This study derives regression models for above-ground biomass (AGB) estimation in miombo woodlands of Tanzania that utilise the high availability and low cost of Sentinel-1 data. The limited forest canopy penetration of C-band SAR sensors along with the sparseness of available ground truth restrict their usefulness in traditional AGB regression models. Therefore, we propose to use AGB predictions based on airborne laser scanning (ALS) data as a surrogate response variable for SAR data. This dramatically increases the available training data and opens for flexible regression models that capture fine-scale AGB dynamics. This becomes a sequential modelling approach, where the first regression stage has linked in situ data to ALS data and produced the AGB prediction map; We perform the subsequent stage, where this map is related to Sentinel-1 data. We develop a traditional, parametric regression model and alternative non-parametric models for this stage. The latter uses a conditional generative adversarial network (cGAN) to translate Sentinel-1 images into ALS-based AGB prediction maps. The convolution filters in the neural networks make them contextual. We compare the sequential models to traditional, non-sequential regression models, all trained on limited AGB ground reference data. Results show that our newly proposed non-sequential Sentinel-1-based regression model performs better quantitatively than the sequential models, but achieves less sensitivity to fine-scale AGB dynamics. The contextual cGAN-based sequential models best reproduce the distribution of ALS-based AGB predictions. They also reach a lower RMSE against in situ AGB data than the parametric sequential model, indicating a potential for further development.
翻译:本研究为坦桑尼亚米伦布林地的地面生物量(AGB)估计提供了回归模型,利用哨兵1号数据的高可用性和低廉成本,在坦桑尼亚米伦布林地利用高可用性和低廉的Sentinel-1数据进行地面生物量估计。C波段SAR传感器的森林树冠渗透有限,加上现有的地面真理稀少,限制了其在传统的AGB回归模型中的用处。因此,我们提议使用基于空中激光扫描(ALS)数据的AGB预测,作为SAR数据的代谢性反应变量。这大大增加了现有培训数据,并打开了灵活的回归模型,以捕捉到精细的AGB动态。这已成为一个连续的建模,第一个回归阶段将原地数据与ALS的精确度数据连接起来,并制作了AGB的预测地图;我们把这个地图的下一个阶段与Sentinel-1号数据相联系。我们为这个阶段开发了传统的、参数回归模型和替代的非参数模型。后者使用一个有条件的基调的对抗性对抗性对抗网络,将Sentilel-1的图像转化为模型比基于A的A-ral-ral Restal-ral-restal-laimal 一种更精确模型更精确模型。我们所的A-de-de 显示的A-deal-deal-deal-de-deal-deal-deal-deal-deal-deal-deal-maisal-laisal-deal-一个更精确的模型,用来显示了一种更精确模型。