The mapping of sugarcane plantations and their changes is relevant to the economy and the environment, notably due to sugarcane's interface in the biofuel industry through ethanol. The necessary mapping of sugarcane crop changes is especially challenging when plantation occurs under smallholders' land ownership structures used for heterogeneous crop management. We evaluated two approaches to address this challenge with the example of sugarcane abandonment in the Norte Fluminense Region, Northeastern Rio de Janeiro state, Brazil. The region is characterized by a large share of smallholders. We trained a random forest classifier for sugarcane for 2018 based on all available Landsat imagery. Based on the concept of temporal generalization, we applied the classifier trained in 2018 for the period from 1986 until 2020. The resulting annual sugarcane probability maps were used as input for two abandonment mapping methods: LandtrendR and muti-temporal cropland abandonment mapping. The performance of both approaches was evaluated based on a stratified sampling approach. We detected three distinct trajectories for sugarcane farmland: i) permanently abandoned sugarcane, ii) fallow sugarcane, and iii) and stable sugarcane. The multi-temporal cropland abandonment mapping performed better for the sugarcane abandonment class (F1 = 0.84) than the LandtrendR approach (F1 = 0.21). The LandtrendR results revealed a higher omission (PA = 0.12) in mapping the sugarcane abandonment class. For the multi-temporal cropland abandonment mapping, we found that 66% (67,353 ha) of the stable sugarcane areas were abandoned between 1990 and 2016. The highest abandonment rates occurred between 1990 and 1994 and between 2010 and 2016. The spatial distribution of abandonment was heterogeneous. The earliest abandonment was concentrated in the northern part of the study area. The most recent abandonment was more extensive in the southern part of the study site. Our results highlight the advantages and challenges of using Landsat time series to map sugarcane abandonment in a heterogeneous management system. Our results also highlight the spatially and temporal heterogeneous pattern of sugarcane abandonment in the region and provide the necessary database for subsequent studies to identify underlying and proximate causes for the abandonment.

甘蔗种植园及其变化的绘图与经济和环境相关，特别是由于甘蔗通过乙醇在生物燃料工业中的界面。当种植发生在用于异质作物管理的小农土地所有权结构下时，对甘蔗作物变化的必要映射尤其具有挑战性。我们以巴西里约热内卢州东北部的北弗鲁米嫩塞地区甘蔗遗弃为例，评估了应对这一挑战的两种方法。该地区的特点是小农户比例很大。我们根据所有可用的 Landsat 图像为 2018 年的甘蔗训练了一个随机森林分类器。基于时间泛化的概念，我们将 2018 年训练的分类器应用于 1986 年至 2020 年的时间段。由此产生的年度甘蔗概率图被用作两种废弃映射方法的输入：LandtrendR 和多时相农田废弃映射。两种方法的性能均基于分层抽样方法进行评估。我们检测到甘蔗农田的三个不同轨迹：i）永久废弃的甘蔗，ii）休耕甘蔗，以及 iii）稳定的甘蔗。多时相农田废弃图在甘蔗废弃类 (F1 = 0.84) 上的表现优于 LandtrendR 方法 (F1 = 0.21)。LandtrendR 结果显示，在绘制甘蔗遗弃类别图时遗漏较高（PA = 0.12）。对于多时相农田废弃图，我们发现 66%（67,353 公顷）的稳定甘蔗区在 1990 年至 2016 年间被废弃。1990-1994年和2010-2016年的废弃率最高。废弃的空间分布是异质的。最早的废弃集中在研究区的北部。最近的废弃在研究地点的南部更为广泛。我们的结果突出了使用 Landsat 时间序列在异构管理系统中绘制甘蔗废弃地图的优势和挑战。我们的研究结果还突出了该地区甘蔗遗弃的时空异质模式，并为后续研究提供了必要的数据库，以确定遗弃的潜在和近因。最早的废弃集中在研究区的北部。最近的废弃在研究地点的南部更为广泛。我们的结果突出了使用 Landsat 时间序列在异构管理系统中绘制甘蔗废弃地图的优势和挑战。我们的研究结果还突出了该地区甘蔗遗弃的时空异质模式，并为后续研究提供了必要的数据库，以确定遗弃的潜在和近因。最早的废弃集中在研究区的北部。最近的废弃在研究地点的南部更为广泛。我们的结果突出了使用 Landsat 时间序列在异构管理系统中绘制甘蔗废弃地图的优势和挑战。我们的研究结果还突出了该地区甘蔗遗弃的时空异质模式，并为后续研究提供了必要的数据库，以确定遗弃的潜在和近因。