Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km² vs. ~195 km²) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery.

Arctic temperatures have increased at almost twice the global average rate since the industrial revolution. Some studies also reported a further amplified rate of climate warming at high elevations; namely, the elevation dependency of climate change. This elevation-dependent climate change could have important implications for the fate of glaciers and ecosystems at high elevations under climate change. However, the lack of long-term climate data at high elevations, especially in the Arctic, has hindered the investigation of this question. Because of the linkage between climate warming and plant phenology changes and remote sensing’s ability to detect the latter, remote sensing provides an alternative way for investigating the elevation dependency of climate change over Arctic mountains. This study investigated the elevation-dependent changes to plant phenology using AVHRR (Advanced Very High Resolution Radiometer) time series from 1985 to 2013 over five study areas in Canada’s Arctic. We found that the start of the growing season (SOS) became earlier faster with an increasing elevation over mountainous study areas (i.e., Sirmilik, the Torngat Mountains, and Ivvavik National Parks). Similarly, the changes rates in the end of growing season (EOS) and the growing season length (GSL) were also higher at high elevations. One exception was SOS in the Ivvavik National Park: “no warming trend” with the May-June temperature at a nearby climate station decreased slightly during 1985–2013, and so no elevation-dependent amplification.

Illumination variations in non-atmospherically corrected high-resolution satellite (HRS) images acquired at different dates/times/locations pose a major challenge for large-area environmental mapping and monitoring. This problem is exacerbated in cases where a classification model is trained only on one image (and often limited training data) but applied to other scenes without collecting additional samples from these new images. In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one area to a source WV2 image of another area on which a lichen detector model was trained. In this regard, we considered an extreme case where the classifier was not fine-tuned on the normalized images. We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2 panchromatic band and Sentinel-2 surface reflectance (SR) imagery. Our experiments showed that normalizing even based only on the WV2 panchromatic band led to a significant lichen-detection accuracy improvement compared to the use of original pansharpened target images. However, we found that conditioning the cGAN on both the WV2 panchromatic band and auxiliary information (in this case, Sentinel-2 SR imagery) further improved normalization and the subsequent classification results due to adding a more invariant source of information. Our experiments showed that, using only the panchromatic band, F1-score values ranged from 54% to 88%, while using the fused panchromatic and SR, F1-score values ranged from 75% to 91%.

Forests cover nearly half of Canada's landmass. While forested lands are often viewed as areas of wood production, forests also provide wildlife habitat and ecosystem mechanisms to clean air and water, and sequester carbon. Measuring the area of forested land in Canada on a regular basis provides an indicator of the availability of these important ecosystem services. This study examines the capability of coarse spatial resolution satellite data to quantify forest cover based on crown closure estimates. Field data, high and medium resolution remote sensing imageries, and a canopy radiative transfer model (Five-Scale) are used to assess the mapping potential of 1-km data, such as AVHRR, VGT and MODIS. The main challenge of the research is in the transition zone between boreal forest and the tundra, where few inventory data are available and the trees are found in clusters. The results will be used in a Canada-wide forest indicator that aimed at monitoring yearly changes in the forest extend due in part to forest fires, insect defoliation, regrowth and changes due to climate change. Different methodologies and initial results of this ongoing activity are presented.