Mapping abandoned farmland in China using time series MODIS NDVI

Revealing the distribution and change of abandoned cropland in Ukraine based on dual period change detection method

Since the outbreak of the Russia-Ukraine conflict in 2022, Ukraine has experienced different types of abandoned cropland, such as unused and unattended cropland, as a result of war damage, agricultural infrastructure destruction, and refugee outflows. Common methods for detecting abandoned cropland have difficulty effectively identifying and distinguishing these different types. This study proposes a Dual-period Change Detection method to reveal the spatial distribution and changes of different types of abandoned cropland in Ukraine, which can aid in agricultural assessments and international assistance in conflict-affected areas. The method mainly utilizes time-series NDVI data to fit the crop curves corresponding to cropland on a pixel-by-pixel basis, and then establishes discrimination rules for different types of abandoned cropland based on the crop curves, so as to detect unused cropland in the pre-conflict period (2015-2021) as well as unused cropland and unattended cropland in the post-conflict period (2022-2023). Finally, the detection results are validated and accuracy assessed using medium and high resolution spatiotemporal remote sensing imagery interpretation. The results show that the overall accuracy of the abandoned cropland extraction in Ukraine ranges from 83 to 96% during the study period. Before the conflict, the national average unused rate was 1.6%, with the lowest in 2021 and the highest in 2018. In 2022, the unused cropland area was approximately twice the average unused area before the conflict, and it was widely distributed, with the area of unattended cropland reaching 462,000 hectares, mainly in the eastern part of Ukraine. In 2023, compared to 2022, the unused cropland area decreased by 67.8%, while unattended cropland increased by 116.7%. Both types of abandoned cropland exhibited spatial clustering, with major clusters identified in the Crimea region, Kherson Oblast, Zaporizhzhia Oblast, and Donetsk Oblast.

How to develop nature-based solutions for revegetation on abandoned farmland in the Loess Plateau of China?

Adequate revegetation of abandoned farmland acts as a defence against desertification and soil loss, and can help remove carbon dioxide in the atmosphere, thereby playing an important role in regulating regional climate change. Legume, a nitrogen-fixation species, which could effectively improve vegetation coverage to control soil erosion, was widely used for revegetation. However, the dynamics of soil and plant development after legume introduction on abandoned farmland remain unclear. A 16-year in situ experiment including three treatments, natural abandonment (fallow), planting of alfalfa (Medicago sativa L.), and sweet clover (Melilotus officinalis L.) was conducted on bare farmland of the Loess Plateau in 2003-2018. The results showed that initially introduced species significantly affected the potential succession patterns in the community. Alfalfa introduction decreased plant community stability (CS) and hindered plant species establishment in early successional stages due to inter/intraspecific competition caused by high aboveground biomass (AB). Plant CS was affected by species evenness, AB, revegetation time and revegetation methods. Sweet clover facilitated succession process by rapidly improving soil conditions (organic carbon, nitrogen, and phosphorus) and quickly exiting from the community after its life span to avoid further competitive effects. During 2003-2018, the soil (water storage, organic carbon, nitrogen, and phosphorus), plant (AB, CS), and ecological related variables (plant diversity and soil carbon sequestration) contributed 60.1%, 15.7% and 20.2%, respectively, to the ecosystem health. Alfalfa planting increased ecosystem health index (EHI) in the long-term while sweet clover favours plant diversity, providing less overall EHI but recover faster than natural abandonment community. We concluded that alfalfa introduction, which provides the greatest AB, is a good option for comprehensively improving ecosystems (e.g., soil nutrient sequestration and control soil erosion) if the site in question suffers from few disturbances. Sweet clover introduction, however, is recommendable for restoring native biodiversity effectively if disturbances are frequent.

Investigating the Spatial Distribution and Influencing Factors of Non-Grain Production of Farmland in South China Based on MaxEnt Modeling and Multisource Earth Observation Data

Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution and influencing factors of NGPF is essential for food and agricultural management. However, previous studies on NGPF identification have mainly relied on high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques have difficulty in accurately identifying NGPF based solely on spectral information, as NGPF is not merely a natural phenomenon. Accurately identifying the distribution of NGPF at a grid scale and elucidating its influencing factors have emerged as critical scientific challenges in current literature. Therefore, the aims of this study are to develop a grid-scale method that integrates multisource remote sensing data and spatial factors to enhance the precision of NGPF identification and provide a more comprehensive understanding of its influencing factors. To overcome these challenges, we combined multisource remote sensing images, natural/anthropogenic spatial factors, and the maximum entropy model to reveal the spatial distribution of NGPF and its influencing factors at the grid scale. This combination can reveal more detailed spatial information on NGPF and quantify the integrated influences of multiple spatial factors from a microscale perspective. In this case study of Foshan, China, the area under the receiver operating characteristic curve is 0.786, with results differing by only 1.74% from the statistical yearbook results, demonstrating the reliability of the method. Additionally, the total error of our NGPF identification result is lower than that of using only natural/anthropogenic information. Our method enhances the spatial resolution of NGPF identification and effectively detects small and fragmented farmlands. We identified elevation, farming radius, and population density as dominant factors affecting the spatial distribution of NGPF. These results offer targeted strategies to mitigate excessive NGPF. The advantage of our method lies in its independence from negative samples. This feature enhances its applicability to other cases, particularly in regions lacking high-resolution grain crop-related data.

Assessing the effects of climate and human activity on vegetation change in Northern China

Fractional vegetation cover (FVC) has changed significantly under various disturbances over northern China in recent decades. This research examines the dynamics of FVC and how it is affected by climate and human activity during the period of 1990-2018 in northern China. The effects of climate change (i.e., temperature, precipitation, solar radiation, and soil moisture) and human activity (socioeconomic data and land use) on vegetation coverage change in northern China from 1990 to 2018 were quantified using the Sen + Mann-Kendall test, partial correlation analysis, and structural equation modelling (SEM) methods. The findings of this research indicate the following: (1) From 1990 to 2018, the overall trend in FVC in northern China was increased. The areas with obvious increases were mainly situated on the northern slope of Tianshan Mountains, Xinjiang, the Loess Plateau, the Northeast China Plain, and the Sanjiang Plain, while the areas with distinct degradation were located in the Inner Mongolia Plateau, the Changbai Mountain and the eastern part of north China. (2) In the past 29 years, the FVC in northern China has been mainly affected by precipitation and soil moisture. (3) Based on structural equation modelling, we discovered that certain variables impacted the main factors influencing the amount of FVC in northern China. Human activity has had a larger impact on FVC than climate change. Our findings can accelerate the comprehension of vegetation dynamics and their underlying mechanisms and provide a theoretical basis for regional ecological environmental protection.

Identifying and monitoring of abandoned farmland in key agricultural production areas on the Qinghai‒Tibet Plateau: A case study of the Huangshui Basin

Curbing the continuous abandonment of large areas of farmland is important for meeting the global food demand and promoting agricultural and rural development. Accurate identification is the key to the effective management and utilization of abandoned farmland. The identification of abandoned land based on a long time series of remote sensing data has become rapid and effective. Therefore, a set of training and test datasets generated from invariant samples and reference sample sets is established in this paper. On this basis, the Google Earth Engine (GEE) is used to classify Landsat and Sentinel high-precision long-term remote sensing images from 2000 to 2022. In addition, a change detector based on the sliding window algorithm is proposed to extract abandoned farmland in the Huangshui Basin from 2002 to 2020, and the intensity, trend, frequency, reclamation rate and utilization efficiency are analyzed. The results revealed that the OA of land use classification in the Huangshui Basin from 2000 to 2022 was between 0.852 and 0.91, and the kappa coefficient was between 0.822 and 0.89, indicating a good classification effect. From 2002 to 2020, the accumulated abandoned farmland area in the Huangshui Basin continued to increase, showing a fluctuating upward trend, and the phenomenon of farmland abandonment and reclamation occurs repeatedly in some areas. From the overall distribution, the abandoned area gradually increased from the central region to the southeast. With the passage of time, the amount of abandoned farmland in the valley increased gradually, and the abandoned area was transferred from the high mountains to the valley area. The average annual abandonment rate of supplementary farmland was 50.45%, which was much greater than that of basic farmland. Most of the supplementary farmland could not be effectively and judiciously used, and the utilization efficiency was low. The research results provide data support for the reuse of abandoned farmland in ecologically fragile plateau areas, the formulation of targeted strategies, the implementation of timely adjustments, and the establishment of new ideas and methods for the accurate identification of abandoned farmland.

The 10-m cotton maps in Xinjiang, China during 2018-2021

Cotton maps (10 m) of Xinjiang (XJ_COTTON10), which is the largest cotton production region of China, were produced from 2018 to 2021 through supervised classification. A two-step mapping strategy, i.e., cropland mapping followed by cotton extraction, was employed to improve the accuracy and efficiency of cotton mapping for a large region of about 1.66 million km2 with high heterogeneity. Additionally, the time-series satellite data related to spectral, textural, structural, and phenological features were combined and used in a supervised random forest classifier. The cotton/non-cotton classification model achieved overall accuracies of about 95% and 90% on the test samples of the same and adjacent years, respectively. The proposed two-step cotton mapping strategy proved promising and effective in producing multi-year and consistent cotton maps. XJ_COTTON10 agreed well with the statistical areas of cotton at the county level (R2 = 0.84-0.94). This is the first cotton mapping for the entire Xinjiang at 10-meter resolution, which can provide a basis for high-precision cotton monitoring and policymaking in China.

Cropland fallow reduces agricultural water consumption by 303 million tons annually in Gansu Province, China

The high-intensity utilization of global cropland causes water shortage and food crisis, which seriously affects the realization of SDG 2 (zero hunger), SDG 6 (clean water and sanitation) and SDG 15 (life on land), and threatens the sustainable social, economic and ecological development. Cropland fallow can not only improve the quality of cropland and maintain ecosystem balance, but also have a significant water-saving effect. However, in most developing countries, such as China, cropland fallow has not yet been widely promoted, and there are few reliable fallow cropland fallow identification methods, making it even more challenging to assess the water-saving effect. To remedy this deficit, we propose a framework for mapping cropland fallow and evaluating its water savings. First, we used the Landsat series of data to interpret the annual land use/cover changes in Gansu Province, China from 1991 to 2020. Subsequently, the spatial-temporal variation of cropland fallow in Gansu province (giving up farming for one to two years) was mapped. Finally, we evaluated the water-saving effect of cropland fallow using evapotranspiration, precipitation, irrigation maps, and crop-related data, instead of actual water consumption. The results showed that the mapping accuracy of fallow land in Gansu Province was 79.50 %, which was higher than that of most known fallow mapping studies. From 1993 to 2018, the average annual fallow rate in Gansu Province, China, was 10.86 %, which was at a low level in arid/semi-arid regions worldwide. More importantly, from 2003 to 2018, cropland fallow reduces annual water consumption of 303.26 million tons in Gansu Province, accounting for 3.44 % of agricultural water use in Gansu Province and equivalent to the annual water demand of 655,000 people in Gansu Province. Based on our research, we speculate that the increasing pilot projects of cropland fallow in China can bring significant water-saving effects and help achieve China's Sustainable Development Goals.

The pattern of abandoned cropland and its productivity potential in China: A four-years continuous study

The increased requirement of food production with the rising population challenges limited cultivation land in China. The abandoned cropland has high potential in grain production to ensure China's food security. However, the spatial distributions of abandoned cropland in China are understudied and therefore it is difficult to estimate its potential grain production. Our study proposed a new definition of abandoned cropland considering unique multiple cropping systems in China, and estimate the abandoned cropland distribution and grain productivity potential by using Landsat-8 and GF-1 images under deep learning technology. The area of abandoned cropland in three main grain-producing regions was approximately 1.53 million hectares during 2014-2017. The estimated images agreed with the field survey and the national agricultural statistical data with the accuracy larger than 87 %. The spatial distribution of abandoned cropland in China was scattered and a high abandonment rate observed in the Middle-lower Yangtze River Plain. Moreover, the uncultivated cropland accounted for approximately 50 % of the total area of abandoned cropland. The maximum production potential of abandoned cropland could reach 8.5 million tons, including 2.7, 2.5 and 3.3 million tons of maize, wheat and rice, respectively. The exploitation of abandoned cropland is also beneficial for additional soybean production in China. National-scale estimation of abandoned cropland in China is crucial for land use policy making and cropland protection, as well the implementation of national food security strategy.

Evolution of livelihood vulnerability in rice terrace systems: Evidence from households in the Ziquejie terrace system in China

Introduction

Globally, terraces, and rice terrace systems face problems that affect their sustainability, such as terrace degradation, abandonment, de-agriculturalization, labor migration, etc. The implementation of development projects such as reforestation, poverty alleviation, and tourism development have changed traditional smallholder livelihood patterns. It is not clear whether farmers' livelihoods have become more resilient or vulnerable as a result.

Methods

Using survey data on households' livelihoods in a rice terrace system in Southern China, we evaluated the livelihood impacts of multiple changes.

Results

The results show that development projects are not entirely beneficial. The attributes and intensity of the disturbance of projects (e.g., tourism) on land functions, and differentiated farmer strategies jointly drive the hierarchical evolution of livelihood vulnerability. In detail, underdeveloped tourism increased rather than reduced livelihood vulnerability; the role of agriculture in livelihood directly exacerbated the variation in vulnerability levels; this resulted in the most vulnerable livelihood for households that are exogenously dependent or located in the core tourism area.

Discussion

Subsequently, an evolutionary model of livelihood vulnerability is proposed in this study. Based on this, we judged that the livelihood vulnerability of rice terrace systems has entered a chaotic stage of adaptation. Reducing livelihood vulnerability will require the support of a tangible and circular pathway of benefits between farmers and the land. Policies should focus on the heterogeneity of farmers and the “negative effects” of development projects on livelihood. This household-level farmer livelihood vulnerability dynamics study goes beyond anti-poverty to provide science-based practical guidance to promote the sustainable development of rice terrace systems.

Reveal the severe spatial and temporal patterns of abandoned cropland in China over the past 30 years

The abandonment phenomenon is familiar in China. It threatens food security and seriously affects the sustainable development of society, the economy, and the natural environment. However, monitoring and mapping abandoned cropland on a large scale remains a challenge because of its complex land-use change process. According to the World Food and Agriculture Organization, cropland not used for agricultural production for more than 5 years is defined as abandoned cropland. This study uses the Landsat high-precision long-time series product to detect cropland nationwide from 1990 to 2019 by using the sliding window method on Google Earth Engine to profile the spatial distribution, intensity, trend, frequency, and recultivation of abandoned cropland. Between 1992 and 2015, the results illustrate that the total area of abandoned cropland in China was 559,170.26 km², 18.59 % of the cropland area. Excluding the recultivated cropland area, China's abandoned cropland area amounted to 392,156.24 km², 13.03 % of the total cropland. Cropland abandonment is mainly distributed in the second terrain gradient in midwestern and southwestern regions such as Inner Mongolia and Gansu. It rarely happens in western and eastern coastal areas. A high abandonment rate area usually has high elevation and slope. The light index is negatively correlated with the abandonment rate in suburban areas. This study is the first to map the spatiotemporal distribution of abandoned cropland in China with high precision at 30 m resolution. It provides an important basis for policies regarding the recultivation of abandoned cropland.

Spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity in the Mekong subregion in Cambodia

Water resources in the Mekong subregion in Cambodia (MSC) have experienced dramatic changes in past decades, threatening regional ecosystem quality and sustainable development. Thus, it is important to explore the spatiotemporal impacts of climate change and human activities on water resources and ecological sensitivity. This study proposed an effective framework including spatiotemporal analysis of land use/cover change (LUCC) and ecological sensitivity assessment by combining remote sensing (RS) and geographic information system/science (GIS). An optimized feature space and a machine learning classification algorithm were constructed to extract four typical land cover types in the MSC from 1990 to 2020. An ecological sensitivity evaluation system, including four sub-sensitivities calculated by twelve indicators, was then constructed. The results suggest that severe shrinkage of water resources occurred before 2006, decreasing by 21.68%. The correlation between water resources and climate conditions displays a high to low level as human activity becomes involved. A significant spatiotemporal evolutionary pattern of ecological sensitivity was observed under the impact of external interference. Generally, the largest proportion of MSC belongs to the lightly sensitive level, which is mainly concentrated in the lower reaches, with an average of 33.93%. The highly sensitive area with a significant value in ecological protection has a slightly downward trend from 23.72 in 1990 to 22.55% in 2020.

Risk assessment and validation of farmland abandonment based on time series change detection

Farmland abandonment, a widespread phenomenon during land-use transition, leads to a cycling or vanishing evolution of farmland resources. As urbanization advances, an increasing number of agricultural laborers migrate from rural to urban areas, causing ongoing farmland abandonment. However, in contrast to the abandoned information extraction and driving mechanisms revelation, the potential risk of farmland abandonment has received insufficient attention. This study took Yangtze River Economic Belt of China as study area, selected multiple aspects to construct a risk assessment system for farmland abandonment, and applied time series change detection to verify the results. The results showed that (1) farmland abandonment risk, with a regional average value of 0.0978, has strong spatial heterogeneity, with high values clustering in Yunnan-Guizhou and Sichuan-Chongqing mountainous areas and low values distributed in the midstream and downstream plains and the Sichuan Basin. (2) The proportion of farmland area gradually decreased as the risk grade increased. Farmland, with low abandonment risk, occupied an area of 204,837 km², constituting the highest percentage of 35.18% among the overall farmland, and was mainly distributed in the provinces of Jiangsu and Anhui. The area of farmland with high risk was 16,458 km², only accounting for 2.83%, the majority of which was clustered in Sichuan and Yunnan provinces. (3) The Normalized Difference Vegetation Index (NDVI) time series change detection validated the reliability of the risk assessment system. Samples of farmland having low abandonment risk indeed had the lowest abandonment rate of 10%, and those which indicated high risk had the highest abandonment rate of 32%. We propose differentiated managements for farmland resources with high and low abandonment risk from the perspective of sustainable use. This study provides a more reasonable and scientific system for farmland abandonment risk assessment and helps to fill the research gap.

Zoning of precipitation regimes on the Qinghai-Tibet Plateau and its surrounding areas responded by the vegetation distribution

Compared with other factors influencing vegetation patterns, such as light and temperature, precipitation has relatively large variability, especially on the Qinghai-Tibet Plateau (QTP), where the natural environment is extremely fragile and sensitive. However, the impact of precipitation regimes, rather than precipitation amount, on vegetation has seldom been revealed. This study characterised the precipitation regimes by both the amount and temporal distribution of precipitation and zoned the QTP as different precipitation regimes accordingly. The response of vegetation to such precipitation regimes was then investigated. The results indicate that the vegetation patterns are quite consistent with zoning, that is, there is a certain type or a few dominant types of vegetation in each sub-region divided by the precipitation regimes. The areas where the precipitation became more uniform within a year were concentrated in grassland and bare land, which benefits the restoration and improvement of the ecological environment of the plateau. The increase in precipitation variability in the south-eastern part of the plateau may lead to natural disasters such as floods and mudslides. This study provides a novel perspective to understand the distribution of vegetation patterns.

Generating High-Resolution and Long-Term SPEI Dataset over Southwest China through Downscaling EEAD Product by Machine Learning

Drought is an event of shortages in the water supply, whether atmospheric, surface water or ground water. Prolonged droughts have negative impacts on ecosystems, agriculture, society, and the economy. Although existing drought index products are widely utilized in drought monitoring, the coarse spatial resolution greatly limits their applications on regional or local scales. Machine learning driven by remote sensing observations offers an opportunity to monitor regional scale droughts. However, the limited time range of remote sensing observations such as vegetation index (VI) resulted in a substantial gap in generating high resolution drought index products before 2000. This study generated spatiotemporally continuous Standardized Precipitation Evapotranspiration Index (SPEI) data spanning from 1901–2018 in southwestern China by machine learning. It indicated that four Classification and Regression Tree (CART) approaches, decision trees (DT), random forest (RF), gradient boosted regression trees (GBRT) and extra trees (ET), can provide valid local drought information by downscaling the Estación Experimental de Aula Dei (EEAD) data. The in-situ SPEI dataset produced by the Penman–Monteith method was used as a benchmark to evaluate the temporal and spatial performance of the downscaled SPEI. In addition, the necessity of VI in SPEI downscaling was also assessed. The results showed that: (1) the ET-based product has the best performance (R2 = 0.889, MAE = 0.232, RMSE = 0.432); (2) the VI provides no significant improvement for SPEI re-construction; (3) topography exerts an obvious influence on the downscaling process, and (4) the downscaled SPEI shows more consistency with the in-situ SPEI compared with EEAD SPEI. The proposed method can be easily extended to other areas without in-situ data and enhance the ability of long-term drought monitoring.

Digital soil mapping of soil total nitrogen based on Landsat 8, Sentinel 2, and WorldView-2 images in smallholder farms in Yellow River Basin, China

Predicting spatial explicit information of soil nutrients is critical for sustainable soil management and food security under climate change and human disturbance in agricultural land. Digital soil mapping (DSM) techniques can utilize soil-landscape information from remote sensing data to predict the spatial pattern of soil nutrients, and it is important to explore the effects of remote sensing data types on DSM. This research utilized Landsat 8 (LT), Sentinel 2 (ST), and WorldView-2 (WV) remote sensing data and employed partial least squares regression (PLSR), random forest (RF), and support vector machine (SVM) algorithms to characterize the spatial pattern of soil total nitrogen (TN) in smallholder farm settings in Yellow River Basin, China. The overall relationships between TN and spectral indices from LT and ST were stronger than those from WV. Multiple red edge band-based spectral indices from ST and WV were relevant variables for TN, while there were no red band-based spectral indices from ST and WV identified as relevant variables for TN. Soil moisture and vegetation were major driving forces of soil TN spatial distribution in this area. The research also concluded that farmlands of crop rotation had relatively higher TN concentration compared with farmlands of monoculture. The soil prediction models based on WV achieved relatively lower model performance compared with those based on ST and LT. The effects of remote sensing data spectral resolution and spectral range on enhancing soil prediction model performance are higher than the effects of remote sensing data spatial resolution. Soil prediction models based on ST can provide location-specific soil maps, achieve fair model performance, and have low cost. This research suggests DSM research utilizing ST has relatively high prediction accuracy, and can produce soil maps that are fit for the spatial explicit soil management for smallholder farms.

Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure

The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R² from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.

Influence of farmland marginalization in mountainous and hilly areas on land use changes at the county level

Agricultural works alter earth's surface at the largest scale among human-driven activities. Previous studies have focused more on the reclamation of natural land, however, farmland marginalization (FM), emerging as an important mean of land use changes in mountainous and hilly areas (MHAs) has always been overlooked in the background of production efficiency improvement along with urbanization and population migration. This paper examined the characteristics of the spatial-temporal distribution and conversion of marginalized farmland in the MHAs of China at county level (excluding Hong Kong, Macau, and Taiwan) from 1990 to 2020, regarding farmland in MHAs converted into non-built-up land as FM. The results showed that: (1) The total area of marginalized farmland in the MHAs was 1.03 × 10⁶ km². The counties with larger area of marginalized farmland were concentrated around the Hu Line, and those with higher ratio were distributed in southern mountainous areas. (2) The area of marginalized farmland in each stage exhibited a fluctuating trend from 1990 to 2020. Forests and grasslands were prioritized as the desirable types in land conversion, and had prominent spatial agglomeration. (3) The influence of FM in MHAs on land use changes at county level demonstrated significant spatial-temporal heterogeneity, with wide range and low intensity from 1990 to 2000 and 2015 to 2020, and narrow range and high intensity from 2000 to 2015, and the counties with high intensity were distributed in the Loess Plateau and Sichuan-Chongqing hilly region. (4) The slope of marginalized farmland exhibited a prominent rule of spatial distribution, but an insignificant temporal trend under the influence of governmental policies. The larger the slope was, the higher the degree of marginalization was, but not necessarily earlier it occurred. The results can provide a reference for the formulation and implementation of farmland protection policies.

Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images

Hilly areas are important parts of the world’s landscape. A marginal phenomenon can be observed in some hilly areas, leading to serious land abandonment. Extracting the spatio-temporal distribution of abandoned land in such hilly areas can protect food security, improve people’s livelihoods, and serve as a tool for a rational land plan. However, mapping the distribution of abandoned land using a single type of remote sensing image is still challenging and problematic due to the fragmentation of such hilly areas and severe cloud pollution. In this study, a new approach by integrating Linear stretch (Ls), Maximum Value Composite (MVC), and Flexible Spatiotemporal DAta Fusion (FSDAF) was proposed to analyze the time-series changes and extract the spatial distribution of abandoned land. MOD09GA, MOD13Q1, and Sentinel-2 were selected as the basis of remote sensing images to fuse a monthly 10 m spatio-temporal data set. Three pieces of vegetation indices (VIs: ndvi, savi, ndwi) were utilized as the measures to identify the abandoned land. A multiple spatio-temporal scales sample database was established, and the Support Vector Machine (SVM) was used to extract abandoned land from cultivated land and woodland. The best extraction result with an overall accuracy of 88.1% was achieved by integrating Ls, MVC, and FSDAF, with the assistance of an SVM classifier. The fused VIs image set transcended the single source method (Sentinel-2) with greater accuracy by a margin of 10.8–23.6% for abandoned land extraction. On the other hand, VIs appeared to contribute positively to extract abandoned land from cultivated land and woodland. This study not only provides technical guidance for the quick acquirement of abandoned land distribution in hilly areas, but it also provides strong data support for the connection of targeted poverty alleviation to rural revitalization.

The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.

Analysis of Farmland Abandonment and Government Supervision Traps in China

Farmland abandonment has become relatively common in rural China. In the context of food security, the Chinese government has introduced policies for farmland abandonment supervision, but the effect of these policies has proven to be marginal. By constructing an evolutionary game model, our research explores the evolutionary logic during the supervision of farmland abandonment by governments and rural households. The results indicate that low food yield and high opportunity costs are the leading causes of farmland abandonment. The probable punishment administered by the central government for dereliction is a major motivation for the local government to practice farmland abandonment supervision. The low supervision avoidance cost for rural households leads local governments and households to form collaborations to jointly cope with central government supervision. When this occurs, local governments' supervision of farmland abandonment falls into a trap, as it leads to continued supervision practices that are costly and ineffective. Food security risk comes from the contradictory population and land resources demands. To improve food security while managing these contradictory demands, it is both necessary and feasible for the government to control population growth and focus on farmland protection, whereas it is unnecessary and unfeasible for the government to supervise whether or not farmland should be abandoned.