Mapping Cropland Abandonment in Mountainous Areas in China Using the Google Earth Engine Platform

Abstract

Knowledge about the spatial-temporal pattern of cropland abandonment is the premise for the management of abandoned croplands. Traditional mapping approaches of abandoned croplands usually utilize a multi-date classification-based land cover change trajectory. It requires quality training samples for land cover classification at each epoch, which is challenging in regions of smallholder agriculture in the absence of high-resolution imagery. Facing these challenges, a theoretical model is proposed to recognize abandoned croplands based on post-abandonment secondary succession. It applies the continuous change detection and classification (CCDC) temporal segmentation algorithm to Landsat time series (1986~2021) to obtain disjoint segments, representing croplands’ status. The post-abandonment secondary succession showing a greening trend is recognized using NDVI-based harmonic analysis, so as to capture its preceding abandonment. This algorithm is applied to a mountainous area in southwest China, where cropland abandonments are widespread. Validation based on stratified random samples referenced by a vegetation index time series and satellite images shows that the detected abandoned croplands have user accuracy, producer accuracy and an F1 score ranging from 43% to 71%, with variation among abandonment year. The study area has a potential cropland extent of 22,294 km2, within which 9252 km2 of the cropland was abandoned. The three peak years of abandonment were 1994, 2000, and 2011. The algorithm is suitable to be applied to large-scale mapping due to its automatic manner.

Tracking the Effects of Mangrove Changes and Spartina alterniflora Invasion on Soil Carbon Storage: A Case Study of the Beibu Gulf of Guangxi, China

“…The occlusion of clouds and fog, the stripe problem of Landsat 7 satellite sensors, and tidal inundation have a certain impact on long-term observation. Other scholars have similar accuracy with this study, and the Kappa coefficient is 0.70-0.78 [38,39]. However, it cannot be denied that the CCDC algorithm is fully automated and can quickly provide temporal information on mangrove changes.…”

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

“…Some researchers have adopted methods based on landcover classification to extract NGP information (Su et al, 2019; Xiao et al, 2015; Yang & Zhang, 2021). However, this approach may face challenges in ensuring temporal consistency due to issues such as image noise and classification algorithms, which can affect the accuracy of classification results in each period and may magnify uncertainties in land cover change trajectories, making it difficult to determine whether the observed changes are real or result from misclassification in a particular image (Xu et al, 2023). Some other scholars have used decision tree models (Zhang et al, 2023) or existing datasets of food crop planting (Zhu et al, 2022), which are all limited in their ability to distinguish specific types of NGP.…”