Land cover classification and mapping of a polar desert in the Canadian Arctic Archipelago

The use of remote sensing for developing land cover maps in the Arctic has grown considerably in the last two decades, especially for monitoring the effects of climate change. The main challenge is to link information extracted from satellite imagery to ground covers due to the fine-scale spatial heterogeneity of Arctic ecosystems. There is currently no commonly accepted methodological scheme for high-latitude land cover mapping, but the use of remote sensing in Arctic ecosystem mapping would benefit from a coordinated sharing of lessons learned and best practices. Here, we aimed to produce a highly accurate land cover map of the surroundings of the Canadian Forces Station Alert, a polar desert on the northeastern tip of Ellesmere Island (Nunavut, Canada) by testing different predictors and classifiers. To account for the effect of the bare soil background and water limitations that are omnipresent at these latitudes, we included as predictors soil-adjusted vegetation indices and several hydrological predictors related to waterbodies and snowbanks. We compared the results obtained from an ensemble classifier based on a majority voting algorithm to eight commonly used classifiers. The distance to the nearest snowbank and soil-adjusted indices were the top predictors allowing the discrimination of land cover classes in our study area. The overall accuracy of the classifiers ranged between 75 and 88%, with the ensemble classifier also yielding a high accuracy (85%) and producing less bias than the individual classifiers. Some challenges remained, such as shadows created by boulders and snow covered by soil material. We provide recommendations for further improving classification methodology in the High Arctic, which is important for the monitoring of Arctic ecosystems exposed to ongoing polar amplification.

过去二十年间，遥感（remote sensing）在北极地区土地覆盖图绘制中的应用大幅增长，尤其在气候变化影响监测领域备受关注。当前的核心挑战在于，北极生态系统具有精细尺度的空间异质性，如何将卫星影像提取的信息与地表覆盖建立有效关联。目前尚无被广泛认可的高纬度土地覆盖制图方法体系，但北极生态系统制图中的遥感应用，可通过协同共享经验教训与最佳实践获得显著提升。
本研究以加拿大努纳武特地区埃尔斯米尔岛东北端的极地荒漠——加拿大武装部队阿尔特站（Canadian Forces Station Alert）周边区域为研究对象，通过测试不同预测因子与分类器，旨在生成高精度土地覆盖图。鉴于该纬度带普遍存在的裸土背景效应与水分限制条件，我们将土壤调节植被指数（soil-adjusted vegetation indices）以及若干与水体、雪堆相关的水文预测因子纳入预测变量。
我们将基于多数投票算法的集成分类器结果，与八种常用分类器的结果进行对比分析。研究发现，到最近雪堆的距离与土壤调节指数是区分本研究区土地覆盖类别的最优预测因子。各分类器的总体精度介于75%至88%之间，集成分类器同样表现出高精度（85%），且相较于单分类器偏差更低。
本研究仍存在部分待解决的挑战，例如巨石投射的阴影以及被土壤覆盖的积雪。我们针对高北极地区的分类方法优化提出改进建议，这对于监测正经历极地放大效应的北极生态系统具有重要意义。