Impact of cropland displacement on the potential crop production in China:a multi-scale analysis

Changes in the amount and location of cropland areas may affect the potential crop production at different spatial scales. However, most studies ignore the impacts of cropland displacement on potential crop production. In many countries, cropland protection policies mainly aim for no loss in cropland area, while there is no restriction on change of cropland location. Taking China as the study area, we analyze the impacts of cropland displacement on potential crop production at four administrative levels during the period 2000 and 2018. At the national level, we find a net decrease in cropland area of 0.81 Mha, while another 19.63 Mha was displaced. The former led to a decrease of 4.20 Mton in potential crop production, while the latter resulted in a decrease of 43.26 Mton as a result of lower quality of the newly cultivated lands. In other words, cropland displacement explains 91% of the total loss in potential crop production at the national scale. However, the contribution of cropland displacement to total change in potential crop production is increasingly smaller at provincial level, municipal level, and county levels. These findings highlight the importance of geographic location on crop production and suggest that cropland policies should consider geographic location in addition to cropland area

Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion

Global Change of Land-Sparing and Land-Sharing Patterns over the Past 30 Years: Evidence from Remote Sensing and Statistics

Agricultural expansion, driven by the increasing demand on crops, poses a severe threat to the global environment and to human welfare. Regarded as an effective landscape pattern for balancing biodiversity and food security, land sparing bears high expectations from ecologists. To reflect the spatial-temporal pattern change of land sparing, we calculate a land sparing/sharing (LSS) index on the basis of a remote sensing dataset. The land-sparing pattern has shown an apparent increasing trend globally, especially in hotspots, including the eastern United States, central South America, northern Europe, Kazakhstan, southeastern China, and the Korean Peninsula. Meanwhile, the land-sharing pattern has been increasing in some other regions, including in the southeast of South America, western Europe, central Europe, southern Europe, and northwestern China. However, according to statistical datasets, contrary to the overall increasing trend of land sparing, passive land sparing, incentivized by lower food prices due to increased yields, is decreasing, especially in countries with high levels of development. Our results reveal the global trends in land sparing and passive land sparing, providing support for balancing biodiversity conservation and food security among countries and ecoregions

Projecting future impacts of cropland reclamation policies on carbon storage

Cropland reclamation policies result in carbon storage loss by the conversion of natural land. However, the future impacts of cropland reclamation policies (CRP) on carbon storage have seldom been explored. Taking Hubei, China as study area, this study assesses the impacts of cropland reclamation policies before and after optimization on carbon storage from 2010 to 2030. The LAND System Cellular Automata model for Potential Effects (LANDSCAPE) was used to simulate the land use patterns in 2030, while the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) Carbon Storage and Sequestration model was applied to calculate the changes in carbon storage. Results indicate that carbon storage loss due to cropland reclamation policies is expected to increase from 0.48 Tg·C to 4.34 Tg·C between 2010 and 2030 in Hubei. This increase is related to the loss of wetland and forest. Carbon storage loss can be reduced by 52%–73% by protecting carbon-rich lands. This study highlights the importance of considering the carbon storage loss when implementing cropland reclamation policies

Land cover change from national to global scales:A spatiotemporal assessment of trajectories, transitions and drivers

Changes in global land cover (LC) have significant consequences for global environmental change, impacting the sustainability of biogeochemical cycles, ecosystem services, biodiversity, and food security. Different forms of LC change have taken place across the world in recent decades due to a combination of natural and anthropogenic drivers, however, the types of change and rates of change have traditionally been hard to quantify. This thesis exploits the properties of the recently released ESA-CCI-LC product – an internally consistent, high-resolution annual time-series of global LC extending from 1992 to 2018. Specifically, this thesis uses a combination of trajectories and transition maps to quantify LC changes over time at national, continental and global scales, in order to develop a deeper understanding of what, where and when significant changes in LC have taken place and relates these to natural and anthropogenic drivers. This thesis presents three analytical chapters that contribute to achieving the objectives and the overarching aim of the thesis. The first analytical chapter initially focuses on the Nile Delta region of Egypt, one of the most densely populated and rapidly urbanising regions globally, to quantify historic rates of urbanisation across the fertile agricultural land, before modelling a series of alternative futures in which these lands are largely protected from future urban expansion. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion between 1992 and 2015. Furthermore, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density could be achieved, hence preserving large areas of fertile agricultural land within the Nile Delta. The second analytical chapter goes on to examine LC changes across sub-Saharan Africa (SSA), a complex and diverse environment, through the joint lenses of political regions and ecoregions, differentiating between natural and anthropogenic signals of change and relating to likely drivers. The results reveal key LC change processes at a range of spatial scales, and identify hotspots of LC change. The major five key LC change processes were: (i) “gain of dry forests” covered the largest extent and was distributed across the whole of SSA; (ii) “greening of deserts” found adjacent to desert areas (e.g., the Sahel belt); (iii) “loss of tree-dominated savanna” extending mainly across South-eastern Africa; (iv) “loss of shrub-dominated savanna” stretching across West Africa, and “loss of tropical rainforests” unexpectedly covering the smallest extent, mainly in the DRC, West Africa and Madagascar. The final analytical chapter considers LC change at the global scale, providing a comprehensive assessment of LC gains and losses, trajectories and transitions, including a complete assessment of associated uncertainties. This chapter highlights variability between continents and identifies locations of high LC dynamism, recognising global hotspots for sustainability challenges. At the national scale, the chapter identifies the top 10 countries with the largest percentages of forest loss and urban expansion globally. The results show that the majority of these countries have stabilised their forest losses, however, urban expansion was consistently on the rise in all countries. The thesis concludes with recommendations for future research as global LC products become more refined (spatially, temporally and thematically) allowing deeper insights into the causes and consequences of global LC change to be determined

A spatiotemporal epidemiological investigation of the impact of environmental change on the transmission dynamics of Echinococcus spp. in Ningxia Hui Autonomous Region, China

Background: Human echinococcoses are zoonotic parasitic diseases of major public health importance globally. According to recent estimates, the geographical distribution of echinococcosis is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and might be affected by global environmental change over time. The aims of my research were: 1) to assess and quantify the spatiotemporal variation in land cover and climate change in Ningxia Hui Autonomous Region (NHAR); 2) to identify highly endemic areas for human echinococcoses in NHAR, and to determine the environmental covariates that have shaped the local geographical distribution of the disease; 3) to develop spatial statistical models that explain and predict the spatiotemporal variation of human exposure to Echinococcus spp. in a highly endemic county of NHAR; and 4) to analyse associations between the environment and the spatiotemporal variation of human exposure to the parasites and dog infections with Echinococcus granulosus and Echinococcus multilocularis in four echinococcosis-endemic counties of NHAR. Methods: Data on echinococcosis infections and human exposure to E. granulosus and E. multilocularis were obtained from different sources: 1) A hospital-based retrospective survey of human echinococcosis cases in NHAR between 1992 and 2013; 2) three cross-sectional surveys of school children conducted in Xiji County in 2002–2003, 2006–2007 and 2012–2013; and 3) A cross-sectional survey of human exposure and dog infections with E. granulosus and E. multilocularis conducted in Xiji, Haiyuan, Guyuan and Tongxin Counties. Environmental data were derived from high-resolution (30 m) imagery from Landsat 4/5-TM and 8-OLI and meteorological reports provided by the Chinese Academy of Sciences. Image analysis techniques and a Bayesian statistical framework were used to conduct a land cover change detection analyses and to develop regression models that described and quantified climate trends and the environmental factors associated with echinococcosis risk at different spatial scales. Results: The land cover changes observed in NHAR from 1991 to 2015 concurred with the main goals of a national policy on payments for ecosystem services, implemented in the Autonomous Region, in increasing forest and herbaceous vegetation coverages and in regenerating bareland. Statistically significant positive trends were observed in annual, summer and winter temperatures in most of the region, and a small magnitude change was found in annual precipitation, in the same 25-year period. The south of NHAR was identified as a highly endemic area for cystic echinococcosis (CE; caused by E. granulosus) and alveolar echinococcosis (AE; caused by E. multilocularis). Selected environmental covariates explained most of the spatial variation in AE risk, while the risk of CE appeared to be less spatially variable at the township level. The risk of exposure to E. granulosus expanded across Xiji County from 2002–2013, while the risk of exposure to E. multilocularis became more confined in communities located in the south of this highly endemic area. In 2012–2013, the predicted seroprevalences of human exposure to E. granulosus and dog infection with this parasite were characterised by similar geographical patterns across Xiji, Haiyuan, Guyuan and Tongxin Counties. By contrast, the predicted high seroprevalence areas for human exposure and dog infection with E. multilocularis did not coincide spatially. Climate, land cover and landscape fragmentation played a key role in explaining some of the observed spatial variation in the risk of infection with Echinococcus spp. among schoolchildren and dogs in the south of NHAR at the village level. Conclusions: The findings of this research defined populations at a high risk of human exposure to E. granulosus and E. multilocularis in NHAR. The research provides evidence on the potential effects of landscape regeneration projects on the incidence of human echinococcoses due to the associations found between the infections and regenerated land. This information will be essential to track future requirements for scaling up and targeting the control strategies proposed by the National Action Plan for Echinococcosis Control in China and may facilitate the design of future ecosystem management and protection policies and a more effective response to emerging local environmental risks. The predictive models developed as part of this research can also be used to monitor echinococcosis infections and the emergence in Echinococcus spp. transmission in the most affected areas

Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery

Tropical disturbed forests play an important role in global carbon sequestration due to their rapid post-disturbance biomass accumulation rates. However, the accurate estimation of the carbon sequestration capacity of disturbed forests is still challenging due to large uncertainties in their spatial distribution. Using Google Earth Engine (GEE), we developed a novel approach to map cumulative disturbed forest areas based on the 27-year time-series of Landsat surface reflectance imagery. This approach integrates single date features with temporal characteristics from six time-series trajectories (two Landsat shortwave infrared bands and four vegetation indices) using a random forest machine learning classification algorithm. We demonstrated the feasibility of this method to map disturbed forests in three different forest ecoregions (seasonal, moist and dry forest) in Mato Grosso, Brazil, and found that the overall mapping accuracy was high, ranging from 81.3% for moist forest to 86.1% for seasonal forest. According to our classification, dry forest ecoregion experienced the most severe disturbances with 41% of forests being disturbed by 2010, followed by seasonal forest and moist forest ecoregions. We further separated disturbed forests into degraded old-growth forests and post-deforestation regrowth forests based on an existing post-deforestation land use map (TerraClass) and found that the area of degraded old-growth forests was up to 62% larger than the extent of post-deforestation regrowth forests, with 18% of old-growth forests actually being degraded. Application of this new classification approach to other tropical areas will provide a better constraint on the spatial extent of disturbed forest areas in Tropics and ultimately towards a better understanding of their importance in the global carbon cycle

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Spatial assessment and management of regional ecosystem services in the Yangtze River Delta Region

The Yangtze River Delta (YRD) metropolitan region is one of the most rapidly urbanized regions in China and has experienced a remarkable period of population growth (at an annual growth rate of 3.0%), and urbanization (at an annual growth rate of 9.2%). Rapid urbanization has dramatically changed land use/land cover patterns and ecosystems in the region, causing widespread environmental problems such as habitat fragmentation, aggravation of environmental pollution, decline in biodiversity and ecosystem degredation. These problems have restricted the sustainable development of socio-economic system of the Yangtze River Delta Region. Facing the challenges, the Yangtze River Delta Region is carrying out the practice of regional integration planning and cooperation in environmental governance, which urgently needs the guidance of relevant theories and methods. Some of the key environmental policy pilots have been carried on in this region, such as the Ecological Red Line Policy (Bai et al, 2016; Lü et al, 2013). This policy has one of the main objectives of protecting important eco-function areas i.e. ecosystem service hot spots, to deliver services such as water storage, clean drinking water, and carbon sequestration, and to maintain ecological safety to support economic and social development, which an important policy orientation of ecosystem services approach.Ecosystem services are the contributions of ecosystem structure and function to human well-being, connecting natural and socio-economic systems. The ecosystem services approach is considered to be one of the important decision support tools for guiding and formulating environmental policies. Based on the theories and methods of ecosystem services, combined with local expert knowledge, remote sensing and GIS technologies, this dissertation aims is mainly to develop a comprehensive framework of ecosystem services assessment and decision support for rapid urbanization regions.Based on this framework, the spatial characteristics, supply-demand relationship and flow direction of ecosystem services in the Yangtze River Delta Region are analysed and evaluated. Main results of this thesis are as follows:(1) According to the characteristics of the regional ecosystems of the Yangtze River Delta Region, combined with local expert knowledge, the Burkhard’s scoring and assessment method of ecosystem services was improved, and the score matrix between twelve ecosystem types and twenty-three ecosystem services in the Yangtze River Delta Region was established.(2) Based on DPSIR model, the characteristics of the social-ecological complex ecosystem and change of ecosystem services in the Yangtze River Delta Region were analysed. The main ecological and environmental problems were identified. Causes and main driving forces of decline in ecosystem services were revealed in the region.(3) Based on ARCGIS platform, the status quo of ecosystem services in the Yangtze River Delta region was analysed and evaluated. The spatial differentiation characteristics and main impact factors of ecosystem services in the Yangtze River Delta region were clarified. The hot spots of total ecosystem services were aggregated in the southwest areas, while the cold spots were distributed in the middle and northeast areas of the region. The hot spots of supporting services and regulating services aggregately distributed in the southwest mountainous areas while hot spots of provisioning services mainly in the northeast plain, and high value of cultural services widespread in the waterbodies and southwest mountainous areas. The spatial heterogeneity is determined by biophysical features and land use types. Based on the assessment, six major ecosystem services functional zones were divided: (I) South Ecological Integrity Conservation Zone, (II) Southwest Mountainous and Hilly Forest Ecological Zone, (III) Northeast Plain Agriculture Ecological Zone, (IV) Middle Aquatic Ecological Conservation Zone, (V) Eastern Coastal Estuaries Ecological Zone, and (VI) Urban Development Area., and the corresponding management strategies on the basis of environmental problems and ecosystem services characteristics in each of the functional zones were put forward.(4) Using regional spatial data in net primary productivity, the quality levels of forest and cropland were graded and the previous scores of ecosystem services in forest and cropland were calibrated. Then, the hot spots and clustering patches of forest and farmland ecosystem services were identified by ARCGIS tools. Finally, the forest ecosystem conservation areas (red line) and cropland ecosystem conservation areas (red line) in the Yangtze River Delta region were delineated.(5) Based on the improved Burkhard’s supply-demand budget of ecosystem services method, the budgets of three regulating services (erosion regulating service, flood regulating service and water purification regulating service) of the sixteen core cities in the region were established, and the characteristics of surplus and deficit of three services of the cities in the region were analysed. Combing the budget with analysis of flow direction of ecosystem services, the potential provisioning cities and the benefiting cities of ecosystem services are identified. On the basis of the results, the potential model of regional inter-city environmental cooperation is proposed.This dissertation not only improves the methods of ecosystem service assessment and decision support in rapidly urbanized regions, but also makes contributions to the guidance in delineation of ecological red line, regional environmental cooperation and sustainable development in the Yangtze River Delta Region

Spatial associations between household and community livelihood capitals in rural territories : an example from the Mahanadi Delta, India

Community-level assets, such as environmental conditions (elevation, rainfall, soil quality), distance to natural resources (forest, wetlands) and distance to services (markets, hospitals) are a significant component of rural livelihoods. This paper argues that common-pool resources (community capitals) should be differentiated from private goods (household capitals) within the Sustainable Livelihoods Framework, as they operate under different dynamics of decision-making and management. It presents methods for differentiating household capital from community assets, identifies patterns in their spatial distribution and analyzes their interactions. Results of the project show that households trade their natural assets to cope with environmental shocks such as cyclones and flooding.UK Department for International Development (DFID