Deriving land cover from remotely sensed data is fundamental to many operational mapping and reporting programs as well as providing core information to support science activities. The ability to generate land cover maps has benefited from free and open access to imagery, as well as increased storage and computational power. The accuracy of the land cover maps is directly linked to the calibration (or training) data used, the predictors and ancillary data included in the classification model, and the implementation of the classification, among other factors (e.g., classification algorithm, land cover heterogeneity). Various means for improving calibration data can be implemented, including using independent datasets to further refine training data prior to mapping. Opportunities also arise from a profusion of possible calibration datasets from pre-existing land cover products (static and time series) and forest inventory maps through to observation from airborne and spaceborne lidar observations. In this research, for the 650 Mha forested ecosystems of Canada, we explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation. We refined calibration data using measures of forest vertical structure, integrated novel spatial (via distance-to metrics) model predictors, and implemented a regionalized approach for optimizing training data selection and model-building to ensure local relevance of calibration data and capture of regional variability in land cover conditions. We found that additional vetting of training data involved the removal of 44.7% of erroneous samples (e.g. treed vegetation without vertical structure) from the training pool. Nationally, distance to ephemeral waterbodies was a key predictor of land cover, while the importance of distance to permanent water bodies varied on a regional basis. Regionalization of model implementation ensured that classification models used locally relevant descriptors and resulted in improved classification outcomes (overall accuracy: 77.9% ± 1.4%) compared to a generalized, national model (70.3% ± 2.5%). The methodological developments presented herein are portable to other land cover projects, monitoring programs, and remotely sensed data sources. The increasing availability of remotely sensed data for land cover mapping, as well as non-image data for aiding with model development (from calibration data to complementary spatial data layers) provide new opportunities to improve and further automate land cover mapping procedures.

从遥感数据中获取土地覆盖对于许多业务测绘和报告计划以及提供核心信息以支持科学活动至关重要。生成土地覆盖图的能力得益于对图像的免费和开放访问，以及增加的存储和计算能力。土地覆盖图的准确性与使用的校准（或训练）数据、分类模型中包含的预测变量和辅助数据、分类的实施以及其他因素（例如分类算法、土地覆盖异质性）直接相关）。可以实施各种改进校准数据的方法，包括使用独立数据集在映射之前进一步细化训练数据。从预先存在的土地覆盖产品（静态和时间序列）和森林清单地图到机载和星载激光雷达观测的大量可能的校准数据集也带来了机会。在这项研究中，对于加拿大 650 Mha 的森林生态系统，我们探索了改进校准数据、整合新预测因子和优化分类器实施的方法。我们使用森林垂直结构的测量来改进校准数据，集成新的空间（通过距离到度量）模型预测器，并实施区域化方法来优化训练数据选择和模型构建，以确保校准数据的本地相关性和区域变异性的捕获在土地覆盖条件下。我们发现对训练数据的额外审查涉及删除 44。7% 的错误样本（例如没有垂直结构的树木植被）来自训练池。在全国范围内，与短暂水体的距离是土地覆盖的关键预测因素，而与永久水体的距离的重要性因区域而异。模型实施的区域化确保分类模型使用本地相关的描述符，并与广义的国家模型 (70.3% ± 2.5%) 相比，提高了分类结果（总体准确度：77.9% ± 1.4%）。此处介绍的方法发展可移植到其他土地覆盖项目、监测程序和遥感数据源。用于土地覆盖测绘的遥感数据越来越多，