Large-scale Land Research Articles

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization.

Indonesia’s forest management progress: empirical analysis of environmental Kuznets curve

Purpose. High dependence on land resources is often the main cause of large-scale land conversion and deforestation in Indonesia. However, as a country vulnerable to climate change, Indonesia continues to increase its efforts to improve forest management and reduce deforestation. This is also in line with Indonesia’s commitment to reduce emission levels to achieve Net-Zero Emissions by 2060, where the forestry sector will be the base of this achievement. This study aims to measure Indonesia’s progress during 1970–2018, especially in reducing deforestation. With this study, we can see trends in forest management in developing countries, especially Indonesia. In addition, based on available data, we can see which factors are most closely related to Indonesia’s deforestation rate.
 Methodology / approach. This study uses a time-series data analysis with Error Correction Method (ECM). This ECM is used to detect the existence of the Environmental Kuznets Curve (EKC), which describes the relationship between environmental degradation and economic variables. In this study, EKC is used to assess further relationship pattern between the Gross Domestic Product (GDP) per capita and deforestation. The relation pattern between this GDP and deforestation is tested using quadratic and cubic models.
 Results. Under the quadratic model, a classic inverted U-shape EKC is detected in this study. It means that with economic growth and technological improvement, deforestation can be reduced. In other words, Indonesia has improved its forest management. However, as the N-shape relation was also detected in further cubic model simulation, it is also an indication that, at some point, Indonesia is also prone to increase its deforestation again. Another finding from the model is that the palm oil plantation area strongly relates to the deforested area. At the same time, the simulation detects a negative relationship between the horticulture cropland and the deforested area. The result may capture an insight into the forest transition period in Indonesia. In this transition, the government has started to slow down the speed at which forests are exploited in various ways.
 Originality / scientific novelty. Although the forestry sector is an important economic sector for Indonesia, there is still limited research on this topic, especially at the national level. This study uses more updated statistical information to better describe the Indonesian forestry condition. Moreover, the analysis is aligned with the Indonesian land policy by introducing additional variables from palm oil and crop areas. Moreover, the previous EKC assessments in Indonesia usually only use the quadratic model, which has some limitations in detecting other turning points and seeing the existence of N-shaped or inverted N-shaped EKC. This study also tries to add a bit on cubic model assessment to detect this N-shaped EKC in Indonesia.
 Practical value / implications. This study gives broader information based on the available data and statistics on the country’s deforestation situation and how the economic situation affects it. Policymakers can use the materials from this study to develop an effective forest management system throughout Indonesia.

An ultrahigh-resolution E3SM land model simulation framework and its first application to the Seward Peninsula in Alaska

The availability of supercomputers and state-of-science datasets has made it possible to conduct large-scale land simulations at an ultrahigh-resolution. This study reported a computational framework for land surface simulation using the E3SM land model (ELM) at an unprecedented resolution (1 km x 1 km gridcell). The ultrahigh-resolution ELM (uELM) simulation framework includes three parts: (1) high-resolution atmospheric forcing and surface properties dataset generation, (2) massive gridcell-based simulation, and (3) large-scale simulation results analysis. We implemented the uELM simulation framework and completed the first 1 km x 1 km terrestrial ecosystem simulation (from 1850 to 2014) over the Seward Peninsula in Alaska (78,000 km2). The experiment contained two phases: a spin-up simulation and a transient simulation, and required five weeks of calculations using 320 cores in a 44-node Linux HPC computer. It created approximately 1.3 TB of data from the transient simulation alone (1850 - present). We selected sample results (monthly and daily simulation outputs) to illustrate the temporal and spatial variations of several variables in high-latitude Arctic ecosystems’ water, energy, and carbon cycles. At last, we summarized the lessons learned and proposed new developments for full-scale uELM simulations over the entire North American continent (approximately 22,000,000 km2).

The Spatiotemporal Change in Land Cover and Discrepancies within Different Countries on the Qinghai–Tibet Plateau over a Recent 30-Year Period

The Qinghai–Tibet Plateau is a unique global natural geographical unit with a high altitude and fragile ecology, where land cover change has been affecting its regional ecological security and sustainable development. The plateau covers nine countries with different variations in climate change and human activities, which cause significant spatial variations in its land cover change. This paper uses land cover data to reveal the spatiotemporal characteristics and multi-country differences in land cover change on the Qinghai–Tibet Plateau from 1992 to 2020 by applying spatiotemporal characteristic analysis and mapping methods. The results show that grassland (65.70%), bare land (15.56%), and woodland (10.25%) are the main types of land cover on the plateau, accounting for 91.51% of the total area. The interconversion of bare land and grassland was dominant in 1992–2020, with an area share of 67.83%. The turning point year of grassland change occurred in 2015. The grassland area increased by 6312 km2 in 1992–2015, while it decreased by 14,646 km2 in the plateau in 2015–2020. The areas of woodland, cropland, and water increased by 2.77%, 5.85%, and 7.57%, respectively, and the area of built-up land increased from 299.17 km2 to 1206.29 km2. Overall, a warming and wetting trend of the climate in the Qinghai–Tibet Plateau has driven the expansion of natural vegetation and water in the central plateau within China, with its natural vegetation area increasing by 0.11%. However, the development of regional warming and drying caused local grassland degradation, where Kyrgyzstan and India within the Qinghai–Tibet Plateau experienced the most prominent vegetation degradation. Human activities are relatively frequent in the Qinghai–Tibet Plateau within China and India, causing higher growth rates of built-up land and cropland than in other countries. The establishment of reserves and effective forest management policies have led to significant increases in woodland areas in China and Nepal within the plateau, while weak forest management and limited investment in forest conservation have led to high rates of deforestation in India and Myanmar. Accelerated temperature rises and regional differences in precipitation are the main drivers of large-scale land cover change on the plateau and differences in human activities and land use policies are responsible for the dramatic and diverse localized land cover change.

How did a tailings spill change the distribution of legacy organochlorine compounds in a Southeast Atlantic inner shelf area: Is a hidden danger being transferred to the ocean?

Polychlorinated biphenyls (PCBs) and dichloro-diphenyl-trichloroethane (DDT) were evaluated in water and sediments from the Espírito Santo Inner Shelf (ESIS), Brazil, three years after the Fundão dam failure (FDF). We discuss the levels, sources, fate, and current environmental risks of these contaminants on temporal and spatial scales. In addition, the associated coastal dispersion patterns, water-sediment exchange trends, and environmental alterations were also discussed. Low contributions and no environmental risks were verified for PCBs after FDF. However, the low concentrations and frequency of occurrence in the samples did not allow for further reliable conclusions regarding the source of this contaminant. In contrast, hazard risk has been detected for DDTs in water and sediments. In sediments, there were a significant increase in level (up to 13.42 ng g−1; outlier = 369.6 ng g−1), inventory (maximum = 35.98 ng cm−2) and mean total mass (21.1 ± 39.4 kg) of DDTs after FDF. The integrated assessment of the spatial distribution in water and sediment suggests that DDTs was released from the Doce River, travelled south by the water column, and returned to the mouth region by northward sediment transport, where it accumulated. However, intense rainfall increased the input of DDTs to the ESIS and may have also altered its spatial distribution. Fugacity fraction analysis (ƒƒ) indicated a net flux of DDTs from water to sediment, suggesting that vertical sinking was an important transport process in this area. Finally, the findings indicate that FDF contributed to DDTs input on ESIS by remobilizing contaminated past sediments and soils from the Doce River drainage basin. This contribution is expected to continue since a large amount of tailings is still stored in the river basin and estuary. These results highlight the importance of assessing the indirect impacts of large-scale land disasters on marine environments, and may be helpful in future interpretations of additional local trends and global inventories of legacy pollutants.

The Impact of Resettlement on Quality of Life in Ghana: The Mediating Role of Residential Satisfaction

The effects of large-scale land acquisitions for development projects such as mining, urbanization and dams are widely contested in the literature. Prior research has focused on the difficulties that resettlement poses for the affected families in terms of their capacity to meet the demands of the new community. This study suggests that resettlement (built environment) quality may act as a predictor and influence the residential satisfaction and quality of life of project-affected families. This study investigates how residential satisfaction influences the relationship between the quality of the dwelling, the context of resettlement homes, and the quality of life of mining-affected families in Ghana. A sample of 256 household heads displaced by a large-scale mining project in Ellembelle was used to evaluate the suggested model. The findings indicate that resettlement quality greatly enhances the quality of life of the mining-affected families. The findings also revealed that residential satisfaction was a significant mediator of the quality of dwellings and the context of the resettlement, in addition to being a strong predictor of quality of life. Indirectly, resident characteristics, such as socioeconomic and demographic conditions, influenced how people felt about their homes and quality of life. These results indicate that residential satisfaction and resettlement quality are accurate indicators of the mining-affected families’ quality of life. Integrating these features make it possible to quantify the mining-affected families’ quality of life for future research and public policy.

Large-scale agricultural land investments and food security in Nigeria

The study examines how large-scale agricultural land investments (LIs) affect household food security in Nigeria. It is one of the few studies in Nigeria that offers new empirical insights into household food security. Precisely, it unravels how LIs affect the livelihood outcomes of households in communities where LIs operate compared to households in communities without such LIs. This study engages the mixed methods of analysis involving quantitative and qualitative techniques. The quantitative data is drawn from the Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) utilising the Logistic regression technique. The qualitative aspect of this study involved fieldwork conducted in two distinct Nigerian states: Kwara State, situated in the North-Central region, and Ogun State, located in the South-West region. The qualitative findings further enrich the quantitative findings. It also helped to investigate the nature of jobs households engaged in and also the employment density of the investors’ farms which could not be done using the national representative data (LSMS). The results indicate that households in communities without land investments have 0.2% higher likelihood of being more food secure than households in communities with land investments and it is significant at 5%. The qualitative analysis reveals several key insights, including the observation that female headed households in communities with LIs are less likely to be food secure, receive less wages, and spend more time in communities with LIs. Government should put in place policies (for example, compensation policies) that will ensure that land deals are properly monitored and structured to benefit and protect the host communities. If the government fails to properly regulate land deals, rural people will always suffer. Also, they should monitor and control the activities, and the type of crops grown in investment farms so that land investments can bring about the desired benefits such as improvement in household livelihood and enhance food security in the communities where they exist.

Identifying a scenario for preindustrial cropland cover using cultivation data: A case study of France, Germany and Italy

Cropland expansion is effected by physiogeographic and sociocultural factors, which vary across region and over time, but have not been adequately represented in large-scale anthropogenic land cover change scenarios. Taking preindustrial cropland expansion in France, Germany, and Italy as a case study, this study first adopted a productivity-based estimation of cropland per capita, which converted crop yield to cropland demand per capita through negative correlations under different crop rotations, to improve the accuracy of national cropland areas. Then, a new allocation algorithm was proposed to allocate national cropland areas into 5′× 5′ grids. The algorithm combines land suitability, which characterizes regional differentiation of potential productivity indicated by physiogeographic factors (climate, topography, soil, etc.) that dominate cultivation, and cultivation preference, which quantifies sociocultural impacts by kernel density estimation based on city information (i.e., location, size, accessibility). The cropland cover scenario here shows similar phases but different modes of cropland expansion in these countries: (1) National cropland areas increased 1.73–2.46 times during the 11th–13th centuries, manifested by the expanding cultivated ranges in France and Germany and the increasing cultivation intensity in Italy. (2) National cropland fractions decreased by 9.16–19.98 percentage points in the 14th century, accompanied by widespread reductions in cultivation intensity. (3) Cropland gradually recovered toward its peak in 1300 AD during the 15th–18th centuries, achieved by general and remarkable increases in cultivation intensity. Compared to representative global datasets, this study is more consistent with cultivation-related descriptions in the literature, especially with respect to the cropland expansion in newly cultivated regions and on marginal lands.

Land Concessions and Postwar Conflict in Laos

Although it is commonly thought that the war in Laos ended in 1975 when the communist Pathet Lao took over the country, in fact the takeover stirred up considerable military and political opposition, leading to civil war. For a variety of reasons, the conflict diminished in the 1990s and 2000s. The improved security situation allowed the government to adopt a new policy allocating large-scale land concessions for use in developing commercial plantations. These concessions inflicted social and environmental violence on the people who lost their land. These two types of violence in Laos, war and dispossession, are linked more closely than is generally recognized.

Kuala Lumpur Green Space Fragmentation Index Analysis and Green Corridors Suggestion using Geographic Information System

More than half of the world’s population lives in urban areas, and the United Nations has projected that nearly all future global population growth will occur in urban areas. Urbanization has caused rapid industrial, transportation and residential development at the expense of environmental deterioration. Activities involving large-scale land clearances are conducted to satisfy human needs for expansion. The clearances have caused green area fragmentation and patches, especially in the rapidly developing urban areas. This study is aimed at detecting the current coverage of green space in the metropolitan city of Kuala Lumpur (KL), the capital city of Malaysia and monitor its fragmentation using a very high spatial resolution satellite data. Green cover which includes trees, grass, and shrubs of KL was first extracted from the SPOT 6 data using Object Oriented method which can classified the green covers. The fragmented green spaces in KL were analyzed using a range of fragmentation indices namely Number of Patches, Edge Density, Class Area, Total Landscape Area, Mean Patch Size and Mean Shape Index using the Patch Analyst tool embedded in the ArcGIS software version 10.0 to study the patterns of green patches. The connectivity of the green patches was studied and proposed to overcome the fragmentation problems. Kuala Lumpur (KL) was chosen as the case study as it is a rapidly developing city with a significant loss of green space. The urban green spaces were more fragmented as there were more built-up areas. Results of the green cover extraction reveal that the KL city centre had the lowest percentage of green cover (2.09 %) area to the urban area compared to the other five zones (Sentul Manjalara 5.03 %, Wangsa Maju Maluri 6.09 %, Damansara Penchala 11.22 %, Bukit Jalil Seputeh 7.07 % and Bandar Tun Razak Sg Besi 7.42 %). Since KL’s urban growth predominantly consists of compact building blocks, the highest land use was believed to be for commercial purposes. Around 66% of industrial and commercial activities are concentrated in the KL city centre, resulting in a diverse green space structure and a growing gap between patches in the city centre zone. The green patches were then connected to create green corridors in the city centre. Several parameters were involved in the analysis, including area, distance, types of land use (green areas and place of interest for the origin and destination), slopes under 7% value (topology with flat surfaces) and road networks. The origin and destination points were analyzed using the network analysis method in Quantum GIS to produce routes with the shortest path algorithm. Results showed that six routes were suitable for designing green corridors in the KL city centre area connecting green spaces and places of interest. All six routes were analyzed every 400 meters using Google Earth Street View to see the surrounding facilities. The Green Corridor Route 6 was the most appropriate for people, as it included parks and places of interest for city dwellers. The green corridor could then be promoted as a walkable area to minimize carbon emissions in the urban area, reducing the urban heat island phenomenon and increasing green connectivity for a better urban life.

Estimation of Carbon Stocks and Carbon Sequestration Rates in Abandoned Agricultural Soils of Northwest Russia

The fallow agricultural soils of Northwest Russia represent an evolutionary model of the development of ecosystem components in time and space with multidirectional dynamics of agrogenic impact during the long history of agricultural land development. There has been both large-scale land development and uncontrolled conversion of arable lands to a fallow state along with their removal in recent times. All this has led to the formation of a chrono-series of different-age soils with varying degrees of exposure of agrogenic factors. This paper presents a current review of the humus state of fallow soils in Northwest Russia, and examines the main factors (self-restoration, humus transformation, acidification) influencing the transformation of the soil cover under the process of post-agrogenesis. Effective farming techniques aimed at fixing carbon in soils as part of increasing the sequestration potential to mitigate the impact of climate change are considered. The ongoing process of the transition of lands into a fallow state could lead to organic carbon losses and changes in the main physical and chemical parameters, which negatively affects the self-restoration of fallow lands. We offer some recommendations for the effective rewetting of fallow lands in Northwest Russia with the purpose of carbon sequestration in the soil cover.

Map vector tile construction for arable land spatial connectivity analysis based on the Hadoop cloud platform

The concentrated connection of arable land is one of the important indicators reflecting the quality of cultivated land, and large-scale arable land blocks are more conducive to agricultural mechanization operation, thereby improving the land use efficiency. However, the calculation of farmland connectivity is essentially a large-scale calculation of spatial vector data, especially for the national or global farmland patch data. This article proposes a framework for calculating farmland connectivity based on spatial vector map tiles and parallelizes the algorithm based on the Hadoop cloud platform. The framework is based on the tile pyramid model and uses the Douglas–Peucker algorithm to simplify the data to meet the needs of rapid display of large-scale data under multi-scale. The consistency and integrity of the front display of vector tiles are ensured using the setting tile buffer. Meanwhile, the parallelization of the vector tile construction algorithm is realized based on the MapReduce programming mode. Finally, the effectiveness and usability of this framework were verified through the calculation of patch connectivity on the tillage map. Experiments show that the algorithm can not only meet the rapid construction requirements of large-scale vector tile data but also support the cultivated land spatial connectivity analysis and greatly improve the efficiency of supporting data calculation.

A Study of Sandy Land Changes in the Chifeng Region from 1990 to 2020 Based on Dynamic Convolution

Desertification is the process of land degradation and the reduction or destruction of biological potential in arid, semi-arid, and semi-humid areas, and its impact on agricultural development and the ecological environment cannot be ignored. Therefore, many researchers have aimed to achieve the acquisition of large-scale sandy land areas using sandy land extraction algorithms based on remote sensing images. However, the sandy land extraction accuracy needs to be improved because of the variable contour features in the remote sensing images and the easy confusion with targets such as the Gobi and bare ground areas. In this study, we combine the dynamic convolution with a U-Net model and propose the DU-Net sandy land extraction model. The method is based on dynamic convolution, which can adaptively learn the complex features of the target and build a dynamic convolutional neural network to achieve high-accuracy extraction of complex targets. DU-Net achieved 86.32% in IoU, 93.22% in precision, 94.5% in recall, and 92.66% in F1-score in sandy land extraction accuracy, which are 4.68%, 2.33%, 3.09%, and 2.76% improvements, respectively, compared with the U-Net static neural network. Based on this, we obtained the spatial and temporal evolution trends of sandy land areas based on Landsat images in the Chifeng region in the Inner Mongolia Autonomous Region, China. Meanwhile, in order to investigate the mechanism of spatial and temporal changes in the sandy land area in the study region over the past 30 years, the direct and indirect effects of seven climatic and human socioeconomic activity factors on the changes in the sandy land area in the study region were evaluated based on a structural equation model. The results show that the sandy area in the Chifeng region tended to first increase and then decrease over the study period, with the sandy land area reaching its maximum around the year 2000. In addition, the main driving factor for the change in the sandy land area in the Chifeng region has been human socioeconomic activities, with climatic conditions as the secondary driving factor. The method proposed in this paper realizes the rapid extraction of sandy land areas with high accuracy at a large scale and with a long time series and provides a basis for assessing the effectiveness of ecosystem restoration projects.

Large-Scale Land Transformations and Changing Sociality among the Wampar in Papua New Guinea

ABSTRACT In the Markham Valley of Papua New Guinea, a Biomass Energy company is currently transforming the landscape by planting thousands of hectares of eucalyptus trees to offer a ‘carbon-neutral’ way of electricity generation. The company operates in concert with local Wampar landholders, offering jobs, land rents, and business opportunities. This large-scale land transformation from grassland to eucalyptus forests impacts social relations, especially those regulating access to land, and creates more and novel social conflicts within and between kin groups. By presenting four ethnographic portraits, I show how access to and exclusion from land, jobs and monetary benefits are differentially experienced, and how people negotiate these novel effects of ‘intimate exclusion’ occurring in their midst. There are clear trends towards alienation and exclusion of some people from land to which they formerly had access. Customary claims to usage rights are abrogated except for the primary owners of the land, and sometimes even members of the landholding lineages are deprived of receiving monetary benefits from these enterprises. Access to land, jobs and business opportunities are now governed mainly by social relations with the main decision-makers and proponents of the eucalyptus project.

Exploring an integrated framework for “dynamic-mechanism-clustering” of multiple cultivated land functions in the Yangtze River Delta region

Coordinating multiple cultivated land functions (CLFs) has become a key challenge for land system science. Identifying the dynamic evolution and mechanisms of multiple CLFs will help to formulate targeted cultivated land management policy for achieving the sustainable development of highly urbanized areas. This study quantified four primary CLFs, i.e., agricultural production function (APF), social security function (SSF), ecological service function (ESF), and landscape aesthetic function (LAF) at the prefecture-level cities of the Yangtze River Delta region and then explored an integrated framework for “dynamic-mechanism-clustering” of these CLFs. The results showed that four primary CLFs varied substantially across the prefecture-level cities and presented significant changes over time. The slope has a great negative impact on multiple CLFs. Precipitation was negatively correlated with APF and ESF, rural residents’ income and distance to the road showed negative correlations with SSF and LAF. The areas covered by large-scale contiguous cultivated land presented the strongest cultivated land multifunctionality, whereas the highly urbanized areas and the areas with considerable forest land showed weak cultivated land multifunctionality. We suggested that decision-makers should consider the underlying drivers of multiple CLFs for better cultivated land management and promoting the sustainable use of cultivated land.

Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network

Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping.

Introducing ICEDAP: An ‘Iterative Coastal Embayment Delineation and Analysis Process’ with Applications for the Management of Coastal Change

Coastal embayments provide vital benefits to both nature and humans alike in the form of ecosystem services, access to waterways, and general aesthetic appeal. These coastal interfaces are therefore often subject to human development and modifications, with estuarine embayments especially likely to have been anthropogenically altered. Frequent alterations include damming to eliminate tidal influx, backfilling to create new land, and development for the sake of economic gain, which may cause profound damage to local habitats. By providing a record of transitions in surface waters over time, satellite imagery is essential to monitoring these coastal changes, especially on regional to global scales. However, prior work has not provided a straightforward way to use these satellite-derived datasets to specifically delineate embayed waters, limiting researchers’ ability to focus their analyses on this ecologically and economically important subset of coastal waters. Here, we created ICEDAP, a geometry-based ArcGIS toolbox to automatically delineate coastal embayments and quantify coastal surface water change. We then applied ICEDAP to the coast of South Korea, and found that coastal habitat change was particularly profound within embayed regions identified using an 8 km epsilon convexity setting (denoting a moderate distance from the coast and degree of enclosure by surrounding land areas). In the mapped coastal embayments, more than 1400 km2 of coastal habitats were lost during the past 38 years, primarily due to human modification such as large-scale land reclamation projects and the construction of impoundments. Our results suggest that anthropogenic alterations have resulted in the widespread loss of more than USD 70 million of valuable coastal ecosystem services. Together, ICEDAP provides a new innovative tool for both coastal scientists and managers to automatically identify hotspots of coastal change over large spatial and temporal scales in an epoch where anthropogenic and climate-driven changes commonly threaten the stability of coastal habitats.

Abandoned Land Mapping Based on Spatiotemporal Features from PolSAR Data via Deep Learning Methods

Monitoring agricultural abandonment is essential in understanding the effects on the environment and food security. Polarimetric synthetic aperture radar (PolSAR) is an efficient approach for the monitoring of large-scale agricultural land cover in cloudy and rainy areas. However, previous studies have not taken advantage of the valuable phase information and not fully utilized the spatiotemporal features of farmland parcels, which has seriously limited the abandoned land identification accuracy. In this study, we developed a new method for the mapping of abandoned land based on the spatiotemporal features from PolSAR Single Look Complex (SLC) images via deep learning methods. First, backscattering coefficients (σ0VV, σ0VH) were derived, and the polarimetric parameters (entropy, anisotropy and mean alpha angle) were obtained based on Cloude–Pottier polarimetric decomposition. Then, the VGG16 deep convolutional network was innovatively used to extract spatial features from both the backscattering coefficients and polarimetric parameters. Next, the separability index was calculated to select the most effective spatial features. Finally, LSTM classifications were conducted based on the time series of backscattering features, the polarimetric parameters, the extracted spatial features and their combinations. The results showed that the introduction of multitemporal polarimetric parameters and spatial features both led to an improvement in the abandoned land identification accuracy. The combination of backscattering features, polarimetric parameters and spatial features yielded the best performance in identifying abandoned land, with producer’s accuracy of 88.29% and user’s accuracy of 84.03%. This study demonstrated the potential of polarimetric parameters and validated the effectiveness of spatiotemporal features in abandoned land identification. It provided a practical method for the production of a highly reliable abandoned land mapping in cloudy and rainy areas.

The Impact of Large-scale Agricultural Investments in Low-income Economies

Recent years have witnessed an increasing interest in large-scale agricultural land acquisitions in developing countries. The accompanying socio-economic implications have been areas of debate among politicians, policymakers and development agents. This paper argues that the traditional way of simulating the impacts of these investments in developing countries is misleading as the approach implies that the new investments are identical to the semi-subsistence way of farming that dominates agricultural practices in the host countries. In this study, we incorporate the peculiarity of large-scale agro-investments into an existing database for economy-wide models, i.e., social accounting matrix (SAM), and capture welfare and distributional outcomes properly. SAM-based multiplier models applied to Ethiopian data justify the need to account for the peculiarity of the investments in terms of production technology and their geographic distribution.

Large-scale land acquisitions exacerbate local farmland inequalities in Tanzania

Land inequality stalls economic development, entrenches poverty, and is associated with environmental degradation. Yet, rigorous assessments of land-use interventions attend to inequality only rarely. A land inequality lens is especially important to understand how recent large-scale land acquisitions (LSLAs) affect smallholder and indigenous communities across as much as 100 million hectares around the world. This paper studies inequalities in land assets, specifically landholdings and farm size, to derive insights into the distributional outcomes of LSLAs. Using a household survey covering four pairs of land acquisition and control sites in Tanzania, we use a quasi-experimental design to characterize changes in land inequality and subsequent impacts on well-being. We find convincing evidence that LSLAs in Tanzania lead to both reduced landholdings and greater farmland inequality among smallholders. Households in proximity to LSLAs are associated with 21.1% (P = 0.02) smaller landholdings while evidence, although insignificant, is suggestive that farm sizes are also declining. Aggregate estimates, however, hide that households in the bottom quartiles of farm size suffer the brunt of landlessness and land loss induced by LSLAs that combine to generate greater farmland inequality. Additional analyses find that land inequality is not offset by improvements in other livelihood dimensions, rather farm size decreases among households near LSLAs are associated with no income improvements, lower wealth, increased poverty, and higher food insecurity. The results demonstrate that without explicit consideration of distributional outcomes, land-use policies can systematically reinforce existing inequalities.