indices and the measured grassland biomass data indicates that the cubic equation is the best one in terms of the suitability of use in the study area; 3) the results from the non-linear regression analysis show that the order is RVI, NDVI, SAVI, MSAVI and DVI in terms of the fitting accuracy between these vegetation indices and grassland biomass data; and 4) the non-linear model Y = -18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around the Qinghai Lake.

Key words: remote sensing, vegetation index, grassland, biomass, Qinghai Lake

牛志春,倪绍祥. 青海湖环湖地区草地植被生物量遥感监测模型[J]. 地理学报, 2003, 58(5): 695-702.

NIU Zhichun, NI Shaoxiang. Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing[J]. Acta Geographica Sinica, 2003, 58(5): 695-702.

参考文献

[1] Liu Haiqi, Jin Minyu, Gong Weipeng. Applications of remote sensing in agriculture in the United States. Journal of China Agricultural Resources and Regional Planning, 1999, 20(2): 56-60.
[刘海启, 金敏毓, 龚维鹏. 美国农业遥感技术应用状况概述. 中国农业资源与区划, 1999, 20(2): 56-60.]

[2] Taylor B F et al. Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA 7 data. Remote Sensing of Environment, 1985, 18: 177-192.

[3] Li Jianlong, Jiang Ping, Liang Tiangang. The developing processes, contents and prospects of grassland remote sensing science in China. Grassland of China, 1998, (3): 53-56.
[李建龙, 蒋平, 梁天刚. 我国草地遥感科学发展的轨迹、内涵及展望. 中国草地, 1998, (3): 53-56.]

[4] Huang Jingfeng, Wang Xiuzhen, Hu Xinbo. Studies on grass yield monitoring for different natural grassland types using remote sensing data in northern Xinjiang. Grassland of China, 1999, (1): 7-11, 18.
[黄敬峰, 王秀珍, 胡新博. 新疆北部不同类型天然草地产草量遥感监测模型. 中国草地, 1999, (1): 7-11, 18.]

[5] Huang Jingfeng, Wang Xiuzhen, Wang Renchao et al. Relation analysis between the production of natural grassland and satellite vegetation indices. Research of Agricultural Modernization, 2000, 21(1): 33-36.
[黄敬峰, 王秀珍, 王人潮等. 天然草地牧草产量与气象卫星植被指数的相关分析. 农业现代化研究, 2000, 21(1): 33-36.]

[6] Huang Jingfeng, Wang Xiuzhen, Wang Renchao et al. A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data. Journal of Remote Sensing, 2001, 5(1): 71-76.
[黄敬峰, 王秀珍, 王人潮等. 天然草地牧草产量遥感综合监测预测模型研究. 遥感学报, 2001, 5(1): 71-76.]

[7] Li Jianlong, Jiang Ping. The study on the remote sensing technology in estimating and forecasting grassland field applications. Journal of Wuhan Technical University of Surveying and Mapping, 1998, 23(2): 153-157.
[李建龙, 蒋平. 遥感技术在大面积天然草地估产和预报中的应用探讨. 武汉测绘科技大学学报, 1998, 23(2): 153-157.]

[8] Zhang Jianhua. Imitation methods of remote sensing. Journal of Arid Land Resources and Environment, 2000, 14(2): 82-86.
[张建华. 作物估产的遥感——数值模拟方法. 干旱区资源与环境, 2000, 14(2): 82-86.]

[9] Lanzhou Institute of Geology of CAS, Institute of Hydrobiology of CAS, Institute of Microbiology of CAS, Nanjing Institute of Geology and Paleontology of CAS. The Report of Integrated Survey of Qinghai Lake. Beijing: Science Press, 1979.
[中国科学院兰州地质研究所, 中国科学院水生物所, 中国科学院微生物所, 中国科学院南京地质古生物所合编. 青海湖综合考察报告. 北京: 科学出版社, 1979.]

[10] Jensen J R. Remote Sensing of the Environment: An Earth Resource Perspective. New Jersey: Prentice Hall, 2000, 361-365.

[11] Tian Qingjiu, Min Xiangjun. Advances in study on vegetation indices. Advance in Earth Sciences, 1998, 13(4): 327-333.
[田庆久, 闵祥军. 植被指数研究进展. 地球科学进展, 1998, 13(4): 327-333.]

[12] Zhang Hongliang, Ni Shaoxiang, Jiang Jianjun et al. An analysis of natural grassland TM images vegetation index in the region around Qinghai Lake. Grassland of China, 2002, 24(2): 6-11.
[张洪亮, 倪绍祥, 蒋建军等. 环青海湖地区天然草地TM影像植被指数分析. 中国草地, 2002, 24(2): 6-11.]

[13] Wen Jun, Wang Jiemin. A modified soil-adjusted vegetation index obtained from satellite remote sensing data. Climatic and Environmental Research, 1997, 2(3): 302-309.
[文军, 王介民. 一种由卫星遥感资料获得的修正的土壤调整植被指数. 气候与环境研究, 1997, 2(3): 302-309.]

[14] Li Rendong, Liu Jiyuan. An estimation of wetland vegetation biomass in the Poyang Lake using Landsat ETM data. Acta Geographica Sinica, 2001, 56(5): 532-540.
[李仁东, 刘纪远. 应用Landsat ETM 数据估算鄱阳湖湿生植被生物量. 地理学报, 2001, 56(5): 532-540.]

[15] Huete A F, H Q Liu. An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the Normalized Difference Vegetation Index for the MODES-EOS. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4): 897-905.

青海湖环湖地区草地植被生物量遥感监测模型

Study on Models for Monitoring of Grassland Biomass around Qinghai Lake Assisted by Remote Sensing

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

[1]	汪晓帆, 戴尔阜, 郑度, 吴卓. 南方红壤丘陵区采伐变量对森林面积和生物量影响模拟[J]. 地理学报, 2021, 76(1): 223-234.
[2]	徐晨晨, 叶虎平, 岳焕印, 谭翔, 廖小罕. 城镇化区域无人机低空航路网迭代构建的理论体系与技术路径[J]. 地理学报, 2020, 75(5): 917-930.
[3]	王晓茹, 唐志光, 王建, 王欣, 魏俊锋. 基于MODIS积雪产品的高亚洲融雪末期雪线高度遥感监测[J]. 地理学报, 2020, 75(3): 470-484.
[4]	姚永慧, 张俊瑶, 索南东主. 南北过渡带1∶5万植被类型图遥感制图案例研究[J]. 地理学报, 2020, 75(3): 620-630.
[5]	周成虎, 孙九林, 苏奋振, 杨晓梅, 裴韬, 葛咏, 杨雅萍, 张岸, 廖小罕, 陆锋, 高星, 付东杰. 地理信息科学发展与技术应用[J]. 地理学报, 2020, 75(12): 2593-2609.
[6]	余姝辰, 王伦澈, 夏卫平, 余德清, 李长安, 贺秋华. 清末以来洞庭湖区通江湖泊的时空演变[J]. 地理学报, 2020, 75(11): 2346-2361.
[7]	黄麟, 翟俊, 祝萍, 郑瑜晗. 中国草畜平衡状态时空演变指示的草地生态保护格局[J]. 地理学报, 2020, 75(11): 2396-2407.
[8]	赵贵宁, 张正勇, 刘琳, 徐丽萍, 王璞玉, 李丽, 宁珊. 基于多源遥感数据的玛纳斯河流域冰川物质平衡变化[J]. 地理学报, 2020, 75(1): 98-112.
[9]	刘文超, 刘纪远, 匡文慧. 陕北地区退耕还林还草工程土壤保护效应的时空特征[J]. 地理学报, 2019, 74(9): 1835-1852.
[10]	隆院男,闫世雄,蒋昌波,吴长山,李志威,唐蓉. 基于多源遥感影像的洞庭湖地形提取方法[J]. 地理学报, 2019, 74(7): 1467-1481.
[11]	杨成德, 王欣, 魏俊峰, 刘琼欢, 鲁安新, 张勇, 唐志光. 基于3S技术方法的中国冰湖编目[J]. 地理学报, 2019, 74(3): 544-556.
[12]	唐见,曹慧群,陈进. 生态保护工程和气候变化对长江源区植被变化的影响量化[J]. 地理学报, 2019, 74(1): 76-86.
[13]	范科科,张强,史培军,孙鹏,余慧倩. 基于卫星遥感和再分析数据的青藏高原土壤湿度数据评估[J]. 地理学报, 2018, 73(9): 1778-1791.
[14]	陈玮彤,张东,崔丹丹,吕林,谢伟军,施顺杰,侯泽宇. 基于遥感的江苏省大陆岸线岸滩时空演变[J]. 地理学报, 2018, 73(7): 1365-1380.
[15]	刘纪远,宁佳,匡文慧,徐新良,张树文,颜长珍,李仁东,吴世新,胡云锋,杜国明,迟文峰,潘涛,宁静. 2010-2015年中国土地利用变化的时空格局与新特征[J]. 地理学报, 2018, 73(5): 789-802.