Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data

doi:10.11733/j.issn.1007-0435.2015.01.005

Acta Agrestia Sinica ›› 2015, Vol. 23 ›› Issue (1): 27-33.DOI: 10.11733/j.issn.1007-0435.2015.01.005

Previous Articles Next Articles

Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data

ZHOU Xin, SHENG Jian-dong, ZHANG Wen-tai, WU Hong-qi, WANG Xin-jun

Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China

Received:2014-03-17 Revised:2014-12-02 Online:2015-02-15 Published:2015-01-31

基于MODIS数据的伊犁地区草地地上生物量反演

周鑫, 盛建东, 张文太, 武红旗, 王新军

新疆农业大学草业与环境科学学院新疆土壤与植物生态过程重点实验室, 新疆乌鲁木齐 830052

通讯作者: 盛建东
作者简介:周鑫(1988-),男,湖北荆州人,硕士研究生,研究方向为草地遥感应用,E-mail:zhouxin5.5@163.com
基金资助:
中科院战略性先导科技专项"生态系统固碳现状、速率、机制与潜力"项目;第四课题"草地生态系统固碳现状、速率、机制与潜力"(XDA05050400)资助

Abstract

Abstract:

The basic data involved the observed data of aboveground biomass from July to August in 2012 in Ili as well as the MODIS data during this period, and the purpose of this study was to protect and utilize grassland resources reasonably. The correlation relationships among enhanced vegetation index (EVI), normalized difference vegetation index (NDVI) and observed data of aboveground biomass were analyzed by linear regression model, quadratic polynomial regression model and index regression model. The spatial distribution of aboveground biomass in the grassland of Ili was simulated and analyzed. The results showed that a significant correlation relationship existed between vegetation index and aboveground biomass, while the quadratic polynomial regression model based on EVI(y = 14759x² - 4758x + 1346.2, R² = 0.8402) was the most suitable one, and estimation accuracy was 92.19%. The dry weight per unit area in the grassland of Ili was 1817 kg·hm^-2, and the total yield was 70.59×10⁸ kg in 2012. Along with the increasing elevation, aboveground biomass increased first and then decreased.

Key words: MODIS, Aboveground biomass, Inversion model, Vegetation index

摘要：

为伊犁草地资源监测、保护及合理利用提供参考依据,利用新疆伊犁地区2012年7—8月野外草地地上生物量采样数据和同期的MODIS数据,分析了增强型植被指数(EVI)、归一化植被指数(NDVI)与实测草地地上生物量的一元线性、指数和二次多项式回归模型,并对各种回归模型进行分析比较.利用优选模型的反演结果分析了伊犁地区地上生物量的空间分布.结果表明:各植被指数都与实测生物量有较好的相关性,但以EVI指数建立的二次多项式回归模型(y = 14759x²-4758x + 1346,R²= 0.8402)较优,拟合模型平均估产精度达到 92.19%,可作为该区域草地地上生物量遥感反演模型;伊犁地区2012年平均产草量为1817 kg·hm^-2,总产草量达70.59×10⁸ kg,并且产草量随高程增加呈现先增加后减少的特征.

关键词: MODIS, 地上生物量, 反演模型, 植被指数

CLC Number:

S812

ZHOU Xin, SHENG Jian-dong, ZHANG Wen-tai, WU Hong-qi, WANG Xin-jun. Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data[J]. Acta Agrestia Sinica, 2015, 23(1): 27-33.

周鑫, 盛建东, 张文太, 武红旗, 王新军. 基于MODIS数据的伊犁地区草地地上生物量反演[J]. 草地学报, 2015, 23(1): 27-33.

0
/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://manu40.magtech.com.cn/Jweb_cdxb/EN/10.11733/j.issn.1007-0435.2015.01.005

https://manu40.magtech.com.cn/Jweb_cdxb/EN/Y2015/V23/I1/27

References

[1] 刘海启. 美国农业遥感技术应用现状简介[J]. 国土资源遥感,1997,32(3):58-62
[2] 查勇, Jay G,倪绍祥. 国际草地资源遥感研究新进展[J]. 地理科学进展,2003,22(6):607-617
[3] 童庆禧,丁志,郑兰芬,等. 应用NOAA气象卫星图象资料估算草场生物量方法的初步研究[J]. 自然资源学报,1986,1(1):87-95
[4] 史培军,李博,李忠厚,等. 大面积草地遥感估产技术研究:以内蒙古锡林郭勒草原估产为例[J]. 草地学报,1994,2(1):9-13
[5] 李京,陈晋,袁清. 应用NOAA/AVHRR遥感资料对大面积草场进行产草量定量估算的方法研究[J]. 自然资源学报,1994,9(4):365-368,370-374
[6] 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
[7] 张连义,张静祥,赛音吉亚,等. 典型草原植被生物量遥感监测模型:以锡林郭勒盟为例[J]. 草业科学,2008,25(4):31-36
[8] Anderson G L,Hanson J D,Haas R H. Evaluating Landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands [J]. Remote Sensing of Environment,1993,2(45):165-175
[9] 梁天刚,崔霞,冯琦胜,等. 2001-2008年甘南牧区草地地上生物量与载畜量遥感动态监测[J]. 草业学报,2009,18(6):12-22
[10] Kawamura K, Akiyama T, Yokota H, et al. Quantifying grazing intensities using geographic information systems and satellite remote sensing in the Xilingol steppe region, Inner Mongolia, China [J]. Agriculture, Ecosystems & Environment,2005,107(1):83-93
[11] 赵冰茹,刘闯,刘爱军,等. 利用MODIS-NDVI进行草地估产研究:以内蒙古锡林郭勒草地为例[J]. 草业科学,2004,21(8):12-15
[12] 李聪,肖继东,曹占洲,等. 应用MODIS数据估算草地生物量[J]. 干旱区研究,2007,24(3):386-391
[13] 除多,德吉央宗,姬秋梅,等. 西藏高原典型草地地上生物量遥感估算[J]. 国土资源遥感,2013,25(3):43-50
[14] 李霞. 北疆地区草地地上生物量遥感监测研究[D]. 兰州:兰州大学,2008,28-40
[15] 孙慧兰,李卫红,陈亚鹏,等. 新疆伊犁河流域生态服务价值对土地利用变化的响应[J]. 生态学报,2010,30(4):887-894
[16] 徐远杰,陈亚宁,李卫红,等. 伊犁河谷山地植物群落物种多样性分布格局及环境解释[J]. 植物生态学报,2010,34(10):1142-1154
[17] 王正兴,刘闯,陈文波,等. MODIS增强型植被指数EVI与NDVI初步比较[J]. 武汉大学学报,2006,31(5):407-410
[18] Paruelo J M, Epstein H E, Lauenroth W K, et al. ANPP estimates from NDVI for the central grassland region of the United States [J]. Ecology,1997,78(3):953-958
[19] 马文红,杨元合,贺金生,等. 内蒙古温带草地生物量及其与环境因子的关系[J]. 中国科学:生命科学,2008,38(1):84-92
[20] Ni J. Forage yield-based carbon storage in grasslands of China [J]. Climatic Change,2004,67(2):237-246
[21] 金云翔,徐斌,杨秀春,等. 内蒙古锡林郭勒盟草原产草量动态遥感估算[J]. 中国科学:生命科学,2011, 41(12):1185-1195
[22] 王正兴,刘闯,赵冰茹,等. 利用MODIS增强型植被指数反演草地地上生物量[J]. 兰州大学学报,2005,25(2):10-16
[23] 王新欣,朱进忠,范燕敏,等. 利用EOS/MODIS植被指数建立草地估产模型的研究[J]. 新疆农业科学,2008,45(5):843-846
[24] 辛晓平,张保辉,李刚,等. 1982-2003年中国草地生物量时空格局变化研究[J]. 自然资源学报,2009, 24(9):1582-1592
[25] 崔霞,梁天刚,刘勇. 基于MOD09GA产品的草地生物量遥感估算模型[J]. 兰州大学学报:自然科学版,2009,45(5):79-87
[26] 马文红,方精云,杨元合,等. 中国北方草地生物量动态及其与气候因子的关系[J]. 中国科学:生命科学,2010,40(7):632-641

Aboveground Biomass Inversion of Grassland in Ili Area Using MODIS Data

基于MODIS数据的伊犁地区草地地上生物量反演

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	HUANG Jia-xing, WU Jing, LI Chun-bin, QIN Ge-xia, QIAN Juan-bing, LI Huai-hai. Remote Sensing Retrieval of grassland Above-Ground Biomass in Tianzhu County based on Sentinel-2 and Landsat 8 Data [J]. Acta Agrestia Sinica, 2021, 29(9): 2023-2030.
[2]	XIAO Yun-fei, CHEN Wen-ye, WANG Bin-jie, TAN Yan-rong, BING Dan-hui, ZHU Li, LIU Hong-yuan. Study On Temporal and Spatial Change of Land Use and Its Relationship with Climate Factors in Qilian Mountain National Nature Reserve [J]. Acta Agrestia Sinica, 2021, 29(9): 2049-2057.
[3]	GAO Hong-yuan, HOU Meng-jing, GE Jing, BAO Xu-ying, LI Yuan-chun, LIU Jie, FENG Qi-sheng, LIANG Tian-gang, HE Jin-sheng, QIAN Da-wen. Hyperspectral Estimation of Aboveground Biomass of Alpine Grassland based on Random Forest Algorithm [J]. Acta Agrestia Sinica, 2021, 29(8): 1757-1768.
[4]	SUN Yu, ZHANG Feng, ZHENG Jia-hua, ZHAO Tian-qi, ZHAO Meng-li, ZHANG Bin. Effects of Mowing Stubble Height on Species Diversity and Aboveground Biomass of Stipa grandis Steppe [J]. Acta Agrestia Sinica, 2021, 29(8): 1859-1864.
[5]	LUO Yang, CHEN Zhi-fei, ZHOU Jun-jie, JIAN Chun-xia, LAI Shuai-bin, CHEN Yang, WANG Shao-yan, JIN Yuan, XU Bing-cheng. Spectral Characteristics of Bothriochloa ischaemum Community in Response to Nitrogen and Phosphorus Addition at Loess Hilly-gully Region [J]. Acta Agrestia Sinica, 2021, 29(6): 1158-1165.
[6]	GU Xi-ling, GUO En-liang, YIN Shan, WANG Yong-fang, NA Ren-mandula, WAN Zhi-qiang. Assessment of the Cumulative and Lagging Effects of Drought on Vegetation Growth in Inner Mongolia [J]. Acta Agrestia Sinica, 2021, 29(6): 1301-1310.
[7]	DING Cheng-xiang, LIU Yu. Effects of Solar Photovoltaic Park Construction on Soil Microbial Community in Alpine Desert of Qinghai Tibet Plateau [J]. Acta Agrestia Sinica, 2021, 29(5): 1061-1069.
[8]	GUO Yan-hong, PU Xiao-jian, PU Xiao-peng, DU Wen-hua. Change Tendency of the Aboveground Biomass and Nutrition Quality in the Alpine Pastoral Area of Qinghai Tibet [J]. Acta Agrestia Sinica, 2021, 29(4): 734-742.
[9]	WANG Rui-feng, YAN Wei, SHI Feng-ling. Effects of Rhizobium,Nitrogen Application Rate and Frequency on Alfalfa Growth and Fiber Content [J]. Acta Agrestia Sinica, 2021, 29(10): 2372-2380.
[10]	WANG Xiu-mei, DONG Jian-jun. Estimation of the Aboveground Biomass of Desert Steppe and Typical Steppe in Inner Mongolia Using Generalized Linear Model [J]. Acta Agrestia Sinica, 2020, 28(6): 1711-1718.
[11]	ZHANG Jun, LIU Ju-hong, CAO Na, ZHANG Cai-qin. Time-lag Response of Aboveground Biomass Dynamic of Leymus chinensis in the Growing Season on Precipitation in Inner Mongolian Typical Grassland [J]. Acta Agrestia Sinica, 2020, 28(6): 1719-1725.
[12]	YU Lu, WANG Xun, CHAI Sha-tuo, LIU Shu-jie. Application of BP Neural Network in Natural Grassland Yield Estimation in the Area Around Qinghai Lake [J]. Acta Agrestia Sinica, 2020, 28(5): 1427-1435.
[13]	YU Lu, WANG Xun, CHAI Sha-tuo, LIU Shu-jie. Seasonal Dynamic Inversion of Natural Grassland Forage Content Based on High-resolution Satellite Remote Sensing [J]. Acta Agrestia Sinica, 2020, 28(2): 547-557.
[14]	DING Cheng-xiang, YANG Xiao-xia, Dong Quan-min. Effects of Grazing Patterns on Vegetation,Soil and Microbial Community in Alpine Grassland of Qinghai-Tibetan Plateau [J]. Acta Agrestia Sinica, 2020, 28(1): 159-169.
[15]	GUO Yan-yun, LIU Yan, LI Qiu-yue, CHEN Chen, WANG Sen. Validation and Adaptability Evaluation of Grassland Ecosystem Model CENTURY in the Tianshan Mountain Area [J]. Acta Agrestia Sinica, 2020, 28(1): 252-258.