冬春卫星影像在废弃地白桦林覆盖度评价中的应用

N. Fedorov, I. Tuktamyshev, P. Shirokikh, V. Martynenko, L. Naumova
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引用次数: 0

摘要

苏联解体后,超过4000万公顷的农业用地被废弃。土地的很大一部分被自发再生的木质和灌木植被所覆盖。在识别森林更新时,准确地识别出树木盖度大于50%的林分。夏季卫星影像难以识别撂荒农用地森林更新的初始阶段,一方面幼树和幼树高度低、密度低,另一方面草本植被差异不大。本研究的目的是利用冬季和早春卫星图像来评估在废弃农用地上形成的白桦为主的林分(Betula penp -dula Roth.)的树木覆盖情况(见图1)。我们在巴什科尔托斯坦共和国阔叶林带的废弃农用地上使用了189层白桦林。利用2020年11月4日至2021年5月13日7幅Sentinel-2无云影像的RED、NIR、SWIR11、SWIR12波段光谱反射率值以及NDFSI雪指数值(见图2、3)对森林覆盖评价进行回归分析。在选择最优回归模型时,使用相关系数(R)和决定系数(R2)的值来评估模型质量。为了验证利用所获得的模型评估林分演替早期树木覆盖度的可能性,我们在2013年7月对36个地学高程的林分树冠密度进行了目测评估。然后,利用2014年3月25日拍摄的Landsat-8卫星图像,应用上述方法计算树木覆盖度。在建立回归模型计算树盖度时,使用积雪还很结实时期(3月中旬至4月上半月)的早春影像红色波段得到的结果最好(见表1)。树盖度与红色波段光谱反射率的相关系数为-0.90。该模型使我们能够准确地确定在巴什科尔托斯坦共和国阔叶林区普遍存在的18至20年桦林的树木覆盖。根据获得的其他日期的回归模型确定树木覆盖的模型的准确性不稳定,并且很可能受到雪深和红色和红外波段辐射强度变化的季节动态的影响(见表2,3)。总之,从现代卫星图像计算的方程可用于利用废弃野外恢复早期演替阶段的回顾性图像评估树木覆盖。在使用早春图像时,由于积雪融化日期每年变化很大,因此应考虑雪深。本文包含3张图,3张表,41篇参考文献。作者声明无利益冲突。
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Application of the Winter and Early-Spring Satellite Images for Assessment of the Birch Forest Coverage on the Abandoned Agricultural Lands
More than 40 million hectares of agricultural land were abandoned after the collapse of the Soviet Union. A significant part of the land is covered by spontaneously regenerating woody and shrubby vegetation. When identifying the forest regeneration, the stands with a tree cover of more than 50% are accurately identified. It is difficult to identify the initial stages of forest regeneration on the abandoned agricultural lands using summer satellite images because of little difference between the young trees and saplings due to their low height and low density on the one hand, and herbaceous vegetation on the other. The purpose of this work was to apply winter and early-spring satellite images for assessments of the tree cover of birch-dominated stands (Betula pen-dula Roth.) formed on the abandoned agricultural lands (See Fig. 1). We used 189 releves of birch forests on the abandoned agricultural lands in the broad-leaved forest zone of the Republic of Bashkortostan. A regression analysis of the evaluation of the tree cover was carried out using the values of the spectral reflectance of the RED, NIR, SWIR11, and SWIR12 bands, as well as the values of the NDFSI snow index from seven cloudless Sentinel-2 images taken between 04.11.2020 and 13.05.2021 (See Fig. 2, 3). When selecting optimal regression models, the values of correlation coefficients (R) and determination coefficients (R2) were used to assess the model quality. To test the possibility of using the obtained models for assessing the tree cover of the stand at earlier succession stages, we involved the data on the tree cover from 36 geobotanical releves, where the crown density of the stand was visually evaluated in July 2013. Then, the described procedure was applied to calculate the tree cover using the Landsat-8 image taken on 25.03.2014. When creating regression models to calculate the tree cover, the best results were obtained using the red band of early spring images during the period when snowpack is still solid (from mid-March to the first half of April) (See Table 1). The correlation between the tree cover and the spectral reflectance of the red band was -0.90. The model allowed us to determine accurately the tree cover of birch forests aged from 18 to 20 years which prevail in the zone of broad-leaved forests in the Republic of Bashkortostan. The accuracy of the model for determining the tree cover according to the obtained regression models for other dates is unstable and highly likely influenced by the snow depth and the seasonal dynamics of changes in the radiation intensity of the red and infrared bands (See Table 2, 3). To conclude, the equations calculated from modern satellite images can be used to assess the tree cover using retrospective images at earlier succession stages of the abandoned field recovery. When using early-spring images, the snow depth should be taken into account since the snowpack melting dates can vary greatly from year to year. The paper contains 3 Figures, 3 Tables, and 41 References. The Authors declare no conflict of interest.
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