| Citation: |
MENG Dunchao, ZHAO Jing, LAN Yubin, et al. Vegetation coverage extraction model of winter wheat based on pixel dichotomy[J]. Journal of South China Agricultural University, 2020, 41(3): 126-132. DOI: 10.7671/j.issn.1001-411X.201909055
|
To quickly and accurately extract vegetation coverage information of winter wheat in turning green period.
The field visible light images of winter wheat were obtained by UAV, and four common visible light vegetation indices were extracted. According to the principle of pixel dichotomy model, the vegetation coverage extraction models were established based on visible-band difference vegetation index(VDVI), excess green (EXG), normalized green-blue difference index (NGBDI) and normalized green-red difference index (NGRDI) respectively. The accuracies of four models were verified using the support vector machine (SVM) supervised classification results as the truth values.
The VDVI vegetation coverage extraction model had the highest accuracy and the best extraction effect to extract vegetation coverage among the four models. Compared with the supervised classification results, the extraction errors (EF) of four vegetation coverage extraction models were 3.36%, 15.68%, 8.74% and 15.46% respectively, the values of R2 were 0.946 1, 0.934 4, 0.695 3 and 0.746 0 respectively, and the values of root mean square error (RMSE) were 0.021 9, 0.059 5, 0.042 0 and 0.055 9 respectively.
The vegetation coverage extraction model based on visible vegetation index and pixel dichotomy has realized accurate and rapid extraction of vegetation coverage of winter wheat in turning green period, which provides a new way to extract vegetation coverage and a reference for UAV remote sensing monitoring vegetation coverage information.
| [1] |
赵广才, 常旭虹, 王德梅, 等. 中国小麦生产发展潜力研究报告[J]. 作物杂志, 2012(3): 1-5.
|
| [2] |
刘治开, 牛亚晓, 王毅, 等. 基于无人机可见光遥感的冬小麦株高估算[J]. 麦类作物学报, 2019, 39(7): 859-866.
|
| [3] |
PUREVDORJ T, ISHIYAMA T, HONDA Y, et al. Relationships between percent vegetation cover and vegetation indices[J]. Int J Remote Sens, 1998, 19(18): 3519-3535. doi: 10.1080/014311698213795
|
| [4] |
GITELSON A A, KAUFMAN Y J, STARK R, et al. Novel algorithms for remote estimation of vegetation fraction[J]. Remote Sens Environ, 2002, 80(1): 76-87. doi: 10.1016/S0034-4257(01)00289-9
|
| [5] |
牛亚晓, 张立元, 韩文霆, 等. 基于无人机遥感与植被指数的冬小麦覆盖度提取方法 [J]. 农业机械学报, 2018, 49(4): 212-221.
|
| [6] |
SONG W, MU X, RUAN G, et al. Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method[J]. Int J Appl Earth Obs, 2017, 58: 168-176. doi: 10.1016/j.jag.2017.01.015
|
| [7] |
RUNDQUIST B C. The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie[J]. Remote Sens Environ, 2002, 81(1): 129-135. doi: 10.1016/S0034-4257(01)00339-X
|
| [8] |
ZHOU Q, ROBSON M, PILESJO P. On the ground estimation of vegetation cover in Australian Rangelands[J]. Int J Remote Sens, 1998, 19(9): 1815-1820. doi: 10.1080/014311698215261
|
| [9] |
陈虹兵, 黄贝贝, 彭道黎. 基于混合像元分解的植被覆盖度模型比较研究[J]. 西北林学院学报, 2018, 33(3): 203-207. doi: 10.3969/j.issn.1001-7461.2018.03.31
|
| [10] |
佟斯琴, 包玉海, 张巧凤, 等. 基于像元二分法和强度分析方法的内蒙古植被覆盖度时空变化规律分析[J]. 生态环境学报, 2016, 25(5): 737-743.
|
| [11] |
李苗苗, 吴炳方, 颜长珍, 等. 密云水库上游植被覆盖度的遥感估算[J]. 资源科学, 2004, 26(4): 153-159. doi: 10.3321/j.issn:1007-7588.2004.04.022
|
| [12] |
何勇, 岑海燕, 李艺健, 等. 基于无人机遥感的农田信息获取技术及其机载装备[J]. 农业工程技术: 农产品加工, 2018, 38(9): 28-32.
|
| [13] |
汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报, 2015, 31(5): 152-159. doi: 10.3969/j.issn.1002-6819.2015.05.022
|
| [14] |
赵静, 杨焕波, 兰玉彬, 等. 基于无人机可见光图像的夏季玉米植被覆盖度提取方法[J]. 农业机械学报, 2019, 50(5): 232-240. doi: 10.6041/j.issn.1000-1298.2019.05.027
|
| [15] |
TORRES-SÁNCHEZ J, PEÑA J M, DE CASTRO A I, et al. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV[J]. Comput Electron Agr, 2014, 103: 104-113. doi: 10.1016/j.compag.2014.02.009
|
| [16] |
龙满生, 何东健. 玉米苗期杂草的计算机识别技术研究[J]. 农业工程学报, 2007, 23(7): 139-144. doi: 10.3321/j.issn:1002-6819.2007.07.027
|
| [17] |
HUNT E R, CAVIGELLII M, DAUGHTRY C S T, et al. Evaluation of digital photography from model aricraft for remote sensing of crop biomass and nitrogen status[J]. Precis Agric, 2005, 6(4): 359-378. doi: 10.1007/s11119-005-2324-5
|
| [18] |
MEYER G E, NETO J C. Verification of color vegetation indices of automated crop image application[J]. Comput Electron Agr, 2008, 63(2): 282-293. doi: 10.1016/j.compag.2008.03.009
|
| [19] |
陈晋, 陈云浩, 何春阳, 等. 基于土地覆盖分类的植被覆盖率估算亚像元模型与应用[J]. 遥感学报, 2001, 5(6): 416-422. doi: 10.11834/jrs.20010603
|
| [20] |
ZRIBI M, HÉGARAT-MASCLE S L, TACONET O, et al. Derivation of wild vegetation cover density in semi-arid regions: ERS2/SAR evaluation[J]. Int J Remote Sens, 2003, 24(6): 1335-1352. doi: 10.1080/01431160210146668
|
| [21] |
张军国, 韩欢庆, 胡春鹤, 等. 基于无人机多光谱图像的云南松虫害区域识别方法[J]. 农业机械学报, 2018, 49(5): 249-255. doi: 10.6041/j.issn.1000-1298.2018.05.029
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: