当前位置: X-MOL 学术ISPRS J. Photogramm. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Mapping dominant leaf type based on combined Sentinel-1/-2 data – Challenges for mountainous countries
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2021-08-27 , DOI: 10.1016/j.isprsjprs.2021.08.017
Lars T. Waser 1 , Marius Rüetschi 1 , Achilleas Psomas 1 , David Small 2 , Nataliia Rehush 3
Affiliation  

Countrywide winter and summer Sentinel-1 (S1) backscatter data, cloud-free summer Sentinel-2 (S2) images, an Airborne Laser Scanning (ALS)-based Digital Terrain Model (DTM) and a forest mask were used to model and subsequently map Dominant Leaf Type (DLT) with the thematic classes broadleaved and coniferous trees for the whole of Switzerland. A novel workflow was developed that is robust, cost-efficient and highly automated using reference data from aerial image interpretation. Two machine learning approaches based on Random Forest (RF) and deep learning (UNET) for the whole country with three sets of predictor variables were applied. 24 subareas based on aspect and slope categories were applied to explore effects of the complex mountainous topography on model performances. The reference data split into training, validation and test data sets was spatially stratified using a 25 km regular grid. Model accuracies of both RF and UNET were generally highest with Kappa (K) around 0.95 when predictors were included from both S1/S2 and the topographic variables aspect, elevation and slope from the DTM. While only slightly lower accuracies were obtained when using S2 and DTM data, lowest accuracies were obtained when only predictors from S1 and DTM were included, with RF performing worse than UNET. While on countrywide level RF and UNET performed overall similarly, substantial differences in model performances, i.e. higher variances and lower accuracies, were found in subareas with northwest to northeast orientations. The combined use of S1/S2 and DTM predictors mitigated these problems related to topography and shadows and was therefore superior to the single use of S1 and DTM or S2 and DTM data. The comparison with independent National Forest Inventory (NFI) plot data demonstrated precisions of K around 0.6 in the predictions of DLT and indicated a trend of increasing deviations in mixed forests. A comparison with the Copernicus High Resolution Layer (HRL) DLT 2018 revealed overall higher map accuracies with the exception of pure broadleaved forest. Although, spatial patterns of DTL were overall similar, UNET performed better than RF in areas with a distinct DLT on forest stand level, with the largest differences occurring when only S1 and DTM data was used. In contrast, predictions obtained from RF were more accurate in mixed stands. This study goes beyond the case study level and meets the requirements of countrywide data sets, in particular regarding repeatability, updating, costs and characteristics of training data sets. The 10 m countrywide DLT maps add complementary and spatially explicit information to the existing NFI estimates and are thus highly relevant for forestry practice and other related fields.



中文翻译:

基于联合 Sentinel-1/-2 数据绘制优势叶类型——山区国家面临的挑战

全国冬季和夏季 Sentinel-1 (S1) 反向散射数据、无云夏季 Sentinel-2 (S2) 图像、基于机载激光扫描 (ALS) 的数字地形模型 (DTM) 和森林掩膜被用于建模并随后映射优势叶类型 (DLT),其中包含整个瑞士的阔叶树和针叶树主题类别。使用来自航拍图像解释的参考数据开发了一种新颖的工作流程,该工作流程稳健、经济高效且高度自动化。应用了基于随机森林(RF)和深度学习(UNET)的两种机器学习方法,在全国范围内使用三组预测变量。应用基于坡向和坡度类别的 24 个分区来探索复杂山地地形对模型性能的影响。参考数据分成训练,验证和测试数据集使用 25 公里的规则网格在空间上分层。当包括来自 S1/S2 和来自 DTM 的地形变量方面、高程和坡度的预测变量时,RF 和 UNET 的模型精度通常最高,Kappa (K) 约为 0.95。虽然在使用 S2 和 DTM 数据时获得的准确度略低,但在仅包含来自 S1 和 DTM 的预测变量时获得的准确度最低,RF 的表现比 UNET 差。虽然在全国范围内,RF 和 UNET 的总体表现相似,但在西北到东北方向的子区域中发现模型性能存在显着差异,即更高的方差和更低的精度。S1/S2 和 DTM 预测器的组合使用减轻了这些与地形和阴影相关的问题,因此优于单独使用 S1 和 DTM 或 S2 和 DTM 数据。与独立的国家森林清单 (NFI) 地块数据的比较表明,DLT 预测中 K 的精度约为 0.6,并表明混交林中偏差增加的趋势。与哥白尼高分辨率层 (HRL) DLT 2018 的比较显示,除纯阔叶林外,总体地图精度更高。尽管 DTL 的空间模式总体相似,但在林分级别具有明显 DLT 的区域中,UNET 的表现优于 RF,当仅使用 S1 和 DTM 数据时,差异最大。相比之下,从 RF 获得的预测在混合林中更准确。本研究超越了案例研究的层面,满足了全国数据集的要求,特别是关于培训数据集的可重复性、更新、成本和特征。10 m 全国 DLT 地图为现有的 NFI 估计增加了补充和空间明确的信息,因此与林业实践和其他相关领域高度相关。

更新日期:2021-08-29
down
wechat
bug