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The scientific and reasonable division of geographical units of cultivated land reserve resource areas is an important foundation for comprehensive land consolidation (CLC). Saline–alkali land is an important cultivated land reserve resource, and is significant for ensuring food security. This paper constructs a two-level land consolidation index system for cultivated land reserve resource areas. The Kruskal graph theory clustering algorithm was used to divide the study area into four types of ecological zones. On the basis of ecological zones, the study area was further divided into three types of consolidation units. Finally, the spatial relationship between ecological zoning and land use types was clarified, and the classification distribution of land consolidation potential was determined through an analysis of the CLC of two-level geographical units. Da'an City, Jilin province, China, was selected as the study area. The results of this study show the following: (1) In Da'an City, the conservation priority areas are concentrated in the north, the development priority areas are concentrated in the central and southern areas, and the comprehensive upgrade areas and adjustment rebuilding areas are in the transition zones. (2) The CLC potential trend is low in the north but high in the south in Da'an City. This paper proposes a framework for a geographical unit delineation method for saline–alkali-enriched cultivated land reserve resource zones, and analyzes the spatial layout of ecological protection demands and land consolidation potential in Da'an City. The results and conclusions of this study will provide a reference for CLC in cultivated land reserve resource areas. [ABSTRACT FROM AUTHOR]

Extreme drought is increasing in frequency and intensity in many regions globally. Understanding the changes in vegetation resistance and resilience under aggravated drought is essential for maintaining regional ecosystem stability. In this study, a drought event–vegetation response framework was developed to explore vegetation resistance and resilience changes. The normalized difference vegetation index (NDVI) was correlated with the standardized precipitation evapotranspiration index (SPEI) at multiple timescales to screen out the vegetation response time to drought. Then, the SPEI for the response time was detected using run theory to identify drought events during the period 2000–2017. Finally, drought-induced NDVI anomaly changes were identified using a sliding window to explore the changes in resistance and resilience to drought. This study focuses on Jilin province, China, which contains a famous environmentally vulnerable area. The results illustrate that the response time of vegetation to drought is 3 months. The northwest of Jilin province is considered to be drought-vulnerable because it has suffered the most drought events, i.e., 19–21 times, with severities in the range of 2.6–3.2 and durations in the range of 3.6–4.1 months. Grassland shows the weakest resistance and the strongest resilience, and tree cover shows the strongest resistance and the weakest resilience under severe drought disturbance among all vegetation. As the severity and duration of drought increase, the resistance decreases, and the resilience increases. During the growing season, the drought from May to July significantly impacts the vegetation resistance. Drought occurring from June to July has much less impact on resilience. Drought in August to September has less impact on resistance and a more significant impact on resilience. The results of this study may increase our knowledge regarding the response of vegetation to drought and guide ecosystem stability restoration. [ABSTRACT FROM AUTHOR]

Spatio-temporal changes in cultivated land have a profound impact on food security and sustainable development. However, existing studies on spatio-temporal changes in cultivated land mostly focus on single factors, for instance quantity, quality and ecology, that cannot comprehensively reflect the changes in total production capacity and the sustainability of cultivated land. This study aims to construct a comprehensive analysis approach and to provide a reference basis for a comprehensive analysis of the extent of changes in overall cultivated land food-production capacity and the formulation of cultivated land conservation-related policies. This comprehensive analysis method constructed from three dimensions: quantity, production capacity and ecology, fully reflects the changes in the total amount, structure, rate of change, spatial distribution, quality, total production capacity and sustainability of cultivated land. The results from the application of this approach to Long'an County, Guangxi Province, China demonstrate that: (a) from 2010 to 2020, the total amount of cultivated land in Long'an County decreased sharply by 30.83%, accounted for mainly by the conversion into orchards, forest land and other garden land; (b) the quality of cultivated land improved by 2.71% on average, mostly in relation to natural factors; (c) the total food-production capacity of cultivated land decreased by 28.96% on average, mainly due to the decrease in the area of cultivated land; (d) both the ecological grade and the sustainability of cultivated land decreased slightly; (e) the barycenter of cultivated land migrated 3.3 km to the ecologically sensitive areas in the west, and the patch size of cultivated land decreased from an average of 2.60 hectares/pc in 2010 to that of 1.34 hectares/pc in 2020, suggesting increased fragmentation of cultivated land; and (f) the patch regularity of cultivated land decreased from 2.08 in 2010 to 1.76 in 2020, showing improved patch regularity and slightly better adaptability to mechanization. There were two main reasons for the lower, total food production capacity in Long'an County: first, the low comparative income of grain cultivation, because of which farmers spontaneously adjusted the agricultural cultivation structure to pursue high returns; and second, the lack of targeted government policies to protect cultivated land. In general, this comprehensive analysis method is applicable to other provinces in China or other regions abroad to provide a reference basis for a comprehensive understanding of changes in the food production capacity of cultivated land and the formulation of policies on cultivated land protection. [ABSTRACT FROM AUTHOR]

As cultivated land quality has been paid more and more scientific attention, its connotation generalization and cognitive bias are widespread, bringing many challenges to the investigation and evaluation of regional cultivated land quality and its data analysis and mining. Establishing a systematic and interdisciplinary cognitive approach to cultivated land quality is urgent and necessary. Therefore, we explored and developed a conceptual framework of the model for the cultivated land quality analysis from the data perspective, including cultivated land quality ontology, mapping, correlation, and decision models. We identified the primary content of cultivated land quality perceptions and four cognitive mechanisms. We built vital technologies, such as the collaborative perception of the quality of cultivated land, intelligent treatment, diagnostic evaluation, and simulation prediction. Applying this analysis framework, we sorted out the frequency of indicators that characterize the function of cultivated land according to the literature in recent years and have built the cognitive system of cultivated land quality in the black soil region of Northeast China. The system's central component was production capacity and it had three components: a foundation, a guarantee, and an effect. The black soil region cultivated land quality evaluation system has seven purposes involving 20–31 key indicators: production supply, threat control, farmland infrastructure regulation, cultivated land ecological maintenance, economics, social culture, and environmental protection. In various application contexts, the system had many critical supporting technologies. The results demonstrate that the framework has strong adaptability, efficiency, and scalability, which might offer a theoretical direction for further studies on the evaluation of the quality of cultivated land in the area. The analysis framework established in this study is helpful to deepen the understanding of cultivated land quality systems from the perspective of big data. Taking the big data of cultivated land quality as the driving force, combined with the technical methods of cultivated land quality analysis, the evaluation results of cultivated land quality under different scenarios and different objectives are optimized. In addition, the framework can serve the practice of farmland management and engineering improvement, adapt to the management needs of different objects and different scales, and achieve the combination of theory and practice. [ABSTRACT FROM AUTHOR]

A Semivariogram, as defined in geostatistics, is a powerful tool for texture extraction of remotely sensed images. However, the traditional texture features extracted by a semivariogram are generally for pixel-based classification. Moreover, most studies have been based on the original computation mode of semivariogram and discrete semivariance values. This article describes a set of semivariogram texture features (STFs) based on the mean square root pair difference (SRPD) to improve the accuracy of object-oriented classification (OOC) in QuickBird images. The adaptive parameters for the calculation of a semivariogram were first derived from semivariance analysis, including directions, moving window size, and lag distance. Then, 22 STFs were extracted from the discrete and mean/standard deviation semivariance, and 15 features were selected from the extracted STFs based on feature optimization. Then five grey-level co-occurrence matrix (GLCM) texture features (mean, homogeneity, contrast, angular second moment, and entropy) were calculated based on segmented image objects using the panchromatic band. A comparison of classification results demonstrates that the STFs described in this article are useful supplement information for the spectral OOC, and the spectral + STFs classification method can be used to obtain a higher classification accuracy than can the combination of spectral and GLCM features. [ABSTRACT FROM AUTHOR]

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China's commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited. [ABSTRACT FROM AUTHOR]

Identifying ecologically vulnerable areas and understanding the responses of phenology to negative changes in vegetation growth are important bases for ecological restoration. However, identifying ecologically vulnerable areas is difficult because it requires high spatial resolution and dense temporal resolution data over a long time period. In this study, a novel method is presented to identify ecologically vulnerable areas based on the normalized difference vegetation index (NDVI) time series from MOD09A1. Here, ecologically vulnerable areas are defined as those that experienced negative changes frequently and greatly in vegetation growth after the disturbances during 2000–2018. The number and magnitude of negative changes detected by the Breaks for Additive Season and Trend (BFAST) algorithm based on the NDVI time-series data were combined to identify ecologically vulnerable areas. TIMESAT was then used to extract the phenology metrics from an NDVI time series dataset to characterize the vegetation responses due to the abrupt negative changes detected by the BFAST algorithm. Focus was given to Jilin Province, a region of China known to be ecologically vulnerable because of frequent drought. The results showed that 13.52% of the study area, mostly in Jilin Province, is ecologically vulnerable. The vulnerability of trees is the lowest, while that of sparse vegetation is the highest. The response of phenology is such that the relative amount of vegetation biomass and length of the growing period were decreased by negative changes in growth for dense vegetation types. The present research results will be useful for the protection of environments being disturbed by regional ecological restoration. [ABSTRACT FROM AUTHOR]

Thermal remote sensing provides a method to describe spatial heterogeneity of the "urban heat island" effect and to evaluate the function of temperature regulation. Rapid urbanization and heatwave events with increasing frequencies need a quantitative analysis on the supply and demand for an urban temperature regulating service, which is a gap in urban heat island (UHI) studies in rapidly urbanizing cities. In order to study the quantitative relationship between landscape metrics (including area index and shape index) and temperature regulating service, this study applied a temperature regulating service in an urban thermal environment study based on the "source–sink" landscape theory in western Shenzhen in different periods. The identification of source and sink landscapes is based on the spatial relationship of unusual surface features derived from Landsat-5 and -8 and the consideration of the temperature difference. We found that the source landscapes at different periods provide temperature regulating services for different distances, which directly lead to the difference between the theoretical service value based on the Alternative Cost Method and the actual service value considering demand, changing in the same trend. The results show that the supply distance of temperature regulating services in 2005, 2010, and 2013 is 150 m, 180 m, and 210 m, respectively. The temperature regulating service value is 3.043, 3.273, and 4.308 billion yuan in 2005, 2010, and 2013, which is lower than the estimation value without considering supply and demand (16.638, 23.728, and 37.495 billion yuan, respectively). The value of the temperature regulating service has a positive correlation with the increase of the patch area index. With the gradual complexity of the shape, the service value increases first and then decreases. Moreover, the landscapes with the smallest shape index and area index have the shortest distance for service supplying. The assessment of the temperature regulating service needs to consider the presence of demand landscapes. Furthermore, the interaction of landscapes under different conditions requires further consideration. The setting of the cooling landscape shape and area for mitigating the "urban heat island" effect can provide references to urban planners and policymakers in the practice of urban climate adaptation and regulation. [ABSTRACT FROM AUTHOR]

The extraction and evaluation of crop production units are important foundations for agricultural production and management in modern smallholder regions, which are very significant to the regulation and sustainable development of agriculture. Crop areas have been recognized efficiently and accurately via remote sensing (RS) and machine learning (ML), especially deep learning (DL), which are too rough for modern smallholder production. In this paper, a delimitation-grading method for actual crop production units (ACPUs) based on RS images was explored using a combination of a mask region-based convolutional neural network (Mask R-CNN), spatial analysis, comprehensive index evaluation, and cluster analysis. Da'an City, Jilin province, China, was chosen as the study region to satisfy the agro-production demands in modern smallholder areas. Firstly, the ACPUs were interpreted from perspectives such as production mode, spatial form, and actual productivity. Secondly, cultivated land plots (C-plots) were extracted by Mask R-CNN with high-resolution RS images, which were used to delineate contiguous cultivated land plots (CC-plots) on the basis of auxiliary data correction. Then, the refined delimitation-grading results of the ACPUs were obtained through comprehensive evaluation of spatial characteristics and real productivity clustering. For the conclusion, the effectiveness of the Mask R-CNN model in C-plot recognition (loss = 0.16, mean average precision (mAP) = 82.29%) and a reasonable distance threshold (20 m) for CC-plot delimiting were verified. The spatial features were evaluated with the scale-shape dimensions of nine specific indicators. Real productivities were clustered by the incorporation of two-step cluster and K-Means cluster. Furthermore, most of the ACPUs in the study area were of a reasonable scale and an appropriate shape, holding real productivities at a medium level or above. The proposed method in this paper can be adjusted according to the changes of the study area with flexibility to assist agro-supervision in many modern smallholder regions. [ABSTRACT FROM AUTHOR]

The spatiotemporal change characteristics of Cultivated Land Productivity (CLP) are imperative for ensuring regional food security, especially given recent global warming, social development and population growth. Based on the hypothesis that the Gross Primary Productivity (GPP) is a proxy of land productivity, the Moderate Resolution Imaging Spectroradiometer (MODIS) data with 500-m spatial resolution and 8-day temporal resolution was employed by the Vegetation Photosynthesis Model (VPM) to calculate GPP in Jilin Province, China. We explored the level of CLP using the GPP mean from 2000 to 2018, and analyzed the changing trend and amplitude of CLP in the whole study period using both Theil–Sen median trend analysis and the Mann–Kendall (MK) test, and forecasted the sustainability of CLP with the Hurst exponent. The trend result and the Hurst exponent were integrated to acquire the future direction of change. The results revealed that: (1) The CLP level was generally high in the southeast and low in the northwest in cultivated land in Jilin, China. The area with the lowest productivity, located in the northwest of Jilin, accounted for 15.56%. (2) The majority (84.77%) of the area showed an increasing trend in 2000–2018, which was larger than the area that was decreasing, which accounted for 3.97%. (3) The overall change amplitude was dominated by a slightly increasing trend, which accounted for 51.48%. (4) The area with sustainability accounted for 33.45% and was mainly distributed in the northwest of Jilin. The area with anti-sustainability accounted for 26.78% and was mainly distributed in the northwest and central Jilin. (5) The Hurst exponent result showed that uncertain variation of CLP is likely to occur in the future over the entire region, and the central region is prone to display degeneration. Therefore, the results of this study indicated that quality improvement policy could be implemented for the middle-to-low yield fields in northwest Jilin, and dynamic monitoring and protection measures could be implemented for the areas with uncertain future changes and decreasing sustainability. [ABSTRACT FROM AUTHOR]

Agriculture sustainability assessment is conducive to promoting sustainable agriculture construction and sustainable development. Modern agriculture and modern small-peasant production have different requirements for agriculture sustainability. Related studies provided assessment frameworks for many parts of the world. However, existing frameworks have distinct limitations and are not applicable to modern small-peasant economy (MSE) areas, such as China. The purpose of this study is regarding China as an example to construct a healthy farmland system assessment framework (HFSAF), to assess smallholder farmland systems' sustainability. HFSAF's theoretical basis, indicator system, data preparation methods, multi-level aggregation rule and results description method are presented in this paper. HFSAF is a multi-level indicator system with adjustable parameters, covering environment, economy and society aspects, including three dimensions, nine visions, 15 themes and 40 basic indicators. Taking Da'an City, Jilin Province, China as the study area to implement HFSAF. The assessment results prove HFSAF can be used to assess agricultural sustainability in MSE areas with limited agro-resource supplies, to assist the sustainable decision-making and regional agriculture remold. [ABSTRACT FROM AUTHOR]