基于标准地表光谱端元空间的苹果园种植时间制图方法

利用遥感数据进行果园种植时间制图可以高效便捷地获取大区域尺度的果园种植时间信息。为避免混合像元对果园的光谱信息造成影响，实现果园范围的精准识别及果园种植年龄的推测，该研究基于Landsat系列影像，开展融合地表标准地表光谱端元空间的果园范围提取和种植时间制图的研究。首先，将标准地表光谱端元空间融入原始影像中运用随机森林算法进行土地利用/覆被分类，重点提取果园分布范围。其次，构建地表植被端元年际间时间序列曲线，求得果园缓慢增长区间，运用四点法求取果园最大环境承载量。最后，回溯找到果园种植起点进行Logistic增长模型拟合，完成果园种植时间制图。研究结果表明：1）由线性光谱混合分解所得的地表光谱四端元能够很好地表达研究区地表组分信息。融合标准端元空间与随机森林算法提高了地物信息的提取精度。分类总体精度达到88.80%，Kappa系数达到0.86，对果园有较好的解译能力。2）通过植被端元年际时间序列曲线可以很容易地捕捉到土地覆被/利用类型的变化，通过Logistic增长模型可以检测植被生长的状况和最大环境承载量。拟合的果树生长模型具有较高的精度和稳定性，整体拟合度达到0.751，年龄验证误差均值为1.86a，说明该方法可以相对准确地确定苹果树的种植时间和长势。

Time-of-planting mapping method for apple orchards based on standard spectral endmembers spaces

Abstract: A highly efficient and convenient mapping can greatly contribute to access the plantation year in a large-scale orchard using remote sensing. Limited studies on the mapping orchard plantation age can be divided into two categories, namely: 1) using the spectral feature differences, and 2) using the vegetation phenology represented by remote sensing images. However, current studies cannot avoid the influence of mixed image elements on the spectral information of features. Alternatively, the linear hybrid decomposition model can be expected to effectively estimate the orchard plantation age in a large scale. The complex hybrid image can further be decomposed into different pure end elements for the physical information. This study aims to integrate the surface standard end element space using Landsat series images, in order to mapping the orchard plantation information. The following parts were included: 1) The area of apple orchard was firstly mapped to incorporate four standard end elements of substrate (SL, rock and soil), vegetation (GV, photosynthetic foliage), dark matter (DA, shadows), and water (WA, water bodies) into the original image, particularly with the random forest for the land use classification. 2) The Landsat8-OLI, Landsat7-ETM+, and Landsat5-TM sensor images were used to conduct the linear spectral mixture decomposition. Then, the time series curves of vegetation end element were constructed to determine the slow growth interval of apple orchard. The four-point method was applied to explore the maximum environmental carrying capacity of apple orchard in the study area. 3) The starting point of apple orchard plantation was found to fit the logistic growth model for the subsequent mapping of the orchard plantation information. The main findings were as follows. 1) Four end elements from the linear spectral mixture decomposition were used to better represent the surface component information in the orchard. The accuracy of feature extraction was also effectively improved after the fusion of the standard end element space and the random forest. Specifically, the overall accuracy of classification mapping reached up to 88.80% than before, with the Kappa coefficient of 0.86. Besides, there was a better interpretation of the orchard, with the accuracy of 92% than before. 2) An excellent stability was obtained to relatively present the vegetation end element time series curves. Among them, three Landsat series sensor images were used to extract the feature information during operation. The variation of land cover/use was easily used to capture the vegetation end member time series curves. Thus, the Logistic growth model better performed on the biological processes of vegetation growth. The fruit tree growth model was also fitted for the higher accuracy and stability, particularly with the overall fit of 0.751, and the mean error of 1.86 years. The finding can provide a strong reference to determine the plantation information and plantation year of fruit trees with the higher accuracy than before.