Abstract Remote sensing assessments of land use and land cover change (LULCC) are critical to improve understanding of socio-economic, institutional and ecological processes that lead to and stem from land use change. This is particularly crucial in the emerging frontiers of Southern Africa, where there is a paucity of LULCC studies relative to the humid tropics. This study focuses on Gurue District (5606 km2) of Zambezia province of Mozambique, one of many countries in the region that has experienced a recent growth in foreign investments in agriculture through large-scale land acquisitions, often resulting in land use conversions and modifications. Previous LULCC assessments covering Mozambique have focused on dynamics between natural and anthropogenic land categories, with limited efforts to distinguish the different land use agents associated with these changes, and relating this with social, economic and technological processes. In this study we built a new LULC assessment methodology that leverages the power of open remote sensing data and tools to integrate categorical and continuous training and validation data obtained from field surveys and Collect Earth software within Google Earth Engine. We then examined the suitability of five pixel-based compositing techniques for generating cloud-free Landsat images that can support analysis of land use dynamics in persistently cloudy, mosaic landscapes with more limited Landsat archives. Drawing upon the spectral and textural features of Landsat data in pixel-based composites, we classified land use over three time periods, 2006, 2012 and 2016, and characterized land use change, focusing on changes between small-scale cropland, large-scale mechanized cropland, and other land uses. This method can be upscaled and applied in many parts of Africa with similar historic image availability challenges, and similar economic contexts with great disparities between small-scale unmechanized cropland and very large-scale mechanized cropland, to explore land consolidation dynamics and agent-specific pathways of land use change.

中文翻译：

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使用灵活的分类系统和基于像素的复合材料绘制莫桑比克新兴边境的小农和大规模农田动态

摘要 土地利用和土地覆盖变化 (LULCC) 的遥感评估对于提高对导致和源于土地利用变化的社会经济、制度和生态过程的理解至关重要。这在南部非洲的新兴前沿地区尤为重要，那里缺乏与潮湿热带地区相关的 LULCC 研究。本研究重点关注莫桑比克赞比西亚省古鲁区（5606 平方公里），该地区是该地区最近通过大规模土地收购实现农业外国投资增长的众多国家之一，这往往导致土地用途的转换和修改。以前涵盖莫桑比克的 LULCC 评估侧重于自然和人为土地类别之间的动态，努力区分与这些变化相关的不同土地利用因素，并将其与社会、经济和技术进程联系起来。在这项研究中，我们构建了一种新的 LULC 评估方法，该方法利用开放遥感数据和工具的力量，整合从实地调查和谷歌地球引擎中的收集地球软件中获得的分类和持续训练和验证数据。然后，我们研究了五种基于像素的合成技术在生成无云 Landsat 图像方面的适用性，这些技术可以支持对 Landsat 档案更有限的持续多云、马赛克景观中的土地利用动态进行分析。利用基于像素的复合材料中 Landsat 数据的光谱和纹理特征，我们对三个时间段的土地利用进行了分类，2006、2012 和 2016，并表征土地利用变化，重点关注小规模耕地、大规模机械化耕地和其他土地利用之间的变化。这种方法可以在非洲许多地区进行推广和应用，这些地区具有类似的历史图像可用性挑战，以及类似的经济背景，小型非机械化农田和超大规模机械化农田之间存在巨大差异，以探索土地整理动态和特定于代理的途径土地利用变化。