Cropland Change Research Articles

Influencing factors of the supply-demand relationships of carbon sequestration and grain provision in China: Does land use matter the most?

The provision of ecosystem services (ESs) such as carbon sequestration and grain provision are critical components to sustainable development. Reaching carbon neutrality generally requires the growing carbon sequestration of forest land, and feeding a growing population needs an expansion of cultivated land. However, limited land resources may lead to a contradiction between the carbon sequestration and grain provision. China has proposed long-term and large-scale land use programs, and exploring whether these land use policies are effective for ES sustainable provision would be instructive for future policy implications. This study integrated multi-source data in the socioecological dimension to determine the extent by which land use and land use change influence the supply-demand mismatches of carbon (carbon sequestration and emission) and grain (grain provision and consumption) in China at the provincial level. The result showed that the total quantity of carbon emissions surpassed carbon sequestration and the grain provision could cover the consumption from 2000 to 2015. Spatially, southeastern coastal provinces had higher grain deficits and northeast provinces had higher carbon deficits. This study further detected the influencing factors of the mismatches between the supply and demand of the two ESs. Excluding land use factors, our results showed that social factors contributed 38% and 47% to the supply-demand mismatches of carbon and grain, respectively, and natural factors contributed 39% and 15%, respectively. During 2000–2005, 2005–2010, and 2010–2015, cropland changes significantly affected grain balance, while forest land changes did not significantly affect carbon balance. These results indicated that cropland protections are vital to food safety, and carbon emission reductions should be the focus for carbon balance. Finally, this study makes policy suggestions for land use and ecosystem management, and a future research framework was proposed to help mitigate ES supply-demand imbalance.

Spatial and Temporal Characteristics and Driving Force Analysis of Cropland Change: Taking Dangyang City, Yichang City as Example

With the land use data of Dangyang City, Yichang City, Hubei Province in 2000, 2010, and 2020, 13 factors of nature, economy, and society were selected as the driving factors affecting the change of Cropland by using the land-use transition matrix and the kernel density analysis. In this study, the dynamic degree model of cropland use was introduced, combined with the geographic detector method, to explore the spatiotemporal variation characteristics and main driving factors of cropland in various towns in Dangyang City from 2000 to 2020. The results showed that the distribution of cropland generally showed a decreasing trend from southeast to northwest. From 2010 to 2020, the cropland in Dangyang City changed significantly, and the cropland area decreased. Economic and social factors turn out to be the main factors affecting the distribution of cropland. When interacting with other factors, the impact on cropland is weakened, and natural factors have interactive effects on cropland.

Analysis of Land Use Change and Driving Mechanisms in Vietnam during the Period 2000–2020

High-accuracy, long-time-series and large-scale land classification mapping are essential for assessing the evolutionary patterns of land systems and developing sustainability studies. In this paper, using Google Earth Engine (GEE) and Landsat satellite remote sensing images, based on the Random Forest (RF) algorithm, we carried out remote sensing classification to obtain a year-by-year land use/cover data set in Vietnam over the past 21 years (2000–2020). Further applying principal component analysis and multiple linear regression methods, we examined the spatio-temporal characteristics, dynamic changes and driving mechanisms of land use change. The results show the following: (1) The RF classification algorithm supported by the GEE can quickly and accurately obtain a land use/cover data set. The overall classification accuracy is 0.91 ± 0.01. (2) The land cover types in Vietnam are dominated by woodland and cropland, with an area share of 54.62% and 37.90%, respectively. In the past 20 years, the area of built-up land has increased the most (+93.49%), followed by the area of water bodies (+54.19%), while the area of woodland has remained almost unchanged. (3) The expansion of built-up land is driven by regional economic development; the area changes in cropland, water bodies and woodland are influenced by both national economic development and climate change. The results of the study provide a basis for assessing land use policies in Vietnam and a reference methodological framework for rapid land mapping and analysis in other countries in the China–Indochina Peninsula.

Reconstructing cropland change since 1650 AD in Shaanxi province, central China

Regional reconstructions of historical cropland change are essential to improve the accuracy of global datasets. In Shaanxi province, central China, frequently affected by rebellions and natural disasters over the past 300 years, the cropland area fluctuated. In this study, based on local gazetteers, statistics, and survey data, systematic steps are followed to reconstruct the cropland area change since 1650 in Shaanxi province. The result shows four stages of changes in the total cropland area: steady increase during 1650–1830 stimulated by policies and population growth, decrease during 1830–1937 affected by wars and natural disasters, rapid growth during 1937–the 1980s, and rapid decrease after the 1980s influenced by urbanization and the agricultural planting structure adjustment. The total cropland area increased from 2.2 × 104 km2 in 1650 to 3.99 × 104 km2 in 2016. The majority of the cropland concentrated on the Guanzhong region from 1650 to 2016. The cropland expanded from the center to the surroundings in the Guanzhong region, from northeastern to southwestern in northern Shaanxi, and from western to eastern in southern Shaanxi before the 1980s. Cropland area declined in all three regions after the 1980s. The comparisons show most cropland area data in the Qing dynasty from the existing regional studies are still highly dependent on the registered tax-paying cropland area. Without considering agricultural history, changes in the SAGE cropland in Shaanxi province over the past 300 years are irrational. Spatially, HYDE 3.2 dataset cannot show the “real” spatial distribution of the cropland before 1930, because too much cropland is allocated to northern and southern Shaanxi.

Disentangling the effect of climate and cropland changes on the water performance of agroecosystems (Spain, 1922–2016)

The main type of consumptive water use is crop evapotranspiration. The historical evolution of crop evapotranspiration depends on climate and cropland changes. These two latter variables present complex interactions and are expected to continue changing in the future, but the coupling between these two processes is insufficiently addressed in the literature. The objective of this study is to disentangle the impact of historical climate and cropland changes on four water performance indicators of agroecosystems in a Mediterranean country (Spain) between 1922 and 2016: crop water requirements actual evapotranspiration, the net primary productivity-based water intensity and violet water, accounting for water stress. These indicators were estimated based on soil water balances and the effects of climate and cropland were unravelled through counterfactual scenarios. The results showed that climate change tended to increase actual evapotranspiration (9%), crop water requirements (14%) and net primary productivity-based water intensity (8%), its greatest impact being on violet water (increasing it by 34%). The cropland variable produced effects of different positive or negative signs according to the parameters considered (type of crop, crop management and point in space-time). In aggregate terms, however, the cropland effect pushed in the same direction as climate change, causing increases in actual evapotranspiration (11%), violet water (15%) and crop water requirements (3%), while reducing net primary productivity-based water intensity (−15%). This approach allows us to quantify and show the importance of agricultural industrialization on the water performance of agroecosystems. In this way, our results highlight great opportunities to manoeuvre to adapt agriculture to climate change through agronomic management and hydrological planning options. Complex interaction patterns between climate and cropland effects were shown. Moreover, geographical, crop-type and temporal evapotranspiration hotspots and drivers were uncovered, and interrelations among the water performance indicators were discussed, thus raising relevant points of discussion in the field.

Impacts of inter-annual cropland changes on land surface temperature based on multi-temporal thermal infrared images

The research on the impact of land cover changes on the thermal environment is crucial to environmental monitoring. Land surface temperature (LST) is often used as the main parameter to analyze the thermal environment. As the primary land cover type, “cropland” is the main factor affecting the LST. Previous studies investigated the effects of cropland with other land cover types on the LST but ignored the changes within cropland. Furthermore, inter-annual changes of crop types in the cropland often occur. With the growth of the crops, their impact on LST also change. Therefore, it is necessary to understand how cropland changes affect the LST. The traditional comparison method based on a single time image is not suitable for evaluating the impact of the different crops during the whole growth period because single-phase information cannot reflect the changing process of crop influence. To address this issue, this paper proposes a cumulative land surface temperature index (cLST) based on multi-temporal images to evaluate the yearly impact of cropland changes. Results indicate that different crops have different effects on the thermal environment, and the annual cLST of cotton extracted from MODIS is higher than that of corn, sorghum, and winter wheat cropland. Also, the changing amplitude of the annual cLST is mainly related to the inter-annual cropland area ratio change (R = 0.85 for winter wheat to cotton, R = 0.75 for corn to cotton) in the study area, indicating that the overall change from corn and winter wheat to cotton cropland increases the annual cLST. The effect of cotton-sorghum and winter wheat-corn change is not significant. By comparing the contribution rate, it is found that the annual cLST is more affected by the cotton-winter wheat change (35%) than air temperature (26%). The method proposed in this paper helps to reveal the impact of cropland changes on LST, and the prediction of regional climate changes.

A CNN-Transformer Network With Multiscale Context Aggregation for Fine-Grained Cropland Change Detection

Non-agriculturalization incidents are serious threats to local agricultural ecosystem and global food security. Remote sensing change detection (CD) can provide an effective approach for in-time detection and prevention of such incidents. However, existing CD methods are difficult to deal with the large intra-class differences of cropland changes in high-resolution images (HRIs). In addition, traditional CNN based models are plagued by the loss of long-range context information, and the high computational complexity brought by deep layers. Therefore, we propose a CNN-transformer network with multi-scale context aggregation (MSCANet), which combines the merits of CNN and transformer to fulfill efficient and effective cropland change detection. In the MSCANet, a CNN-based feature extractor is first utilized to capture hierarchical features, then a transformer-based multi-scale context aggregator (MSCA) is designed to encode and aggregate context information. Finally, a multi-branch prediction head (MBPH) with three CNN classifiers is applied to obtain change maps, to enhance the supervision for deep layers. Besides, for the lack of change detection dataset with fine-grained cropland change of interest, we also provide a new cropland change detection dataset (CLCD), which contains 600 pairs of 512×512 bi-temporal images with the spatial resolution of 0.5-2m. Comparative experiments with several CD models prove the effectiveness of the MSCANet, with the highest F1 of 64.67% on the high-resolution semantic change detection dataset (HRSCD), and of 71.29% on CLCD. Code and dataset in the paper will be available for download from the following link https://github.com/liumency/CropLand-CD.

Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century

Spatiotemporally consistent data on global cropland extent is essential for tracking progress towards sustainable food production. In the present study, we present an analysis of global cropland area change for the first two decades of the twenty-first century derived from satellite data time-series. We estimate that, in 2019, the cropland area was 1,244 Mha with a corresponding total annual net primary production (NPP) of 5.5 Pg C year−1. From 2003 to 2019, cropland area increased by 9% and cropland NPP by 25%, primarily due to agricultural expansion in Africa and South America. Global cropland expansion accelerated over the past two decades, with a near doubling of the annual expansion rate, most notably in Africa. Half of the new cropland area (49%) replaced natural vegetation and tree cover, indicating a conflict with the sustainability goal of protecting terrestrial ecosystems. From 2003 to 2019, global per-capita cropland area decreased by 10% due to population growth. However, the per-capita annual cropland NPP increased by 3.5% as a result of intensified agricultural land use. The presented global, high-resolution, cropland map time-series supports monitoring of natural land appropriation at the local, national and international levels.

Agricultural landscape change impact on the quality of land: An African continent-wide assessment in gained and displaced agricultural lands

Agricultural land area is increasing globally despite the loss of productive agricultural lands in some world regions. We examine the case of Africa where the knowledge about major agricultural land transitions and the impacts on the quality of land is still very limited. A comprehensive assessment of change in agricultural landscapes was conducted at the African continental scale. We identify influencing factors and model the quality of land associated with agricultural land gains and losses between 2000 and 2018. Land quality was established based on spatially-explicit analysis of varying Net Primary Productivity, soil organic carbon content, crop suitability and percent yield change for major crops of global importance grown across Africa such as maize, rice, soybean, wheat, and alfalfa. Distance to settlements was important in explaining agricultural land dynamics. Most land areas that transitioned to cropland in Africa were associated with large distances away from major roads. Poor access to major roads suggests the remoteness of gained croplands. Land quality was better in gained croplands than in those lost, whereas gained grasslands were of lesser quality compared to areas of grassland loss. Five typologies of African countries were developed based on net yield and amount of land cultivated per crop in cropland change areas. Type 1 typifies net yield increase and cultivated land decrease, while type 2 is characterized by yield increase consequent upon cropland expansion. Net yield and land remain unchanged in type 3, while in type 4 cultivated land increased but yield decreased as in 40% of African countries for maize, and in type 5, both yield and land area decreased. This study thus provides evidence about the quality of land in gained and lost agricultural areas and generalizable insights on their dynamics across Africa.

Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China

As a form of land marginalization, abandoned cropland has an important impact on food security and the effective supply of agricultural products. With rapid urbanization across the world, large areas of cropland are abandoned in some regions, especially in mountainous and hilly areas with poor terrain. Due to the fine fragmentation and scattered distribution of abandoned cropland, and considering differences in the abandoned and fallow time of cropland, it is difficult to extract information using remote sensing technology. Therefore, this paper proposes a change in the detection method for extracting abandoned cropland information based on identifying the annual land use trajectory. Based on Landsat satellite data, annual land use was mapped from 2011 to 2020 in Gaolan County, which is located in the hilly and gully region of the Loess Plateau of China, using the random forest classification method. Subsequently, abandoned cropland information in Gaolan County was extracted, based on the land use change trajectory and analysis of the influencing factors of abandoned land. The results showed that: (1) The overall accuracy of land use interpretation in Gaolan County ranged from 86.44% to 95.45%, from 2011 to 2020, with a kappa coefficient of up to 0.93, and the classification results were ideal. (2) The recall of extracted abandoned cropland was 81%, the extraction accuracy of which was relatively high. (3) From 2013 to 2020, the cropland abandonment rate in Gaolan County ranged from 8.41% to 19.65%, with an average of 14.55%, which increased and then decreased. The abandonment rate was highest in 2015 but it then decreased year by year. The average period of abandoned cropland was 4.2 years. (4) The influence factors of the plot scale explain the difference in the spatial distribution of cultivated land abandonment. The higher the slope condition, the lower the soil nutrient content and the greater the possibility of abandonment.

Spatially Explicit Reconstruction of Cropland Using the Random Forest: A Case Study of the Tuojiang River Basin, China from 1911 to 2010

A long-term, high-resolution cropland dataset plays an essential part in accurately and systematically understanding the mechanisms that drive cropland change and its effect on biogeochemical processes. However, current widely used spatially explicit cropland databases are developed according to a simple downscaling model and are associated with low resolution. By combining historical county-level cropland archive data with natural and anthropogenic variables, we developed a random forest model to spatialize the cropland distribution in the Tuojiang River Basin (TRB) during 1911–2010, using a resolution of 30 m. The reconstruction results showed that the cropland in the TRB increased from 1.13 × 104 km2 in 1911 to 1.81 × 104 km2. In comparison with satellite-based data for 1980, the reconstructed dataset approximated the remotely sensed cropland distribution. Our cropland map could capture cropland distribution details better than three widely used public cropland datasets, due to its high spatial heterogeneity and improved spatial resolution. The most critical factors driving the distribution of TRB cropland include nearby-cropland, elevation, and climatic conditions. This newly reconstructed cropland dataset can be used for long-term, accurate regional ecological simulation, and future policymaking. This novel reconstruction approach has the potential to be applied to other land use and cover types via its flexible framework and modifiable parameters.

Impact of Cropland Reclamation on Ecological Security in the Yangtze River Economic Belt, China

Ecological security is important both for maintaining the function of an ecosystem and for providing ecosystem services to the human wellbeing. The impact of land use change/cover on ecological security has attracted considerable attention, whereas the role of cropland reclamation remains unclear. The indirect loss of ecological land that occurs upon the request of cropland requisition-compensation policies offer further changes to ecological security. In order to ascertain the impact of cropland reclamation on ecological security, in this study three scenarios are established, addressing cropland returning to ecological lands without a slope limitation, with a slope <25°, and with a reclaimed cropland slope ≥25°. This study was conducted in the Yangtze River economic belt (YREB) due to its important contribution to ecological security in China. Land uses in different scenarios in 2030 are projected using the land use simulation model LANDSCAPE. Accordingly, ecological security in each scenario was evaluated using the contribution–vigour–organization–resilience framework, comprising the variables carbon storage, water purification, water yield, habitat quality, net primary productivity, mean patch area, Shannon’s diversity index, largest patch index and contagion, as well as the normalized difference vegetation index. The results indicate that about 62% of YREB land is projected to remain stable in terms of ecological security, while about 21% will deteriorate and 17% will improve between 2015–2030. Land where ecological security is projected to improve is concentrated in areas where broad and connected croplands are distributed. The fact that a higher proportion of areas will deteriorate than improve suggests that the negative impact of cropland change on ecological security should not be ignored. Comparing different scenarios, croplands returning to ecological lands pose a particularly significant impact on ecological security, particularly in the upper reaches of the YREB, where steep croplands are concentrated.

A novel organic carbon accumulation mechanism in croplands in the Yellow River Delta, China

The trends and mechanisms of organic carbon changes in coastal delta croplands are not yet clear due to the complexity physicochemical processes in soil. In this study, combing powder x-ray diffraction (XRD), microbial analysis, and density functional theory in quantum mechanics, we proposed a novel mechanism underlying OC accumulation. We investigated changes of three kinds of organic carbon (OC)-dissolved organic carbon (DOC), light fraction organic carbon (LFOC), and heavy fraction organic carbon (HFOC) in the Yellow River Delta croplands. We found that HFOC, dominant in coastal delta cropland soil, formed at different ages and its density increased with increasing reclamation time. Yet, DOC and LFOC had no significant increase or decrease tendency. Moreover, in coastal delta croplands, HFOC accumulation might be a complex progress, including the loss of indigenous OC and the accumulation of newly input OC. Based on these results, we proposed that although root exudative DOC (organic acids) could cause the indigenous OC loss by forming a specific microbial community, it still was a source of HFOC and promoted the OC accumulation. More importantly, based on density functional theory, we verified that these root exudative organic acids could adsorb on SiO2 together with crystalline Fe oxides (Fec) to form aggregates. The finding could explain the phenomenon that the XRD results showed samples were compounds of SiO2, Fec, and OC and the accumulation of HFOC in coastal delta croplands. By revealing a new OC accumulation mechanism in coastal delta croplands, this study provides novel insights into the mechanism of OC dynamics in coastal delta croplands and the global carbon budget.

Spatiotemporal Analysis of Size and Equity in Ownership Dynamics of Agricultural Landholdings in India Vis-à-Vis the World

The increasing threat to sustainable agriculture is a major concern of planners worldwide. Human population growth together with increasing food requirements and competition for land use is leading to land scarcity for agricultural purposes. Farm size influences the extent of the adoption of mechanization and modern methods of farm management practices, which in turn results in increased productivity, production efficiency and agricultural income. We studied changes in macroeconomic factors such as dependency on agriculture, growth of the sector, the pattern of landholdings and tenure rights across major agriculturally important countries, as well as the priority of agriculture for the national economy (i.e., the share of agriculture in the national income) and its relationship to changes in farm size. The data on the percentage of area under farming, population growth, size of the agricultural workforce and other social dimensions from 24 countries of different geographical sizes were analysed. We used parameters such as the extent of changes in cropland, family-owned land, the agricultural workforce and their productivity, number of holdings and their distribution, women-headed holdings and finally total and per capita agricultural income, and measured the changes over time and space. The published data from national and international sources were used to establish the relationship between farm size and farm efficiency measured through the selected parameters. The results clearly establish that the size of farm holdings had an inverse relationship with the population dependent on agriculture, share of agriculture in national income and tenure rights. Australia had the largest average agricultural landholding (3243 ha), while India and Bangladesh had the lowest (1.3 and 0.3 ha, respectively). The inequality in the distribution of farmland ownership was greater in developed countries than in developing countries. Female farmland ownership was less than 20% in most developing countries and the relationship between the number of farm households and farm outcomes was found to have weakened over time. India, a developing as well as an agriculturally important country, was subjected to detailed analysis to understand the spatiotemporal dynamics of the size, distribution and ownership patterns of agricultural landholding.

The spatiotemporal change of cropland and its impact on vegetation dynamics in the farming-pastoral ecotone of northern China

Due to the unfavorable soil conditions and water resources, the cropland use pattern in the farming-pastoral ecotone in northern China is complex. The program named “Grain for Green” has accelerated the cropland change. However, the complex cropland and retired cropland are challenging to monitor with remote sensing due to their spatially dispersed and easily confused with spectrally similar land use classes such as nature grasslands and non-cropped fields. Taking farming-pastoral ecotone in the northern foot of the Yinshan Mountains as a case study, we explored a classification approach for complex cropland and retired cropland, which was introduced as a specific land use class by using multi-temporal Landsat TM and OLI images with Google Earth Engine. During 1990–2000, cropland increased with a sharper growth and increased with a slower growth from 2001 to 2010, and then decreased significantly from 2011 to 2019, to lead the cropland area in 2019 was smaller than an area in 1990. We analyzed the spatiotemporal trajectories of retired cropland in 2019 using the Land Use Change Trajectory method to evaluate its source. In our finding, approximately 77% of retired cropland was labelled as cropland before 2019; albeit, not all retired cropland was converted from cropland. Moreover, we qualitatively assessed the vegetation dynamics in the study area by utilizing the long-term NDVI-mean value to reveal that vegetation coverage has shown a continuously increasing trend. It is related to the decline of cropland and the increase of retired cropland at the same rate. Our results highlighted that the “Grain for Green” program had led the vegetation restoration in the farming-pastoral ecotone. Our approach for monitoring cropland and retired cropland can improve the understanding of the driving factors and consequences of these critical land use change trajectories.

Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea

North Korea being one of the most degraded forests globally has recently been emphasizing in forest restoration. Monitoring the trend of forest restoration in North Korea has important reference significance for regional environmental management and ecological security. Thus, this study constructed and analyzed a time-series land use land cover (LULC) map to identify the LULC changes (LULCCs) over extensive periods across North Korea and understand the forest change trends. The analysis of LULC used Landsat multi-temporal image and Random Forest algorithm on Google Earth Engine(GEE) from 2001 to 2018 in North Korea. Through the LULCC detection technique and consideration of the cropland change relation with elevation, the forest change in North Korea for 2001–2018 was evaluated. We extended the existing sampling methodology and obtained a higher overall accuracy (98.2% ± 1.6%), with corresponding kappa coefficients (0.959 ± 0.037), and improved the classification accuracy in cropland and forest cover. Through the change detection and spatial analysis, our research shows that the forests in the southern and central regions of North Korea are undergoing restoration. The sampling method we extended in this study can effectively and reliably monitoring the change trend of North Korea forests. It also provides an important reference for the regional environmental management and ecological security in North Korea.

Spatio-temporal analysis of cropland change in the Guanzhong area, China, from 1650 to 2016

Spatio-temporal analysis of cropland change in the Guanzhong area, China, from 1650 to 2016

The relationships between economic growth and cropland changes in Bangladesh: An evidence based on annual land cover data

Bangladesh is experiencing rapid economic progress since 1990s, at the same time, facing acute shortage of agricultural land. Using 19 years (2000-2018) time series remote-sensing data and Random Forest machine learning algorithm in Google Earth Engine (GEE), this study classifies the land covers in Bangladesh; determines the trend of land cover changes using least-square growth rate model; and finds the associations between the trends of cropland and growth of socio-economic factors using correlation and regression analysis. The annual average area of cropland decline is 29271 hectare, which is higher than the absolute changes of other major land covers, such as forest (25932 hectare) and built-up area (1649 hectare). With the annual growth of urban population (3.81%) and gross domestic product (GDP) (6.28%), the predicted declining rate of cropland is estimated 0.29%, which is very close to the observed annual declining rate (0.28%). However, the partial effects of urban population and GDP could not be detected. Therefore, the study infers that urbanization and economic growth in Bangladesh are happening simultaneously. As a result, the joint determination of both phenomena can explain the degradation of agricultural land in Bangladesh.

ASSESSING CROPLAND ABANDONMENT FROM VIOLENT CONFLICT IN CENTRAL MALI WITH SENTINEL-2 AND GOOGLE EARTH ENGINE

Abstract. The proposed analysis based on Sentinel-2 imagery provides evidence of impacts of the conflict in the Mopti region (central Mali), which has led to widescale cropland abandonment. This area is characterized by rapidly rising levels of violence since 2018, due to the presence of armed groups and the proliferation of self-defence militias. This study investigates how high-resolution optical imagery can help evaluate the linkages between violence and land cover / land use (LCLU) change. The processing environment of Google Earth Engine was used to generate the so-called 3-Period TimeScan (3PTS) product, a RGB composite combining the maximum NDVI values in the beginning, in the middle and in the end of the growing season, used to single out cultivated land for each year of interest. Theoretically, the period between June 15th and October 15th covers an annual agricultural cycle for the considered area; consequently, images acquired during that period were used to generate the 3PTS composites for the year of interest (2019) and for pre-conflict years. By comparing the situations before and after the start of the crisis, each populated site was categorized according to the degree of cropland change detected in its surroundings. The resulting overview map enables a regional-scale interpretation of farming activities in 2019, clearly highlighting localized areas of cropland abandonment in the region and showing a strong spatial correlation with incidence of conflict.

Land-Use and Land-Cover Changes in Dong Trieu District, Vietnam, during Past Two Decades and Their Driving Forces

Land-use and land-cover (LULC) change analyses are useful in understanding the changes in our living environments and their driving factors. Modeling changes of LULC in the future, together with the driving factors derived through analyzing the trends of past LULC changes, bring the opportunity to assess and orientate the current and future land-use policies. As the entryway of Quang Ninh province, Vietnam, Dong Trieu locale has experienced significant LULC changes during the past two decades. In this study, the spatial distribution of six Level I LULC classes, forest, cropland, orchards, waterbody, built-up, and barren land, in Dong Trieu district at 2000, 2010, and 2019 were obtained from Landsat imageries by maximum likelihood technique. The most significant changes observed over the past twenty years are a decrease of barren land (9.1%) and increases of built-up (8.1%) and orchards (6.8%). Driving factor analysis indicated that the changes of cropland and built-up were dependent on distance from road (DFR), distance from main road (DFMR), distance from urban (DFU), distance from water (DFW), elevation, slope, and population density. The changes of forest were dependent on all the driving forces listed above, except DFMR. The orchards mainly appeared near the high-population-density area. The transformation of the waterbody was affected by geography (elevation and slope) and population density. The higher the population density, the less barren the land would appear.