基于GF-1 WFV与MODIS时空融合的南方森林植被类型识别

doi:10.13287/j.1001-9332.202207.022

摘要/Abstract

摘要： 我国南方地区地形条件较为复杂且多云雨,较难获取长时间序列高空间分辨率遥感影像,而时空融合技术能够同步获取高时空分辨率遥感数据,对提取其森林植被类型信息具有重要意义。以江西省兴国县为研究区,基于增强型时空自适应反射率融合模型(ESTARFM)分别将高空间分辨率的Landsat8 OLI、GF-1 WFV影像与高时间分辨率的MODIS09 A1影像进行融合,重构增强型植被指数(EVI)8 d步长的ESTARFM_Landsat8 EVI与ESTARFM_GF-1 EVI时序数据,获取物候(PH)特征信息,并利用随机森林分类模型识别森林植被类型。结果表明: 融合生成的ESTARFM_Landsat8 EVI、ESTARFM_GF-1 EVI与真实影像的相关系数均大于0.7,且在空间分布上具有较好的一致性,可用于补充缺失的高空间分辨率数据。不同组合方式的随机森林分类提取精度为EVI+PH>EVI>PH,且GF-1融合数据分类精度高于Landsat8融合数据。筛选出43个变量作为优选特征变量进行分类,总体精度与Kappa系数分别为95.6%和94.9%,其中,包括37个时序EVI值和6个物候特征信息,其时序EVI数据对森林植被类型识别的贡献程度更高,物候特征信息有利于分类精度的提高。ESTARFM融合算法适用于GF-1与MODIS数据,在一定程度上可解决长时间序列高空间分辨率影像不足的问题;GF-1时序融合影像在多云雨、地形条件复杂的南方地区森林植被类型识别中具有较高精度。

关键词: 森林植被类型, 时序数据, 时空融合模型, 物候

Abstract: It is difficult to obtain long time series of high spatial resolution remote sensing images in southern China because of the complex terrain and frequent cloudy and rainy weather. In contrast, the spatio-temporal fusion can sychonorously obtain remote sensing data with high spatial-temporal resolution, which is beneficial to extract forest vegetation type information. With Xingguo County of Jiangxi Province as the study area, we fused the Landsat8 OLI and GF-1 WFV images with high spatial resolution with high temporal resolution of MODIS09 A1 image on the basis of enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), reconstructed the time series data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI with 8 d step of enhanced vegetation index (EVI), obtained the phenology (PH) characteristics, and identified the forest vegetation types by using random forest classification model. The results showed that the correlation coefficients between the fusion data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI and the real images were all greater than 0.7, and had good consistency in spatial distribution, which could be used to supplement the missing data with high spatial resolution. The extraction accuracy of random forest classification with different combination modes was EVI+PH>EVI>PH and the classification accuracy of fusion data GF-1 was higher than that of Landsat8. A total of 43 variables were selected as the optimal feature variables for classification. The overall accuracy and Kappa coefficient were 95.6% and 94.9%, respectively, including 37 sequential EVI values and 6 phenological feature information. The sequential EVI data contributed more to the identification of forest vegetation types, while the phenological feature information was beneficial to improve the classification accuracy. The ESTARFM fusion algorithm was suitable for GF-1 and MODIS data, which could solve the problem of insufficient long-term sequence of high spatial resolution images. The GF-1 temporal fusion images had high accuracy in the identification of forest vegetation types in southern China under complex terrain and frequent cloudy and rainy weather.

Key words: forest vegetation type, time series data, spatio-temporal fusion model, phenology

徐丽, 欧阳勋志, 潘萍, 臧颢, 刘军, 杨凯. 基于GF-1 WFV与MODIS时空融合的南方森林植被类型识别[J]. 应用生态学报, 2022, 33(7): 1948-1956.

XU Li, OUYANG Xun-zhi, PAN Ping, ZANG Hao, LIU Jun, YANG Kai. Identification of forest vegetation types in southern China based on spatio-temporal fusion of GF-1 WFV and MODIS data[J]. Chinese Journal of Applied Ecology, 2022, 33(7): 1948-1956.

参考文献

[1] Magnussen S, Nord-Larsen T, Riis-Nielsen T. Lidar supported estimators of wood volume and aboveground biomass from the Danish national forest inventory (2012-2016). Remote Sensing of Environment, 2018, 211: 146-153
[2] 马婷, 李崇贵, 郭瑞霞, 等. 运用植被指数时序特征对落叶松人工林分类. 东北林业大学学报, 2020, 48(3): 56-59
[3] Betbeder J, Rapinel S, Corgne S, et al. TerraSAR-X dual-pol time-series for mapping of wetland vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 107: 90-98
[4] 温仲明, 焦峰, 焦菊英. 黄土丘陵区延河流域潜在植被分布预测与制图. 应用生态学报, 2008, 19(9): 1897-1904
[5] Zhu Y, Feng Z, Lu J, et al. Estimation of forest biomass in Beijing (China) using multisource remote sen-sing and forest inventory data. Forests, 2020, 11: 163
[6] 张沁雨, 王海宾, 彭道黎, 等. 基于基准样地法和国产高分数据的湖南省森林植被碳储量估测. 应用生态学报, 2019, 30(10): 3385-3394
[7] 贾明明, 任春颖, 刘殿伟, 等. 基于环境星与MODIS时序数据的面向对象森林植被分类. 生态学报, 2014, 34(24): 7167-7174
[8] Zhu X, Chen J, Gao F, et al. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 2010, 114: 2610-2623
[9] 高书鹏, 史正涛, 刘晓龙, 等. 基于高时空分辨率可见光遥感数据的热带山地橡胶林识别. 遥感技术与应用, 2018, 33(6): 1122-1131
[10] Dong T, Liu J, Qian BD, et al. Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 2016, 49: 63-74
[11] Hu RM, Wang S, Guo J, et al. Vegetation coverage and impervious surface area estimated based on the ESTRAFM model and remote sensing monitoring. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2018, 4: 113-119
[12] Deng ZW, Zhu X, He QY, et al. Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area. Advances in Space Research, 2019, 63: 2144-2154
[13] Wu MQ, Huang WJ, Niu Z, et al. Combining HJ CCD, GF-1 WFV and MODIS data to generate daily high spatial resolution synthetic data for environmental process monitoring. International Journal of Environmental Research and Public Health, 2015, 12: 9920-9937
[14] 王建勋, 华丽, 邓世超, 等. 基于GF_1与MODIS时空融合的南方丘陵区水稻提取研究. 中国农业资源与区划, 2019, 40(5): 37-46
[15] Fisher JRB, Acosta EA, Dennedy-Frank PJ, et al. Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 2018, 4: 137-149
[16] Yang XM, Yang TB, Ji Q, et al. Regional-scale grassland classification using moderate-resolution imaging spectrometer datasets based on multistep unsupervised classification and indices suitability analysis. Journal of Applied Remote Sensing, 2014, 8: 083548
[17] 张秀敏, 盛煜, 南卓铜, 等. 基于决策树方法的青藏高原温泉区域高寒草地植被分类研究. 草业科学, 2011, 28(12): 2074-2083
[18] 国家林业局. 国家森林资源连续清查技术规定(2014). 北京: 国家林业局, 2014
[19] 江西省森林资源与环境监测中心. 江西省森林资源二类调查技术规程(第七次调查). 南昌: 江西省森林资源与环境监测中心, 2019
[20] Savitzky A, Golay MJE. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 1964, 36: 1627-1639
[21] 宋春桥, 柯灵红, 游松财, 等. 基于TIMESAT的3种时序NDVI拟合方法比较研究——以藏北草地为例. 遥感技术与应用, 2011, 26(2): 147-155
[22] 夏传福, 李静, 柳钦火. 植被物候遥感监测研究进展. 遥感学报, 2013, 17(1): 1-16
[23] 柳文杰, 曾永年, 张猛. 融合时间序列环境卫星数据与物候特征的水稻种植区提取. 遥感学报, 2018, 22(3): 381-391
[24] Jonsson P, Eklundh L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience & Remote Sensing, 2002, 40: 1824-1832
[25] 赵亚杰, 黄进良, 王立辉, 等. 基于时空融合NDVI及物候特征的江汉平原水稻种植区提取研究. 长江流域资源与环境, 2020, 29(2): 424-433
[26] 赵琳琳, 张锐, 刘焱序, 等. GF1-WFV与Landsat8-OLI对植被信息的提取差异研究. 生态学报, 2020, 40(10): 3495-3506
[27] 徐磊, 巫兆聪, 罗飞, 等. 基于GF-1/WFV与MODIS时空融合的森林覆盖定量提取. 农业机械学报, 2017, 48(7): 145-152
[28] 毕恺艺, 牛铮, 黄妮, 等. 基于Sentinel-2A时序数据和面向对象决策树方法的植被识别. 地理与地理信息科学, 2017, 33(5): 16-20
[29] Wu ZJ, Zhang JH, Deng F, et al. Fusion of GF and MODIS data for regional-scale grassland community classification with EVI2 time-series and phenological features. Remote Sensing, 2021, 13: 835
[30] Matsushita B, Yang W, Chen J, et al. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest. Sensors, 2007, 7: 2636-2651
[31] 李红军, 郑力, 雷玉平, 等. 基于EOS/MODIS数据的NDVI与EVI比较研究. 地理科学进展, 2007, 26(1): 26-32
[32] Shuai YM, Schaaf C, Zhang XY, et al. Daily MODIS 500 m reflectance anisotropy direct broadcast (DB) products for monitoring vegetation phenology dynamics. International Journal of Remote Sensing, 2013, 34: 5997-6016
[33] Peng DL, Wu CY, Li CJ, et al. Spring green-up phenology products derived from MODIS NDVI and EVI: Intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations. Ecological Indicators, 2017, 77: 323-336
[34] 侯学会, 牛铮, 高帅, 等. 基于SPOT-VGT NDVI时间序列的农牧交错带植被物候监测. 农业工程学报, 2013, 29(1): 142-150
[35] 康峻, 候学会, 牛铮, 等. 基于拟合物候参数的植被遥感决策树分类. 农业工程学报, 2014, 30(9): 148-156
[36] 何云, 黄翀, 李贺, 等. 基于Sentinel-2A影像特征优选的随机森林土地覆盖分类. 资源科学, 2019, 41(5): 992-1001
[37] 常翔宇, 柯长青. 基于随机森林算法的城市不透水面信息提取——以长春市为例. 测绘通报, 2020(11): 43-49
[38] 王怀警, 谭炳香, 王晓慧, 等. 多分类器组合森林类型精细分类. 遥感信息, 2019, 34(2): 104-112