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An approach to estimate forest LAI with high resolution based on prior knowledge of model parameters

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：201621170031@mail.bnu.edu.cn
  • Introduction：张静宇，1993年生，女，硕士研究生，研究方向为定量遥感。E-mail: 201621170031@mail.bnu.edu.cn
ZHANG Jingyu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China

    Beijing Engineering Research Center for Global Land Remote Sensing Products, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

  • Email：wangjd@bnu.edu.cn
  • Introduction：王锦地，1955年生，女，教授，研究方向为遥感机理和应用。E-mail: wangjd@bnu.edu.cn
WANG Jindi12*,  
  • Affiliation：

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

SHI Yuechan3

resumen

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.

palabra clave

remote sensing;prior knowledge;forest model parameters;LAI;high-resolution

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