扫 描 看 全 文
张静宇, 王锦地, 石月婵. 基于森林模型参数先验知识估算高分辨率叶面积指数[J]. 遥感学报, 2020,24(11):1342-1352.
Jingyu ZHANG, Jindi WANG, Yuechan SHI. An approach to estimate forest LAI with high resolution based on prior knowledge of model parameters[J]. Journal of Remote Sensing(Chinese), 2020,24(11):1342-1352.
目前，估算高分辨率叶面积指数LAI（Leaf Area Index）的常用方法是采用大量地面测量数据和遥感数据建立统计模型，再用统计模型估算LAI。然而，与农田地面测量实验相比，森林地面测量实验获取的观测数据更加有限，这使得基于统计模型的森林高分辨率LAI的估算精度低，难以满足应用需求。为此，本文提出一种基于森林模型参数先验知识、使用森林研究区少量的LAI地面测量数据和归一化植被指数NDVI数据估算森林高分辨率LAI的方法。首先，获取全球20个森林实验区的LAI地面测量数据和NDVI数据，建立LAI-NDVI统计模型并提取森林模型参数的先验知识。然后，以一个新的森林站点Concepción作为研究区，将该研究区的数据分为建模数据和验证数据两个部分。使用研究区有限的建模数据对森林模型参数先验知识进行本地化校正得到优化模型，优化模型用于估算森林高分辨率LAI，使用验证数据评价LAI的估算精度。同时，选取了Camerons站点、Gnangara站点、Hirsikangas站点评价本文方法的LAI估算精度。使用地面测量LAI验证基于森林模型参数先验知识估算高分辨率LAI的结果精度，经验证4个森林站点的均方根误差分别为0.6680，0.4449，0.2863，0.5755。研究结果表明：在仅有少量观测数据时，采用本方法能有效地提高森林高分辨率LAI的估算精度。因此，本方法可为森林高分辨率LAI的遥感估算提供参考。
At present, the high-resolution Leaf Area Index (LAI) is usually estimated by statistical models, which is established by a large quantity of Vegetation Index (VI) data and ground LAI measurements. Compared with a cropland field campaign, the ground LAI measurements of the forest field campaign are less. The accuracy of the high-resolution LAI estimated by statistical models in the forest is low, and it is difficult to meet the application requirements. In this paper, a method was developed to estimate the high-resolution LAI in the forest based on the prior knowledge of modeling parameters for the forest, a small amount of forest ground LAI measurements and the Normalized Differential Vegetation Index (NDVI) data. First, for the power model which contains parameter ,a, and parameter ,b, the prior knowledge of modeling parameters in the forest was achieved. 20 forest sites with a large amount of ground LAI measurements were collected. LAI and NDVI data were obtained from the 20 forest sites respectively. The LAI-NDVI statistical model which is suitable for each forest site was established with the obtained LAI and NDVI data respectively too. The values of the parameter ,a, were extracted from the 20 statistical models, and the mean value and the standard deviation of the values were calculated to determine the prior distribution of the parameter ,a,. The mean value of the parameter ,a, was chosen as the prior initial value and two times of the standard deviation of parameter ,a, was chosen as the uncertainties of the prior initial value. The same method was used to extract the prior initial value and the uncertainties of the prior initial value for the parameter ,b,. So far, the prior knowledge of the modeling parameters for the forest was extracted. Second, the optimized LAI-NDVI statistical model was constructed for the study area. A new forest site, Concepción, was selected as the study area. The data of this site were divided into two parts: the modeling data and the validation data. The limited modeling data were used to adjust the prior initial value under the limitation of the uncertainties of the prior initial value and obtain an optimization model which is suitable for this new forest site by the optimization method SCE-UA. At last, the high-resolution forest LAIs were estimated and validated in the Concepción site. The high-resolution forest LAIs were estimated using the optimization model and the NDVIs in the validation data. The estimated high-resolution forest LAIs were evaluated by the ground LAI in the validation data. Moreover, the Camerons site, Gnangara site, and Hirsikangas site were selected as the study area to evaluate this method too. Compared the estimated high-resolution forest LAI with the ground LAI, the root mean square errors were 0.6680, 0.4449, 0.2863 and 0.5755 respectively. These results indicated that when only a small amount of ground LAI measurements is available, this method based on the forest prior knowledge could improve the accuracy of the high-resolution LAI estimation in the forest. Therefore, the method based on the forest prior knowledge of modeling parameters provided a reference for high-resolution forest LAI estimation.
遥感先验知识森林模型参数叶面积指数高分辨率
remote sensingprior knowledgeforest model parametersLAIhigh-resolution
Campbell J L, Burrows S, Gower S T and Cohen W B. 1999. BigFoot: Characterizing land cover, LAI, and NPP at the landscape scale for EOS/MODIS validation. Field manual,version 2.1[EB/OL]. Environmental Science Division Pub, Oak Ridge. [2018-10-10]. https://daac.ornl.gov/data/bigfoot_val/Field_Measurements/comp/https://daac.ornl.gov/data/bigfoot_val/Field_Measurements/comp/
Chen J M and Black T A. 1992. Defining leaf area index for non‐flat leaves. Plant, Cell and Environment, 15(4): 421-429 [DOI: 10.1111/j.1365-3040.1992.tb00992.xhttp://dx.doi.org/10.1111/j.1365-3040.1992.tb00992.x]
Chen J M and Cihlar J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment, 55(2): 153-162 [DOI: 10.1016/0034-4257(95)00195-6http://dx.doi.org/10.1016/0034-4257(95)00195-6]
Curnel Y, de Wit A J W, Duveiller G and Defourny P. 2011. Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS Experiment. Agricultural and Forest Meteorology, 151(12): 1843-1855 [DOI: 10.1016/j.agrformet.2011.08.002http://dx.doi.org/10.1016/j.agrformet.2011.08.002]
Curran P J, Dungan J L and Gholz H L. 1992. Seasonal LAI in slash pine estimated with Landsat TM. Remote Sensing of Environment, 39(1): 3-13 [DOI: 10.1016/0034-4257(92)90136-8http://dx.doi.org/10.1016/0034-4257(92)90136-8]
Du C Y. 2010. Study on Estimation of LAI Models Based on TM. Shenyang: Northeast Forestry University: 16-17
杜春雨. 2010. 基于TM影像的叶面积指数反演. 沈阳: 东北林业大学: 16-17
Fan R R, Wang N, Li X, Zhang S S, Chen C and Yu Y H. 2016. Establishment of leaf area index estimation model based on TM data of Chuzhou City. Journal of Anhui Agricultural Sciences, 44(21): 241-244
樊荣荣, 王妮, 李霞, 张洒洒, 陈财, 余俞寒. 2016. 基于滁州市TM数据的叶面积指数估算模型研建. 安徽农业科学, 44(21): 241-244 [DOI: 10.3969/j.issn.0517-6611.2016.21.077http://dx.doi.org/10.3969/j.issn.0517-6611.2016.21.077]
Garrigues S. 2003. Ground measurement acquisition report for the VALERI site[EB/OL]. [2018-10-09]. http://w3.avignon.inra.fr/valeri/amerique-du-sud/chili/2003/Campaign/Report.pdfhttp://w3.avignon.inra.fr/valeri/amerique-du-sud/chili/2003/Campaign/Report.pdf
He Y Q, Bo Y C, Chai L L, Liu X L and Li A H. 2016. Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method. International Journal of Applied Earth Observation and Geoinformation, 50: 26-38 [DOI: 10.1016/j.jag.2016.02.010http://dx.doi.org/10.1016/j.jag.2016.02.010]
Jin H A, Liu D W, Wang Z M, Song K S, Li F, Yang F, Du J and Li F X. 2008. Remote sensing estimation models of wetland vegetation LAI in Sanjiang Plain. Chinese Journal of Ecology, 27(5): 803-808
靳华安, 刘殿伟, 王宗明, 宋开山, 李方, 杨飞, 杜嘉, 李凤秀. 2008. 三江平原湿地植被叶面积指数遥感估算模型. 生态学杂志, 27(5): 803-808 [DOI: 10.13292/j.1000-4890.2008.0162http://dx.doi.org/10.13292/j.1000-4890.2008.0162]
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P, Weiss M and Baret F. 2004. Review of methods for in situ leaf area index determination: part I. theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1/2): 19-35 [DOI: 10.1016/j.agrformet.2003.08.027http://dx.doi.org/10.1016/j.agrformet.2003.08.027]
Li X W, Gao F, Wang J D and Strahler A. 2001. A priori knowledge accumulation and its application to linear BRDF model inversion. Journal of Geophysical Research: Atmospheres, 106(D11): 11925-11935 [DOI: 10.1029/2000JD900639http://dx.doi.org/10.1029/2000JD900639]
Li X W, Gao F, Wang J D and Zhu Q J. 1997. Uncertainty and sensitivity matrix of parameters in inversion of physical BRDF model. Journal of Remote Sensing, 1(1): 5-14
李小文, 高峰, 王锦地, 朱启疆. 1997. 遥感反演中参数的不确定性与敏感性矩阵. 遥感学报, 1(1): 5-14 [DOI: 10.11834/jrs.19970102http://dx.doi.org/10.11834/jrs.19970102]
Li X W, Wang J D, Hu B X and Strahler A H. 1998. On utilization of a priori knowledge in inversion of remote sensing models. Science in China Series D: Earth Sciences, 41(6): 580-585
李小文, 王锦地, 胡宝新, Strahler A H. 1998. 先验知识在遥感反演中的作用. 中国科学(D辑), 28(1): 67-72 [DOI: 10.1007/BF02878739http://dx.doi.org/10.1007/BF02878739]
Liang S H, Fang H L, Chen M Z, Shuey C J, Walthall C, Daughtry C, Morisette J, Schaaf C and Strahler A. 2002. Validating MODIS land surface reflectance and albedo products: methods and preliminary results. Remote Sensing of Environment, 83(1/2): 149-162 [DOI: 10.1016/S0034-4257(02)00092-5http://dx.doi.org/10.1016/S0034-4257(02)00092-5]
Morisette J T, Baret F, Privette J L, Myneni R B, Nickeson J E, Garrigues S, Shabanov N V, Weiss M, Fernandes R A, Leblanc S G, Kalacska M, Sanchez-Azofeifa G A, Chubey M, Rivard B, Stenberg P, Rautiainen M, Voipio P, Manninen T, Pilant A N, Lewis T E, Iiames J S, Colombo R, Meroni M, Busetto L, Cohen W B, Turner D P, Warner E D, Petersen G W, Seufert G and Cook R. 2006. Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup. IEEE Transactions on Geoscience and Remote Sensing, 44(7): 1804-1817 [DOI: 10.1109/TGRS.2006.872529http://dx.doi.org/10.1109/TGRS.2006.872529]
Qu Y H, Zhang Y Z and Xue H Z. 2014. Retrieval of 30-m-Resolution leaf area index from China HJ-1 CCD data and MODIS products through a dynamic Bayesian network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 222-228. [DOI: 10.1109/JSTARS.2013.2259472http://dx.doi.org/10.1109/JSTARS.2013.2259472]
Shen G F. 1989. Forestry Panorama. Beijing: China Forestry Publishing House: 48-132
沈国舫. 1989. 林学概论. 北京: 中国林业出版社: 48-132
Shi Y C, Wang J D, Wang J and Qu Y H. 2017. A prior knowledge-based method to derivate high-resolution leaf area index maps with limited field measurements. Remote Sensing, 9(1): 13 [DOI: 10.3390/rs9010013http://dx.doi.org/10.3390/rs9010013]
Tarantola A. 1987. Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation. New York: Elsevier
Turner D P, Cohen W B, Kennedy R E, Fassnacht K S and Briggs J M. 1999. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment, 70(1): 52-68 [DOI: 10.1016/S0034-4257(99)00057-7http://dx.doi.org/10.1016/S0034-4257(99)00057-7]
Weiss M, Baret F, Block T, Koetz B, Burini A, Scholze B, Lecharpentier P, Brockmann C, Fernandes R, Plummer S, Myneni R, Gobron N, Nightingale J, Schaepman-Strub G, Camacho F and Sanchez-Azofeifa A. 2014. On line validation exercise (OLIVE): a web based service for the validation of medium resolution land products. Application to FAPAR products. Remote Sensing, 6(5): 4190-4216 [DOI: 10.3390/rs6054190http://dx.doi.org/10.3390/rs6054190]
Xi J C, Zhang H Q and Zhang Z Q. 2004. Retrieving effective leaf area index of conifer forests using Landsat TM images. Journal of Beijing Forestry University, 26(6): 36-39
席建超, 张红旗, 张志强. 2004. 应用遥感数据反演针叶林有效叶面积指数. 北京林业大学学报, 26(6): 36-39 [DOI: 10.3321/j.issn:1000-1522.2004.06.007http://dx.doi.org/10.3321/j.issn:1000-1522.2004.06.007]
Xiao Z Q, Liang S L, Wang J D, Chen P, Yin X J, Zhang L Q and Song J L. 2014. Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 209-223 [DOI: 10.1109/TGRS.2013.22 37780http://dx.doi.org/10.1109/TGRS.2013.2237780]
Xu G H, Liu Q H, Chen L F and Liu L. 2016. Remote sensing for China’s sustainable development: opportunities and challenges. Journal of Remote Sensing, 20(5): 679-688
徐冠华, 柳钦火, 陈良富, 刘良. 2016. 遥感与中国可持续发展: 机遇和挑战. 遥感学报, 20(5): 679-688 [DOI: 10.11834/jrs.20166308http://dx.doi.org/10.11834/jrs.20166308]
Zhu G L, Ju W M, Chen J M, Fan W Y, Zhou Y L, Li X F and Li M Z. 2010. Forest canopy leaf area index in Maoershan mountain: ground measurement and remote sensing retrieval. Chinese Journal of Applied Ecology, 21(8): 2117-2124
朱高龙, 居为民, Chen J M, 范文义, 周艳莲, 李显风, 李明泽. 2010. 帽儿山地区森林冠层叶面积指数的地面观测与遥感反演. 应用生态学报, 21(8): 2117-2124 [DOI: 10.13287/j.1001-9332.2010.0305http://dx.doi.org/10.13287/j.1001-9332.2010.0305]
相关作者
相关机构
京公网安备11010802024621
