Spatial autocorrelation in multi-scale land use models

Analysis of environmental variables and deforestation in the amazon using logistical regression models

In this study, we applied a multivariate logistic regression model to identify deforested areas and evaluate the current effects on environmental variables in the Brazilian state of Rondônia, located in the southwestern Amazon region using data from the MODIS/Terra sensor. The variables albedo, temperature, evapotranspiration, vegetation index, and gross primary productivity were analyzed from 2000 to 2022, with surface type data from the PRODES project as the dependent variable. The accuracy of the models was evaluated by the parameters area under the curve (AUC), pseudo R2, and Akaike information criterion, in addition to statistical tests. The results indicated that deforested areas had higher albedo (25%) and higher surface temperatures (3.2 °C) compared to forested areas. There was a significant reduction of the EVI (16%), indicating water stress, and a decrease in GPP (18%) and ETr (23%) due to the loss of plant biomass. The most precise model (91.6%) included only surface temperature and albedo, providing important information about the environmental impacts of deforestation in humid tropical regions.

Coupled relationships between landscape pattern and ecosystem health in response to urbanization

Rapid urbanization has highlighted ecological problems in the metropolitan area, with increasing landscape fragmentation and severe threats to ecosystem health (EH). Studying the spatio-temporal coupled relationship between landscape pattern and EH and its response to urbanization in the Fuzhou metropolitan area (FMA) can provide scientific reference for its long-term development planning. We examined the coupled relationship between landscape pattern and EH and its driving mechanism in the FMA at grid and township scales to address the gap. The results show that landscape heterogeneity, diversity, and dispersion are gradually increasing, and EH is rising progressively in the FMA from 2000 to 2020. The spatial distribution of landscape pattern indices and EH indicators showed a "high in the south and low in the north" trend. During the study period, the coupled relationship between landscape patterns and EH was increasingly powerful but with remarkable spatial heterogeneity. The study also found an inverted U-shaped relationship between urbanization and coupled relationships. Ecological landscapes' heterogeneity, diversity, and connectivity in low-urbanization areas are conducive to EH. The opposite is true for high-urbanization areas. This study provides a valuable reference for optimizing landscape planning and ecological management in metropolitan areas.

Spatial distribution and determinants of unmet need for family planning among all reproductive‑age women in Uganda: a multi‑level logistic regression modeling approach and spatial analysis

INTRODUCTION

Unmet need for family planning is defined as the percentage of sexually active and fecund women who want to delay the next birth (birth spacing) or who want to stop childbirth (birth limiting) beyond two years but who are not using any modern or traditional method of contraception. Despite the provision of family planning services, the unmet need of family planning remains a challenge in low- and middle-income countries (LMICs). Thus, this study aimed to assess the spatial distribution and determinant factors of unmet need for family planning among all reproductive‑age women in Uganda.

METHODS

A secondary data analysis was done based on 2016 Ugandan Demographic and Health Surveys (UDHS). Total weighted samples of 18,506 women were included. Data processing and analysis were performed using SPSS Version 26, STATA 14.2, ArcGIS 10.8, and SaTScan 10.1.2 software. Spatial autocorrelation and hotspot analysis was made using Global Moran's index (Moran's I) and Gettis-OrdGi*statistics, respectively. Determinants of unmet needs for family planning were identified by multi-level logistic regression analysis. Variables with a p-value < 0.05 were declared statistically significant predictors.

RESULTS

The spatial distribution of unmet need for family planning among women of reproductive age in Uganda was found to be clustered (Global Moran's I = 0.27, Z-score of 12.71, and p-value < 0.0001). In the multivariable multilevel logistic regression analysis; women in West Nile (AOR = 1.86, 95% CI: 1.39, 2.47), aged 25-49 years old (AOR = .84; 95% CI .72, .99), highly educated (AOR = .69; 95% CI .54, .88), Muslim (AOR = 1.20, 95% CI: 1.03, 1.39), high wealth status (AOR = .73, 95% CI: .64, .82), and had five or more living child (AOR = 1.69, 95% CI: 1.51, 1.88) were significant predictors of unmet need for family planning. Significant hotspot areas were identified in West Nile, Acholi, Teso, and Busoga regions.

CONCLUSION

A significant clustering of unmet need for family planning were found in Uganda. Moreover, age, educational status, religion, wealth status, number of alive children, and region were significant predictors of unmet need for family planning. Therefore, in order to minimize the burdens associated with unmet need, an interventions focusing on promotion of sexual and reproductive health service should be addressed to the identified hotspot areas.

Multi-dimensional landscape ecological risk assessment and its drivers in coastal areas

The eastern coastal areas of Japan are threatened by multiple ecological risks due to frequent natural disasters, climate changes, human activities, etc. Identification spatio-temporal variations and driving mechanisms of landscape ecological risk could be used as significant basis for policymakers. In this study, taking the eastern coastal areas of Japan affected by the 2011 Great East Japan Earthquake and Tsunami Disaster as the study area, the "Nature-Landscape Pattern-Human Society" (NA-LP-HS) multi-dimensional ecological risk assessment framework was established to analyze the spatio-temporal patterns, and identity driving factors using spatial cluster analysis and spatial principal component analysis (SPCA) based on ArcGIS from 2009 to 2021. The findings revealed the distinct geographic patterns in landscape ecological risk, with a noticeable decline from the southwest to the northeast. During the period from 2009 to 2015, the driving factors leading to a sharp risk increase were natural disasters and vegetation coverage. These high-risk areas were concentrated in Sendai Bay and its surroundings. From 2015 to 2021, ecological instability was primarily attributed to a reduction in vegetation coverage, the occurrence of natural disasters, and heightened rainfall erosion. These high-risk areas were mainly clustered within the Tokyo-centered urban agglomeration. Spatial clustering of ecological risks was obvious across all time periods. The key factors contributing to the clustering of high ecological landscape risks focused on the "landscape pattern" criterion, specifically including vegetation coverage, land use land cover. This study demonstrated the ability of multi-dimensional ecological risk assessment to identify high-risk areas and driving factors, and these results could provide a visual analysis and decision-making basis for sustainable development of coastal areas.

Spatial multilevel analysis of age at death of under-5 children and associated determinants: EDHS 2000-2016

OBJECTIVE

This study examines trends, spatial distribution and determinants of age at death of under-5 children in Ethiopia.

DESIGN

This study used secondary data from the 2000, 2005, 2011 and 2016 Ethiopian Demographic and Health Surveys. A multilevel partial ordinal logistic regression model was used to assess the effects of variables on the age at death of children under 5 years.

SETTING

Ethiopia.

PARTICIPANTS

The final analysis included a sample of 3997 deaths of newborns, infants and toddlers.

RESULTS

A total of 1508, 1054, 830 and 605 deaths of under-5 children were recorded in the 2000, 2005, 2011 and 2016 survey years, respectively. The death of newborns, infants and toddlers showed a significant decrease from 2000 to 2016, with reductions of 33.3% to 17.4%, 42.4% to 12.6% and 45.2% to 11.6%, respectively. The analysis using Global Moran's Index revealed significant spatial autocorrelation in mortality for each survey year (p<0.05). The intraclass correlation of age at death of under-5 children within regions was substantial. Furthermore, the odds of newborn deaths among under-5 children (OR: 0.638, 95% CI: 0.535, 0.759) were lower for those delivered in health institutions compared with those delivered at home.

CONCLUSIONS

Throughout the survey years from 2000 to 2016, newborn children had higher mortality rates compared with infants and toddlers, and significant spatial variations were observed across different zones in Ethiopia. Factors such as child's sex, age of mother, religion, birth size, sex of household head, place of delivery, birth type, antenatal care, wealth index, spatial autocovariate, Demographic and Health Survey year, place of residence and region were found to be significant in influencing the death of under-5 children in Ethiopia. Overall, there has been a decreasing trend in the proportion of under-5 child mortality over the four survey years in Ethiopia.

Spatial and temporal characteristics of coal consumption and carbon emissions in China

Based on the requirement of energy restructuring in China's "Dual Carbon" target, this study measures the carbon emissions of 30 provinces (autonomous region and municipality directly under the central government) in China from 1997 to 2019, the relationship between coal consumption and carbon emissions in China was investigated by using the methods of robust test, panel cointegration test, and Granger causality test; exploratory spatial data analysis (ESDA) is applied to analyze the spatial characteristics of energy consumption and carbon emissions in China. The results show that (i) there is a long-term two-way causal relationship between coal consumption and carbon emissions in China; (ii) both coal consumption and carbon emissions in China show spatial correlation, with obvious locational characteristics, and are relatively stable in the spatial pattern as a whole, with relatively small changes in the short term; (iii) both coal consumption and carbon emissions show significant positive correlation and positive spatial correlation, with an increase in coal consumption and an increase in carbon emissions. The local Moran's I can show that there are fewer areas that differ from the overall trend, with H-H agglomeration maintaining a stable contiguous trend, L-L agglomeration decreasing, and contiguous characteristics gradually disappearing.

Detecting the interaction between urban elements evolution with population dynamics model

Exploring the evolution of urban elements can improve understanding of the developmental process of city and drive such development into a better direction. However, the non-linearity and complexity of changes in urban elements have brought great challenges to understanding this process. In this paper, we propose a cross-diffusion partial differential equation based on ecological dynamics to simulate the evolutionary process of urban elements from the microscopic viewpoint. The interaction between urban elements is simulated by constructing a non-linear and spatiotemporal change equation, and the main influence between elements is evaluated by the key parameters in the discussed equation. Our model is first experimented to time-series data on population density and housing prices to analyzes the interaction of these two elements in the evolution process. We then extend the model to label data, land cover data, to obtain a quantitative expression of the interaction between different land types in the process of urban land cover change.

Spatial autocorrelation and hotspot analysis of natural radionuclides to study sediment transport

Tracking sediment movement is typically done with artificial radionuclides. However, this can be environmentally harmful and does not allow for sediment classification. Naturally occurring radionuclides are consequently offered as an alternative. In this study, a mobile Delta Underwater Gamma System (DUGS) capable of measuring low levels of natural radionuclides in sediment was deployed in an estuary, and a radiometric map of the sediment was constructed. Spatial autocorrelation using the Moran's I statistic was used to investigate the spatial distribution patterns of natural radionuclides in the sediments. Hotspot analysis using Getis-Ord* was used to validate and map areas that had been identified as clustered by the Moran's I statistic. The Moran's I analysis indicated that ⁴⁰K displayed a positive spatial autocorrelation with a value of 0.4 and a standardized Z score of 16, thus indicating that the clustering was significant. ²³⁸U and ²³²Th displayed a low Moran's I value but a strong positive correlation, hence indicating some spots of clustering in the river channel. Further analysis of hotspots confirmed that the identified clusters were areas with relatively high radionuclide concentrations. This proved that the hotspot areas identified have a high deposition of sediment. In situ radiometric measurements of sediment, as well as spatial analysis, are consequently useful tools to model and study spatial structure and sediment.

Alcohol consumption: context and association with mortality in Switzerland

PURPOSE

Non-communicable diseases generate the largest number of avoidable deaths often caused by risk factors such as alcohol, smoking, and unhealthy diets. Our study investigates the association between amount and context of alcohol consumption and mortality from major non-communicable diseases in Switzerland.

METHODS

Generalized linear regression models were fitted on data of the cross-sectional population-based National Nutrition Survey menuCH (2014-2015, n = 2057). Mortality rates based on the Swiss mortality data (2015-2018) were modeled by the alcohol consumption group considering the amount and context (i.e., during or outside mealtime) of alcohol consumption and potential confounders. The models were checked for spatial autocorrelation using Moran's I statistic. Integrated nested Laplace approximation (INLA) models were fitted when evidence for missing spatial information was found.

RESULTS

Higher mortality rates were detected among drinkers compared to non-drinkers for all-cancer (rate ratio (RR) ranging from 1.01 to 1.07) and upper aero-digestive tract cancer (RR ranging from 1.15 to 1.20) mortality. Global Moran's I statistic revealed spatial autocorrelation at the Swiss district level for all-cancer mortality. An INLA model led to the identification of three districts with a significant decrease and four districts with a significant increase in all-cancer mortality.

CONCLUSION

Significant associations of alcohol consumption with all-cancer and upper aero-digestive tract cancer mortality were detected. Our study results indicate the need for further studies to improve the next alcohol-prevention scheme and to lower the number of avoidable deaths in Switzerland.

Integration of Moran's I, geographically weighted regression (GWR), and ordinary least square (OLS) models in spatiotemporal modeling of COVID-19 outbreak in Qom and Mazandaran Provinces, Iran

Globally, the COVID-19 pandemic is a top-level public health concern. This paper attempts to identify the COVID-19 pandemic in Qom and Mazandaran provinces, Iran using spatial analysis approaches. This study was based on secondary data of confirmed cases and deaths from February 3, 2020, to late October 2021, in two Qom and Mazandaran provinces from hospitals and the website of the National Institute of Health. In this paper, three geographical models in ArcGIS 10.8.1 were utilized to analyze and evaluate COVID-19, including geographic weight regression (GWR), ordinary least squares (OLS), and spatial autocorrelation (Moran I). The results from this study indicate that the rate of scattering of confirmed cases for Qom province for the period was 44.25%, while the rate of dispersal of the deaths was 4.34%. Based on the GWR and OLS model, Moran's statistics demonstrated that confirmed cases, deaths, and recovered followed a clustering pattern during the study period. Moran's Z-score for all three indicators of confirmed cases, deaths, and recovered was confirmed to be greater than 2.5 (95% confidence level) for both GWR and OLS models. The spatial distribution of indicators of confirmed cases, deaths, and recovered based on the GWR model has been more scattered in the northwestern and southwestern cities of Qom province. Whereas the spatial distribution of the recoveries of the COVID-19 pandemic in Qom province was 61.7%, the central regions of this province had the highest spread of recoveries. The spatial spread of the COVID-19 pandemic from February 3, 2020, to October 2021 in Mazandaran province was 35.57%, of which 2.61% died, according to information published by the COVID-19 pandemic headquarters. Most confirmed cases and deaths are scattered in the north of this province. The ordinary least squares model results showed that the spatial dispersion of recovered people from the COVID-19 pandemic is more significant in the central and southern regions of Mazandaran province. The Z-score for the deaths Index is more significant than 14.314. The results obtained from this study and the information published by the National Headquarters for the fight against the COVID-19 pandemic showed that tourism and pilgrimages are possible factors for the spatial distribution of the COVID-19 pandemic in Qom and Mazandaran provinces. The spatial information obtained from these modeling approaches could provide general insights to authorities and researchers for further targeted investigations and policies in similar circumcises.

Is Following a Cancer-Protective Lifestyle Linked to Reduced Cancer Mortality Risk?

Objectives: This study investigates the association between a cancer protective lifestyle (defined based on the revised World Cancer Research Fund (WCRF) and the American Institute for Cancer Research (AICR) cancer prevention recommendations) and mortality in Switzerland. Methods: Based on the cross-sectional, population-based National Nutrition Survey, menuCH (n = 2057), adherence to the WCRF/AICR recommendations was assessed via a score. Quasipoisson regression models were fitted to examine the association of adherence to the WCRF/AICR recommendations with mortality at the Swiss district-level. Spatial autocorrelation was tested with global Moran's I. Integrated nested Laplace approximation models were fitted when significant spatial autocorrelation was detected. Results: Participants with higher cancer prevention scores had a significant decrease in all-cause (relative risk 0.95; 95% confidence interval 0.92, 0.99), all-cancer (0.93; 0.89, 0.97), upper aero-digestive tract cancer (0.87; 0.78, 0.97), and prostate cancer (0.81; 0.68, 0.94) mortality, compared to those with lower scores. Conclusion: The inverse association between adherence to the WCRF/AICR recommendations and mortality points out the potential of the lifestyle recommendations to decrease mortality and especially the burden of cancer in Switzerland.

Spatial distribution and trends of anemia among pregnant women in Ethiopia: EDHS 2005-2016

Background

Anemia is a public health problem affecting both developed and developing nations worldwide with a significant consequence on health and economic growth. The problem is more severe in pregnant women. Hence, the main purpose of this study was to determine the factors of anemia levels among pregnant women in zones in Ethiopia.

Methods

We utilized data from 2005, 2011, and 2016 Ethiopian demographic and health survey (EDHSs), a population-based cross-sectional study. The study includes 8,421 pregnant women. An ordinal logistic regression model with spatial analysis was used to explore factors of anemia levels among pregnant women.

Result

About 224 (2.7%), 1,442 (17.2%), and 1,327 (15.8%) pregnant women were mild, moderate, and severely anemic, respectively. The spatial autocorrelation of anemia among the administrative zones of Ethiopia for the three consecutive was not significant. The middle wealth index of 15.9% (OR = 0.841, CI: 0.72-0.983) and richest wealth index of 51% (OR = 0.49, CI: 0.409-0.586) were less likely anemic compared to the poorest wealth index, age group of mother 30-39 was 42.9% (OR = 0.571, CI: 0.359-0.908) times less likely to be moderate and above anemic compared to <20 years, several household members 4-6 were 51% (OR = 1.51, CI: 1.175-1.94 more likely moderate and above anemic compared to 1-3.

Conclusion

Over one-third of the pregnant women (34.5%) were anemic in Ethiopia. Wealth index, age group, religion, region, number of household members, source of drinking water, and EDHS were significant factors in anemia levels. The prevalence of anemia among pregnant women varied among Ethiopian administrative zones. North West Tigray, Waghimra, Oromia special woreda, West shewa, and East shewa were a high prevalence of anemia.

Analyzing the Spatio-Temporal Dynamics of Urban Land Use Expansion and Its Influencing Factors in Zhejiang Province, China

The 21st century expansion of built-up areas due to rapid urbanization has recently been at the forefront of global land use/land cover research. Knowledge of the changing dynamics of urban land use is crucial for the monitoring of urbanization and the promotion of sustainable urban development. In this paper, Zhejiang Province was selected as the study area. It is a region with rapid urban growth located along the southeastern coast of China, with a highly developed economy but with a shortage of land resources. We employed remotely sensed and socio-economic panel data for the period between 1990 and 2020 to monitor urban land use changes and utilized the spatial Durbin model (SDM) to examine the urbanization process and the various driving factors of rapid urban expansion in Zhejiang Province, China, from 1990 to 2020. The study's results revealed substantial urban growth of about 6899.59 km2, i.e., 6.6%, whereas agricultural land decreased by 4320.68 km2, i.e., 4.19%. The rapid urban development was primarily attributed to the transformation of farmlands, forestlands, and water bodies into built-up areas by nearly 86.9%, 6.94%, and 6.06%, respectively. The built-up areas revealed features of spatial clustering. The study showed that the expansion hotspots were mainly distributed within the urban fabric of cities such as Hangzhou, Ningbo, Jinhua-Yiwu, and Wenzhou-Taizhou. The results further revealed the substantial influence of urban growth on the local areas of the province. As the core explanatory variables, population and economic development significantly promoted local urban expansion. The study's findings indicated a positive spatial spillover effect as regards the influence of economic development on the study area's urban growth, whereas the spatial spillover effect of the population was negative. Therefore, economic development was a major driving factor contributing immensely to the expansion of urban areas in Zhejiang Province, especially in the 26 mountainous counties of the province. The study enriches our understanding of the transformation of LULC and the changing dynamics of urban areas in China and provides the necessary research data that are vital for urban land-use planners and decision-makers to overcome the negative consequences of the expansion of urban areas due to the continuous economic growth of China.

A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt

Crop yield prediction before the harvest is crucial for food security, grain trade, and policy making. Previously, several machine learning methods have been applied to predict crop yield using different types of variables. In this study, we propose using the Geographically Weighted Random Forest Regression (GWRFR) approach to improve crop yield prediction at the county level in the US Corn Belt. We trained the GWRFR and five other popular machine learning algorithms (Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)) with the following different sets of features: (1) full length features; (2) vegetation indices; (3) gross primary production (GPP); (4) climate data; and (5) soil data. We compared the results of the GWRFR with those of the other five models. The results show that the GWRFR with full length features (R2 = 0.90 and RMSE = 0.764 MT/ha) outperforms other machine learning algorithms. For individual categories of features such as GPP, vegetation indices, climate, and soil features, the GWRFR also outperforms other models. The Moran’s I value of the residuals generated by GWRFR is smaller than that of other models, which shows that GWRFR can better address the spatial non-stationarity issue. The proposed method in this article can also be potentially used to improve yield prediction for other types of crops in other regions.

Helicoverpa zea (Lepidoptera: Noctuidae) feeding incidence and survival on Bt maize in relation to maize in the landscape

BACKGROUND

Characterizing Helicoverpa zea (Boddie) damage to maize (Zea mays L.) in relation to the spatiotemporal composition of Bt crops is essential to understand how landscape composition affects H. zea abundance. To examine this relationship, paired Bt (expressing Cry1A.105 + Cry2Ab2) and non-Bt maize plots were sampled across North and South Carolina during 2017-2019. Kernel damage and larval exit holes were measured following larval development. To understand how maize abundance surrounding sample sites related to feeding damage and larval development, we quantified maize abundance in a 1 km buffer surrounding the sample site and examined the relationship between local maize abundance and kernel damage and larval exit holes.

RESULTS

Across the years and locations, damage in Bt maize was widespread but significantly lower than in non-Bt maize, indicating that despite the widespread occurrence of resistance to Cry toxins in maize, Bt maize still provides a measurable reduction in damage. There were negative relationships between kernel injury and ears with larval exit holes in both Bt and non-Bt maize and the proportion of maize in the landscape during the current year.

CONCLUSION

Despite the widespread occurrence of resistance to Cry toxins in maize, this resistance is incomplete, and on average Bt maize continues to provide a measurable reduction in damage. We interpret the negative relationship between abundance of maize within 1 km of the sample location and maize infestation levels, as measured by kernel damage and larval exit holes, to reflect dispersion of the ovipositing moth population over available maize within the local landscape. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

The resilience of soil erosion rates under historical land use change in agroecosystems of Southern Spain

Land use change (LUC) is identified as one of the main drivers of soil erosion in the Mediterranean. However, very little information exists regarding the relationship between land use and erosion over longer time periods and on regional scales. We quantified the LUC in Southern Spain between 1956 and 2018, examining its effect on soil erosion and assessing the mitigation role of the permanent grassland (PG). The land use influence on erosion is represented by the RUSLE's C-factor, which was modelled using the Monte Carlo Method (MCM) based on historical LUC. Moreover, future LUC scenarios by 2038 were developed by binary logistic model (scFS) and by a complete conversion of PG to cropland (scPC), permanent crop (scPP) and forest and natural (scFP). Historically, Southern Spain has experienced an impressive intensification of its agricultural system. While soil loss variation is noted within the classes, no big variation is observed in cumulative erosion on a regional scale. The underlying reasons for this resilience are multifold, but mainly attributed to the fact that a small fraction of the total surface (20%), dominates total erosion (67%). The C-factor decrease in this area displays a LUC towards forest and natural area, suggesting an agriculture abandonment. On the other hand, the agricultural intensification that has taken place in the remainder of the area, contributes much less to overall soil erosion. Future LUC scenarios illustrate the importance of PG for erosion mitigation. scFS scenario does not project major changes. However, scCP and scPP, show an abrupt increase in regional erosion by 13% and 14%, while scFP shows a negligible reduction of erosion close to 0%. This allows to quantify the erosion mitigation offered by maintaining the PG and should be taken into account for future agricultural policy.

Spatio-temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil

Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil, were investigated. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured in 1993, 1997, 2001, 2005, 2009, 2013, and 2016 and processed using ArcGIS software. The proposed analytical approach handled complex data structures and allowed for a deeper understanding of LULC change over time. The results showed that there was a statistically significant change from regions of natural vegetation to pastures, agricultural regions, and land for other uses, accompanied by a significant trend of expansion of agricultural regions, appearing to stabilize from 2005. Furthermore, different patterns of LULC change were found according to soil type and elevation. In particular, the purple latosol soil type presented the highest expansion indexes since 2001, and the elevated agricultural areas have been expanding and/or stabilizing since 1997.

Analysis of Land Use Change and the Role of Policy Dimensions in Ecologically Complex Areas: A Case Study in Chongqing

China has adopted policies, such as the Grain for Green program (GFGP) and China’s Western Development Strategy, to maintain ecosystem sustainability and the rational use of land resources based on economic development. Existing studies have revealed the impact of these policies on land use and land cover change (LUCC). However, more research is needed to identify what would happen if the original trajectory of land use change were to continue unaffected by policy. In this research, we employed the future land use (FLUS) model to simulate land use changes in Chongqing under the natural scenario in 2020, assuming the existence of policy and natural contexts. The relative contribution conceptual model (RCCM) estimated the contribution of policies to LUCC, assessed the characteristics of LUCC in both situations using a complex network model, and analyzed the policies affecting LUCC. The findings revealed that cropland was the key land use type in both contexts, and the stability of the land use system in the natural context was greater than in the policy context. This research contributes to new research ideas for analyzing land use change and comprehending the role of policy execution in land use change.

Spatial–Temporal Land Loss Modeling and Simulation in a Vulnerable Coast: A Case Study in Coastal Louisiana

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km2 of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana.

Data Gap Filling Using Cloud-Based Distributed Markov Chain Cellular Automata Framework for Land Use and Land Cover Change Analysis: Inner Mongolia as a Case Study

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies.

Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

Spatial-Temporal Pattern and Evolution Trend of the Cultivated Land Use Eco-Efficiency in the National Pilot Zone for Ecological Conservation in China

The cultivated land use eco-efficiency (CLUE) is an important indicator to evaluate ecological civilization construction in China. Research on the spatial-temporal pattern and evolution trend of the CLUE can help to assess the level of ecological civilization construction and reveal associated demonstration and driving effects on surrounding areas. Based on the perspective of the CLUE, this paper obtains cultivated land use data pertaining to National Pilot Zones for Ecological Conservation in China and neighboring provinces from 2008 to 2018. In this study, the SBM-undesirable, Moran's I, and Markov chain models are adopted to quantitatively measure and analyze the CLUE and its temporal and spatial patterns and evolution trend. The research results indicate that the CLUE in the whole study area exhibited the characteristics of one growth, two stable, and two decline stages, with a positive spatial autocorrelation that increased year by year, and a spatial spillover effect was observed. Geographical spatial patterns and spatial spillover effects played a major role in the evolution of the CLUE, and there occurred a higher probability of improvement in the vicinity of cities with high CLUE values. In the future, practical construction experience should be disseminated at the provincial level, and policies and measures should be formulated according to local conditions. In addition, a linkage model between prefecture-level cities should be developed at the municipal level to fully manifest the positive spatial spillover effect. Moreover, we should thoroughly evaluate the risk associated with CLUE transition from high to low levels and establish a low-level early warning mechanism.

Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City

Rapid urbanization significantly affects the productivity of the terrestrial ecosystem and the foundation of regional ecosystem services, thereby detrimentally influencing the ecological environment and urban ecological security. The United Nations’ Sustainable Development Goals (SDGs) also require accurate and timely assessments of where people live in order to develop, implement and monitor sustainable development policies. Sustainable development also emphasizes the process of protecting the ecological environment for future generations while maintaining the current needs of mankind. We propose a comprehensive evaluation method for urban ecological quality (UEQ) using Landsat TM/ETM+/OLI/TIRS images to extract remote sensing information representing four ecological elements, namely humidity, greenness, heat and dryness. An improved comprehensive remote sensing ecological index (IRSEI) evaluation model is constructed by combining the entropy weight method and principal component analysis. This modeling is applied to the city of Wuhan, China, from 1995 to 2020. Spatial autocorrelation analysis was conducted on the geographic clusters of the IRSEI. The results show that (1) from 1995 to 2015, the mean IRSEI of Wuhan city decreased from 0.60 to 0.47, indicating that environmental deterioration overwhelmed improvements; (2) the global Moran’s I for IRSEI ranged from 0.535 to 0.592 from 1995 to 2020, indicating significant heterogeneity in its spatial distribution, highlighting that high and low clusters gradually developed at the edge of the city and at the city center, respectively; (3) the high clusters are mainly distributed in the Huangpi and Jiangxia districts, and the low clusters at the city center, which exhibits a dense population and intense human activity. This paper uses remote sensing index methods to evaluate UEQ as a scientific theoretical basis for the improvement of UEQ, the control of UEQ and the formulation of urban sustainable development strategies in the future. Our results show that the UEQ method is a low-cost, feasible and simple technique that can be used for territorial spatial control and spatiotemporal urban sustainable development.

Effectiveness of the natural resistance management refuge for Bt-cotton is dominated by local abundance of soybean and maize

Genetically engineered crops expressing Bacillus thuringiensis (Bt) Cry toxins have transformed insect management in maize and cotton, reducing insecticide use and associated off-target effects. To mitigate the risk that pests evolve resistance to Bt crops, the US Environmental Protection Agency requires resistance management measures. The approved resistance management plan for Bt maize in cotton production regions requires a structured refuge of non-Bt maize equal to 20% of the maize planted; that for Bt cotton relies on the presence of an unstructured natural refuge comprising both non-Bt crop and non-crop hosts. We examined how abundance of Bt crops (cotton and maize) and an important non-Bt crop (soybean) component of the natural refuge affect resistance to Bt Cry1Ac toxin in local populations of Helicoverpa zea, an important lepidopteran pest impacted by Bt cotton and maize. We show refuge effectiveness is responsive to local abundances of maize and cotton and non-Bt soybean, and maize, in its role as a source of H. zea infesting cotton and non-Bt hosts, influences refuge effectiveness. These findings have important implications for commercial and regulatory decisions regarding deployment of Bt toxins targeting H. zea in maize, cotton, and other crops and for assumptions regarding efficacy of natural refuges.

Urban Land Use Transitions and the Economic Spatial Spillovers of Central Cities in China’s Urban Agglomerations

Urbanization and land use transformation are typical characteristics of China in recent decades. Studying the effects of urban land use transitions (ULUT) on the economic spatial spillovers of central cities (ESSCC) can provide a reference for China to optimize cities’ land space layout and promote their coordinated development. Based on the direct and indirect effects of ULUT in central cities on the production factors and economic growth in other cities, this paper reveals the mechanisms underlying the influence of ULUT on ESSCC. Then, we usethe expanded geographical distance-weighted spatial Durbin model with the panel data of 152 Chinese urban agglomeration cities from 2003 to 2016 to empirically test it. The results show that, since 2003, the rate of urban land expansions, the level of urban land intensive use (ULIU), the degree of land marketization, and the urban land prices in China have increased substantially; and the proportionate supplies of industrial land, commercial land, and residential land have decreased. Moreover, ULUT between cities have significant spatial autocorrelations. The current ULUT have positive but small effects on ESSCC. Among them, ULIU has the greatest promotion effects on ESSCC. The impacts of ULUT on ESSCC vary greatly among urban agglomerations. The ULUT in central cities indirectly enhance the ESSCC, which mainly depend on the positive effects of ULUT on enterprise investment, infrastructure investment, labor and technological efficiency and the spatial spread effects of these production factors. This is the main intermediate mechanism by which the ULUT in central cities enhance the ESSCC. Continuing to strengthen ULIU, promote the improvement of land marketization, and establish and improve the coordination mechanism for the economic development of urban agglomerations will help to strengthen the ESSCC in urban agglomerations. The results provide evidence for how the Chinese government can enhance the ESSCC and promote the coordinated development of cities through ULUT under new urbanization.

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM_2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM_2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM_2.5. Subgroup analyses were performed to determine whether LRIs and PM_2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM_2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM_2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.

Disclosing contrasting scenarios for future land cover in Brazil: Results from a high-resolution spatiotemporal model

Gaining information on the dynamics of land cover changes is a valuable step towards improving practical conservation actions. In recent years, the Brazilian presidential elections in 2018 and the recovery from one of the nation's worst economic recessions defined a political scenario that has been causing shifts in the patterns of land cover change. A variety of national plans for the near-future exist and include the construction of new roads connecting remote Amazonian areas and large dams that could flood up to 10 million hectares. These development plans threaten environmental conservation, but the potential effects on the local or regional land cover are mostly unknown. In this work, we construct a model to evaluate the possible consequences of policy actions on land cover dynamics in the near-future at a high-resolution scale. The regression model extracts the historical relationships between land cover and spatial drivers of change, and its extrapolation for the future enables the simulation of scenarios for the national plans currently discussed in Brazil. We also simulate three scenarios based on the Representative Concentration Pathways of the Intergovernmental Panel on Climate Change, which makes contrasting management assumptions. The resulting maps indicate that considerable changes in land cover composition and configuration may occur even in a short period. The historical Brazilian economic forces make the decrease in natural vegetation probabilities challenging to stop even in an environmentally oriented scenario, where plans for the construction of new infrastructure are abruptly interrupted. Our results also indicate that environmental degradation cannot be prevented without coordinated efforts between public agencies with a broad diversity of development viewpoints.

Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS

Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

Analysis and Projection of Land-Use/Land-Cover Dynamics through Scenario-Based Simulations Using the CA-Markov Model: A Case Study in Guanting Reservoir Basin, China

Understanding the rate and process of land-use/land-cover (LULC) change in a watershed is essential for managing natural resources and achieving sustainable development. Therefore, this study aims to analyze historical LULC change from 1980 to 2010 and project future changes in 2030, 2060, and 2090 in the Guanting Reservoir Basin (GRB), China, a critical water-supplying watershed for China’s capital Beijing, through scenario-based simulations. Two LULC scenarios, ‘business-as-usual’ and ‘governance’ (Gov), were projected using the Cellular Automata-Markov (CA–Markov) model. Historical LULC trend analysis shows that built-up land increased from 2.6% in 1980 to 5.26% in 2010, while cropland, grassland, and water body decreased. LULC conversion analysis indicates that, in general, grassland, cropland, and woodland were converted to built-up area from 1980 to 2010. The BAU scenario projects a dramatic increase in built-up area, rising from 2296.98 km2 (5.26%) in 2010 to 11,757.35 km2 (26.93%) in 2090 at the expense of cropland and grassland areas. Conversely, the Gov scenario predicts an increase in water body, woodland, and grassland, encouraging sustainable development. Overall, these results provide useful inputs to the LULC planners and water resources managers to elaborate on eco-friendly policies and regulations for GRB.

The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China

Cultivated land is a basic resource that is related to the sustainable development of the global economy and society. Studying the spatial and temporal distribution of cultivated land and its influential factors at the township scale is an important way to improve its sustainable use. Based on the land use data in 2009 and 2015, this paper comprehensively uses kernel density estimation, spatial autocorrelation analysis, and the spatial autoregressive model to analyze the spatial distribution characteristics and influential factors of cultivated land. The results show that in 2009 and 2015, the maximum kernel density of cultivated land in Lishan Town was 31/km2 and 38/km2, respectively, and there is an increasing tendency for it in the future. The global spatial autocorrelation Moran’s I of the proportion of cultivated land area in the administrative villages of Lishan Town in 2009 and 2015 was 0.5251 and 0.3970, respectively. Cultivated land has significant spatial self-positive correlation agglomeration characteristics in spatial distribution. Based on spatial error model (SEM) analysis, the regression coefficients of the village were 0.236 and 0.196 in 2009 and 2015, respectively. The regression coefficients of the road were 0.632 and 0.630, respectively. The regression coefficients of the water system were 0.481 and 0.290, respectively. The regression coefficients of the topographic position index were −0.817 and −0.672, respectively. By comparing 2015 with 2009, the regression coefficients of each influential factor have been reduced to varying degrees.

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

Identifying rice stress on a regional scale from multi-temporal satellite images using a Bayesian method

Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57%. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index.

Spatial Autocorrelation Analysis of Multi-Scale Damaged Vegetation in the Wenchuan Earthquake-Affected Area, Southwest China

Major earthquakes can cause serious vegetation destruction in affected areas. However, little is known about the spatial patterns of damaged vegetation and its influencing factors. Elucidating the main influencing factors and finding out the key vegetation type to reflect spatial patterns of damaged vegetation are of great interest in order to improve the assessment of vegetation loss and the prediction of the spatial distribution of damaged vegetation caused by earthquakes. In this study, we used Moran’s I correlograms to study the spatial autocorrelation of damaged vegetation and its potential driving factors in the nine worst-hit Wenchuan earthquake-affected cities and counties. Both dependent and independent variables showed a positive spatial autocorrelation but with great differences at four aggregation levels (625 × 625 m, 1250 × 1250 m, 2500 × 2500 m, and 5000 × 5000 m). Shrubs can represent the characteristics of all damaged vegetation due to the significant linear relationship between their Moran’s I at the four aggregation levels. Clustering of similar high coverage of damaged vegetation occurred in the study area. The residuals of the standard linear regression model also show a significantly positive autocorrelation, indicating that the standard linear regression model cannot explain all the spatial patterns in damaged vegetation. Spatial autoregressive models without spatially autocorrelated residuals had the better goodness-of-fit to deal with damaged vegetation. The aggregation level 8 × 8 is a scale threshold for spatial autocorrelation. There are other environmental factors affecting vegetation destruction. Our study provides useful information for the countermeasures of vegetation protection and conservation, as well as the prediction of the spatial distribution of damaged vegetation, to improve vegetation restoration in earthquake-affected areas.

Assessment of Land-Use and Land-Cover Change in Guangxi, China

It is increasingly acknowledged that land-use and land-cover change has become a key subject that urgently needs to be addressed in the study of global environmental change. In the present study, supported by the long-time-series of land-use and land-cover data from 1990, 2000, and 2017, we used the land-use transition matrix, Markov chain model and Moran’s I to derive detailed information of the spatial patterns and temporal variation of the land-use and land-cover change; additionally, we highlight the deforestation/afforestation conversion process during the period of 1990–2017. The results show that a total of 4708 km² (i.e., 2.0% of the total area) changed in Guangxi from 1990 to 2017, while 418 km² of woodland has been lost in this region. The woodland lost (deforestation) and woodland gained (afforestation) were collocated with intensive forest practices in the past 27 years. The conversions from woodland to cropland and from woodland to grassland were the dominant processes of deforestation and afforestation, respectively. Steep slope cropland was one of the major conversion patterns of afforestation after 2000. This result is mainly explained by the implementation of the “Grain for Green Program” policy and the large-scale development of eucalyptus plantations. Further efforts should be made to control deforestation in this area. These findings can also be used as a reference in the formulation and implementation of sustainable woodland management policies.

Re-Arranging Space, Time and Scales in GIS: Alternative Models for Multi-Scale Spatio-Temporal Modeling and Analyses

The representations of space and time are fundamental issues in GIScience. In prevalent GIS and analytical systems, time is modeled as a linear stream of real numbers and space is represented as flat layers with timestamps. Despite their dominance in GIS and information visualization, these representations are inefficient for visualizing data with complex temporal and spatial extents and the variation of data at multiple temporal and spatial scales. This article presents alternative representations that incorporate the scale dimension into time and space. The article first reviews a series of work about the triangular model (TM), which is a multi-scale temporal model. Then, it introduces the pyramid model (PM), which is the extension of the TM for spatial data, and demonstrates the utility of the PM in visualizing multi-scale spatial patterns of land cover data. Finally, it discusses the potential of integrating the TM and the PM into a unified framework for multi-scale spatio-temporal modeling. This article systematically documents the models with alternative arrangements of space and time and their applications in analyzing different types of data. Additionally, this article aims to inspire the re-thinking of organizations of space, time, and scales in the future development of GIS and analytical tools to handle the increasing quantity and complexity of spatio-temporal data.

Spatial variation in determinants of agricultural land abandonment in Europe

Agricultural abandonment is widespread and growing in many regions worldwide, often because of agricultural intensification on productive lands, conservation policies, or the spatial decoupling of agricultural production from consumption. Abandonment has major environmental and social impacts, which differ starkly depending on the geographical context, as does its potential to serve as a land reservoir for recultivation. Understanding determinants of abandonment patterns, and especially how their influence varies across broad geographic extents, is therefore important. Using a pan-European map of agricultural abandonment derived from MODIS NDVI time series between 2001 and 2012, we quantified the importance of farm management, climatic, environmental, and socio-economic variables in explaining abandonment patterns. We chose a machine learning modelling framework that accounts for spatial variation in the relationship between abandonment and its determinants. We predicted abandonment probability as well as determinant coefficients for the entire study area and summarised them for regions under selected EU support schemes. Our results highlight that agricultural abandonment was mainly explained by climate conditions suboptimal for agriculture (i.e., low/high growing degrees days). Determinants related to farm management (smaller field size, lower yields) and socio-economic conditions (high unemployment, negative migration balance) also contributed to describing agricultural abandonment patterns in Europe. Several determinants influenced abandonment in strongly non-linear ways and we found substantial spatial non-stationarity effects, although abandonment patterns were equally well-explained by predictors specified with spatially constant and varying effects. Predicted abandonment probability was similar inside and outside EU support or conservation zones, whereas observed MODIS-based abandonment was generally higher outside these zones, suggesting that schemes such as Natura 2000 or High Nature Value Farmland likely influence abandonment patterns. Our work highlights the potential value of spatial boosting for gaining insights into land-use change processes and their outcomes, which should increase the ability of such models to inform context-specific, regionalised decision making.

An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015

Land-use and land-cover changes have important effects on ecology, human systems, the environment, and policy at both global and regional scales. Thus, they are closely related to human activities. The extraction of more details about land-use change and grassland degradation is necessary to achieve future sustainable development in Inner Mongolia. The current study presents the patterns and processes of land-use changes over space and time, while also analyzing grassland degradation that is based on an analysis of land-use changes using a transition matrix, the Markov chain model and Moran’s I index, and a combination of long-time-scale remote sensing data as the data source. The major results indicate the following. (1) In 1990–2015, 13% (123,445 km2) of the total study area, including eight land-use types, changed. Woodland increased the most and moderate grassland decreased the most. (2) Grassland degradation, which occupied 2.8% of the total area of Inner Mongolia, was the major land-use conversion process before 2000, while, after 2000, 8.7% of the total area was restored; however, grassland degradation may still be the major ecological issue in Inner Mongolia. (3) Environmental protection policies show a close relationship with land-use conversion.

An Analysis of Land-Use and Land-Cover Change in the Zhujiang–Xijiang Economic Belt, China, from 1990 to 2017

Land-use and land-cover change (LUCC) are currently contested topics in the research of global environment change and sustainable change. Identifying the historic land-use change process is important for the new economic development belt (the Zhujiang–Xijiang Economic Belt, ZXEB). During this research, based on long-time-series land-use and land-cover data, while using a combination of a transition matrix method and Markov chain model, the authors derive the patterns, processes, and spatial autocorrelations of land-use and land-cover changes in the ZXEB for the periods 1990–2000 and 2000–2017. Additionally, the authors discuss the spatial autocorrelation of land-use in the ZXEB and the major drivers of urbanization. The results indicate the following: (1) The area of cropland reduces during the two periods, and woodland decreases after the year 2000. The woodland is the most stable land-use type in both periods. (2) Built-up land expansion is the most important land-use conversion process; the major drivers of built-up land expansion are policy intervention, GDP (gross domestic product), population growth, and rural population migration. (3) Transition possibilities indicate that after 2000, most land-use activities become stronger, the global and local Moran’s I of all land-use types show that the spatial autocorrelations have become more closely related, and the spatial autocorrelation of built-up land has become stronger. Policies focus on migration from rural to urban, and peri-urban development is crucial for future sustainable urbanization.

Georeferenced soil provenancing with digital signatures

The provenance or origin of a soil sample is of interest in soil forensics, archaeology, and biosecurity. In all of these fields, highly specialized and often expensive analysis is usually combined with expert interpretation to estimate sample origin. In this proof of concept study we apply rapid and non-destructive spectral analysis to the question of direct soil provenancing. This approach is based on one of the underlying tenets of soil science – that soil pedogenesis is spatially unique, and thus digital spectral signatures of soil can be related directly, rather than via individual soil properties, to a georeferenced location. We examine three different multivariate regression techniques to predict GPS coordinates in two nested datasets. With a minimum of data processing, we show that in most instances Eastings and Northings can be predicted to within 20% of the range of each within the dataset using the spectral signatures produced via portable x-ray fluorescence. We also generate 50 and 95% confidence intervals of prediction and express these as a range of GPS coordinates. This approach has promise for future application in soil and environmental provenancing.

Moving forward socio-economically focused models of deforestation

Whilst high-resolution spatial variables contribute to a good fit of spatially explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision-making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001-2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approximately 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterization of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. Whilst considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterization of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects.

Global economic trade-offs between wild nature and tropical agriculture

Global demands for agricultural and forestry products provide economic incentives for deforestation across the tropics. Much of this deforestation occurs with a lack of information on the spatial distribution of benefits and costs of deforestation. To inform global sustainable land-use policies, we combine geographic information systems (GIS) with a meta-analysis of ecosystem services (ES) studies to perform a spatially explicit analysis of the trade-offs between agricultural benefits, carbon emissions, and losses of multiple ecosystem services because of tropical deforestation from 2000 to 2012. Even though the value of ecosystem services presents large inherent uncertainties, we find a pattern supporting the argument that the externalities of destroying tropical forests are greater than the current direct economic benefits derived from agriculture in all cases bar one: when yield and rent potentials of high-value crops could be realized in the future. Our analysis identifies the Atlantic Forest, areas around the Gulf of Guinea, and Thailand as areas where agricultural conversion appears economically efficient, indicating a major impediment to the long-term financial sustainability of Reducing Emissions from Deforestation and forest Degradation (REDD+) schemes in those countries. By contrast, Latin America, insular Southeast Asia, and Madagascar present areas with low agricultural rents (ARs) and high values in carbon stocks and ES, suggesting that they are economically viable conservation targets. Our study helps identify optimal areas for conservation and agriculture together with their associated uncertainties, which could enhance the efficiency and sustainability of pantropical land-use policies and help direct future research efforts.

Tropical forests are often destroyed to clear land for agriculture or to harvest forestry products, such as timber. However, the benefits derived from agriculture and these products are countered by the costs to the environment and the loss of ecosystem systems (the benefits that nature provides to humans). Little is known about how the economic benefits and costs of deforestation vary on a global scale. Knowing the distribution of benefits and costs would help identify regions where deforestation is most and least beneficial and thus could help select areas to focus conservation efforts. We studied the trade-offs between agricultural benefits, carbon emissions, and losses of multiple ecosystem services (ES) in tropical deforested areas around the world. We find large differences between costs and benefits globally. For instance, we identify the Atlantic Forest, areas around the Gulf of Guinea, and Thailand as areas where the benefits from agricultural conversion are greater than environmental costs, which could make it difficult to incentivize and implement biodiversity conservation strategies that are based on payments to farmers. By contrast, Latin America, insular Southeast Asia, and Madagascar represent areas with low agricultural benefits and high environmental costs. This suggests that these regions are economically viable conservation targets. Our study helps identify strategies to enhance the sustainability of land-use policies in the tropics.

Unsustainable development pathways caused by tropical deforestation

Global sustainability strategies require assessing whether countries' development trajectories are sustainable over time. However, sustainability assessments are limited because losses of natural capital and its ecosystem services through deforestation have not been comprehensively incorporated into national accounts. We update the national accounts of 80 nations that underwent tropical deforestation from 2000 to 2012 and evaluate their development trajectories using weak and strong sustainability criteria. Weak sustainability requires that countries do not decrease their aggregate capital over time. We adopt a strong sustainability criterion that countries do not decrease the value of their forest ecosystem services with respect to the year 2000. We identify several groups of countries: countries, such as Sri Lanka, Bangladesh, and India, that present sustainable development trajectories under both weak and strong sustainability criteria; countries, such as Brazil, Peru, and Indonesia, that present weak sustainable development but fail the strong sustainability criterion as a result of rapid losses of ecosystem services; countries, such as Madagascar, Laos, and Papua New Guinea, that present unsustainable development pathways as a result of deforestation; and countries, such as Democratic Republic of Congo and Sierra Leone, in which deforestation aggravates already unsustainable pathways. Our results reveal a large number of countries where tropical deforestation is both damaging to nature and not compensated by development in other sectors, thus compromising the well-being of their future generations.