Analysis of the Spatial and Temporal Pattern of Changes in Abandoned Farmland Based on Long Time Series of Remote Sensing Data

Abstract

With the rapid increase in the costs of rural labour and the adjustment of planting structures, the phenomenon of farmland abandonment has appeared in China. It is of great significance to promptly and accurately grasp the information on dynamic temporal and spatial changes in abandoned farmland to ensure national food security and the sustainable use of cultivated land. Luquan District in Hebei, China was selected as the research area based on multispectral images from Sentinel-2A, Landsat-7, and Landsat-8 combined with methods of random forest (RF) classification and vegetation index change detection. Rules for the identification of abandoned farmland were also developed, and remote sensing monitoring of the abandonment status of the cultivated land was also carried out in the study area. We also obtained the spatial distribution of abandoned and reclaimed farmland and analysed the frequency of farmland abandonment. The results show that the overall accuracy of the land-use time-series map ranged from 90.20% to 96.92% for the study period of 2010–2020. The average rate of farmland abandonment in the study area was 10.62%, with the lowest rate (5.83%) in 2020 and the highest (14.09%) in 2012. From 2011 to 2020, the maximum farmland abandonment area was 3906.02 hm2, and the minimum area was 1618.74 hm2. The farmland abandonment area showed a trend of first increasing and then decreasing. From 2012 to 2020, the maximum area of reclaimed farmland was 291.49 hm2, and the highest rate of reclamation was 14.26%. The overall reclamation rate was low. The abandonment frequency of most of the abandoned farmland was 1–3 years, covering an area of 8193.73 hm2, which comprised 79% of the total area of abandoned farmland. The frequency of abandonment was inversely proportional to the area of abandoned farmland. Farmland abandonment mainly occurred in hilly areas. We expect that our results can provide case studies for long time series in farmland abandonment research and can provide a reference for studying the driving factors, risk assessment, and policymaking with respect to abandoned farmland.

Remote monitoring and analysis of sensor data in Internet of Things

“…After the database model is established, we can create the following data tables: user login information table, user basic information table, equipment information table, experimental information table and sensor data information table. Among them, the sensor data information table mainly stores the sensor data information of the Internet of Things collected by the server through the serial port of PC, and the other four tables are mainly used for the information exchange between the client browser web page and the database [13] .…”

Farmland change and its implications in the Three River Region of Tibet during recent 20 years

“…These have raised great concerns on its protection and sustainable utilization [ 7 – 9 ], and thus promoted a large number of relevant researches. The studies addressed the spatiotemporal change [ 10 – 16 ], driving factors [ 9 , 12 , 13 , 17 ], and its impact on grain production [ 1 , 5 , 10 ], mainly in the areas of rapid economic development, such as the Yangtze River Delta [ 10 ], the Pearl River Delta [ 11 ], and the Beijing-Hebei-Tianjin region [ 12 ], and the main grain producing areas of Henan [ 13 ], Hebei [ 14 ], and Shandong provinces [ 15 ]. The data used in the studies were mainly collected from yearbooks [ 16 , 17 ] or land use remote sensing data products [ 5 , 10 – 15 ] at a coarse spatial resolution ranging from 10 m to 8 km [ 18 ], and thus often involved uncertainties, particularly for the areas of fragmented landscape.…”

Groundwater potential mapping based on long time series remote sensing data in Penghu Islands, China

“…Hence, by using long‐term series remote sensing images, regional information characteristics can be acquired over time, greatly reducing the dependence on effective measured data on a limited spatio‐temporal scale. To date, researchers have used many remote sensing time series products of various surface parameters to study land use, climate, environmental change, and other aspects (Bishop‐Taylor et al, 2018; Sun et al, 2017; Wei et al, 2021; Zeng et al, 2020). However, there is no study on groundwater potential assessment and dynamic change using long‐term series remote sensing data.…”