Monitoring three-decade dynamics of citrus planting in Southeastern China using dense Landsat records

Abstract

Across Southeastern China, the expansion of citrus plantation has underpinned substantial economic development at the expense of forest cover and associated ecosystem services. The high-order complexity of citrus planting dynamics requires more detailed observations. To achieve this goal, we developed a novel monitoring scheme by integrating multi-epoch land cover classification and continuous change detection on the Google Earth Engine (GEE) platform using all available Landsat archives (1986–2018). First, a stable/changed area masking approach was adopted. Then, the GEE-based Continuous Change Detection and Classification (GEE-CCDC) algorithm was applied to detect the timing and position of citrus planting-related land cover changes. Finally, the effectiveness of the scheme was validated both spatially and temporally. We applied the proposed scheme in Xunwu, a typical County in Southeastern China where continuous expansion was disrupted by an outbreak of Huanglongbing (HLB, a destructive citrus disease). The validation results indicate that the multi-epoch classification effectively identified the stable, abandoned, and newly cultivated areas related to citrus planting. Within these areas, our monitoring of citrus planting dynamics had an overall temporal accuracy of 90.59% and a mean detection date deviation of −13.21 days (leading). Based on the change detection results, we found a two-stage change pattern, namely a continuous expansion period (1986–2016) followed by a disturbance period (2017–2018). Moreover, almost half of the orchards were cultivated in regions with an elevation and slope of 301–400 m and 11–20°. During the continuous expansion period, a total of 497.85 km2 natural lands were converted for citrus planting. Affected by the HLB outbreak, both orchard abandonment (114.66 km2) and new orchard cultivation (36.87 km2) were detected during the disturbance period. The proposed scheme offers a useful tool for cash crops management, and highlights the value of dense satellite data in monitoring the extent of ongoing human appropriation of natural ecosystems at a large scale and long-term.