Spatially-explicit Land Research Articles

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Land-use change CO2 emissions associated with agricultural products at municipal level in Brazil

Land-use change (LUC) accounted for approximately 66% of CO2 emissions in Brazil in 2020, with significant implications for carbon footprint of Brazilian agricultural products. Accurate LUC estimates associated with agriculture are critical to carbon footprint (CF) and life cycle assessment (LCA) studies and derived measures towards low-carbon supply chains. The aim of the study was to provide direct LUC (dLUC) estimates of CO2 emissions associated with a comprehensive set of agricultural products in Brazil at municipal-level and based on spatially-explicit land conversion data, appropriate for CF and LCA studies. The effect of different dLUC modeling choices on the results are also presented. The modeling followed IPCC guidelines and improved the BRLUC method. MapBiomas spatially-explicit data, municipality-level statistics, regionalized carbon stocks and a shared responsibility approach were combined to obtain dLUC emission rates for 64 crops, plus forestry and planted pastures, in the 5,570 Brazilian municipalities, as well as at state and national levels. It will be open access at www.embrapa.br. The most recent version led to an estimated 911 Mtons of CO2 associated with agriculture in 2019, 81% of that associated with planted pastures. National level dLUC emission rates for corn, pastures, soybean and sugarcane were estimated as 2.0, 4.1, 2.3 and 0.3 tCO2.ha−1.yr−1, respectively. The dLUC emissions are highly heterogeneous across the country and land uses, ranging from positive to negative. In general, they were higher in the Amazon biome, due to deforestation, and lower in Eastern Brazil, where agricultural areas are more consolidated. The resulting data is more consistent with dLUC rationale, IPCC guidelines and PAS2050 when previous land use is known and is recommended to be used, whenever data at farm level are not available. The study also shows the strong effect of different dLUC modeling choices on results and reinforces recommendations for further mitigation options.

Effectiveness of the integration of data balancing techniques and tree-based ensemble machine learning algorithms for spatially-explicit land cover accuracy prediction

Classification of remote sensing images has become a common practice in providing land use/land cover (LULC) maps. Among the main elements of a successful LC mapping task, accuracy assessment significantly influences the quality of decisions taken by the end users and prevents potential misinterpretations. The use of conventional accuracy assessment approaches often overlooks the spatial variation of LC accuracy. In this manner, per-pixel land cover accuracy (PLCA) mapping which provides spatially-explicit accuracy information of classified remote sensing images, emerged as a viable alternative to the traditional accuracy assessment approaches. This study comprehensively compared three tree-based ensemble machine learning algorithms including random forest (RF), rotation forest (RoF), and stochastic gradient boosting (SGB) for PLCA mapping. Moreover, considering the efficiency of the LC mapping practices, there are normally more instances linked with correctly classified pixels than incorrectly classified pixels in a given LC map. This condition leads to a serious challenge for PLCA mapping known as the class imbalance problem. Accordingly, this study also examined the feasibility of coupling three data balancing (DB) techniques (random over-sampling (ROS), synthetic minority oversampling technique (SMOTE), and adaptive synthetic sampling approach for unbalanced learning (ADASYN)) with the selected PLCA mapping algorithms for addressing the class imbalance problem. The experiments were employed on five different Sentinel-2 images with 20 km × 20 km dimensions acquired at different times and parts of Iran. The area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), geometric mean (GM), and overall accuracy (OA) were used to assess performance of the employed PLCA mapping methods. Among the PLCA mapping methods, RF-SMOTE was ranked first (with an average, AUROC = 0.84, AUPRC = of 0.91, GM = 0.81, and OA = 0.82), and was followed by RF-ADASYN and RoF-SMOTE. The results showed that integration of the SMOTE technique with the RF algorithm was the most successful practice for improving the performance of PLCA mapping and significantly outperformed all the algorithms without integration of DB techniques. As the PLCA maps provide the LULC's accuracy locally, they can be of significant use in refining the LULC mapping practices.

Identifying spatially-explicit land use factors associated with forest patch sizes in a forest reserve in Ghana

Anthropogenic land use is a major contributing factor to global environmental change, including forest cover loss and fragmentation. Land managers need up to date spatio-temporal land use information to plan for future land uses and mitigate adverse impacts. Here, we use spatially-explicit logistic regression analysis and methods from remote sensing and geographic information systems (GIS) to model the relationship between land use factors and forest patches using information from satellite images from the Tano-Offin Forest Reserve in Ghana. The study finds that it is 3,350 times more likely for forest patches that are not within 1 km from barren grounds to be greater than the mean patch size of forest (800 m2) (CI = 297-37,785, P = 0.000). Also, the study finds that it is 2,365 times more likely for the forest patches that are not within 1 km from access roads to be greater than the mean patch size of forest (CI=245-22,776; P = 0.000). These findings suggest that to maintain larger and contiguous patches of forest cover, there is a need for a policy that prevents the creation and expansion of logging-induced barren grounds. Also, the creation of buffers along access roads will be necessary to prevent further anthropogenic influences that are likely to fragment forest cover.

Scenarios of climate adaptation potential on protected working lands from management of soils

Management of protected lands may enhance ecosystem services that conservation programs were designed to protect. Practices that build soil organic matter on agricultural lands also increase soil water holding capacity, potentially reducing climatic water deficit (CWD), increasing actual evapotranspiration (AET) and increasing groundwater recharge (RCH). We developed nine spatially-explicit land use and conservation scenarios (2001–2100) in the LUCAS land use change model to address two questions for California working lands (cropland and rangeland): How does land use change limit opportunities to manage soils for hydrologic climate adaptation benefits? To what extent and where can soil management practices increase climate adaptation on protected working lands? Hydrologic benefits [Σ(∆CWD, ∆AET, ∆RCH)] due to soil management were simulated in the Basin Characterization Model (a state-wide water balance model) for two Representative Concentration Pathway 8.5 climate models. LUCAS simulated land conversion and new conservation easements with potential for maximum hydrologic benefits. Climate drove differences in lost potential for water benefits due to urbanization (33.9–87.6 m3 × 106) in 2050. Conflict between development pressure and potential hydrologic benefits occurred most in Santa Clara County in the San Francisco Bay Area and Shasta County in Northern Sacramento Valley. Hydrologic benefits on easements were similar in magnitude to losses from development. Water savings from management of California Land Conservation (a.k.a. Williamson) Act contract lands were an order of magnitude greater, totaling over 460 m3 × 106 annually in a drier climate by 2050. Few counties provide most benefits because of soil properties, climate and land area protected. The increase in hydrologic benefits varies by agricultural practice and adoption rate, land use type and configuration, and terms of conservation agreements. The effectiveness of programs designed to improve climate adaptation at county to state scales will likely increase by taking this variability into consideration.

Scale-dependent impacts of urban and agricultural land use on nutrients, sediment, and runoff

We coupled a spatially-explicit land use/land cover (LULC) change model, Dinamica EGO, (Environment for Geoprocessing Objects), with the Chesapeake Bay Watershed Model (CBWM) to project the impact of future LULC change on loading of total nitrogen (TN), total phosphorous (TP) and total suspended solids (TSS) as well as runoff volume in the watersheds surrounding Virginia's Shenandoah National Park in the eastern United States. We allowed for the dynamic transition of four LULC classes, Developed, Forest, Grasses (including both pasture and hayfields) and Crops. Using 2011 as a baseline scenario and observed differences in LULC between 2001 and 2011, we estimated the temporal and spatial patterns of LULC change as influenced by physiographic and socio-economic drivers 50 years in the future (2061). Between transitions of the four LULC classes, the greatest absolute change occurred between the gain in total Developed land and loss in total Forest. New Developed land was driven primarily by distance to existing Developed land and population density. Major findings on the effect of LULC change on watershed model outputs were that: the impact of LULC change on pollutant loading and runoff volume is more pronounced at finer spatial scales; increases in the area of Grasses produced the greatest increase in TP loading, while loss of Forest increased TN, TSS, and runoff volume the most; and land-river segments with a greater proportion of Developed or a smaller proportion of Forest in the 2011 scenario experienced a greater change in runoff than other land-river segments. Results of this study illustrate the potential impact of projected LULC change on nutrient and sediment loads which can adversely impact water quality. Studies like this contribute to a broader understanding of how ecosystem services such as fresh water respond to LULC change, information relevant to those in planning and watershed management.

Effects of local land-use planning on development and disturbance in riparian areas

Land-use change can significantly affect the provision of ecosystem services. On a local scale, zoning laws and other land-use regulations are commonly used to influence land-use change, but their effectiveness is often unclear. We evaluate the effectiveness of local land-use planning in concentrating development and minimizing impacts in riparian areas. We use spatially-explicit land cover data from the USGS Land Cover Trends project to measure development and disturbance rates before and after implementation of Oregon’s land-use planning system. We apply a difference-in-difference estimator to address the problem of non-random assignment of regulations on the landscape. We find that land-use laws in Oregon have concentrated development inside of UGBs and lowered development rates in riparian areas. However, disturbance in riparian areas has increased inside of UGBs. Overall, our findings suggest that local land-use planning can be an effective tool for promoting the provision of non-market ecosystem services.

Land Use Change and Policy in Iowa’s Loess Hills

<p class="sar-body"><span lang="EN-US">Land use changes have important implications on ecosystems and society. Detailed identification of the nature of land use changes in any local region is critical for policy design. In this paper, we quantify land use change in Iowa’s Loess Hills ecoregion, which contains much of the state’s remaining prairie grasslands. We employ two distinct panel datasets, the National Resource Inventory data and multi-year Cropland Data Layers, that allow us to characterize spatially-explicit land use change in the region over the period 1982-2010. We analyze land use trends, land use transitions and crop rotations within the ecoregion, and contrast these with county and state-level changes. To better comprehend the underlying land use changes, we evaluate our land use characterizing metrics conditional on soil quality variables such as slope and erodibility. We also consider the role of contemporary agricultural policy and commodity markets to seek explanations for land use changes during the period of our study. Although crop production has expanded on the Loess Hills landform since 2005, much of the expansion in corn acres has been from reduced soybean acreage. We find that out of the total 258 km<sup>2</sup> increase in corn acreage during 2005-’10, about 100 km<sup>2</sup> transitioned from soybeans. Data also indicate intensifying monoculture with higher percentage of corn plantings for two to four consecutive years during 2000-’10. In addition, crop production is found to have moved away from more heavily sloped land. Cropping does not appear to have increased on lands with higher crop productivity.</span></p>

Deforestation scenarios for the Bolivian lowlands

Tropical forests in South America play a key role in the provision of ecosystem services such as carbon sinks, biodiversity conservation, and global climate regulation. In previous decades, Bolivian forests have mainly been deforested by the expansion of agricultural frontier development, driven by the growing demands for beef and other productions. In the mid-2000s the Movimiento al Socialismo (MAS) party rose to power in Bolivia with the promise of promoting an alternative development model that would respect the environment. The party passed the world’s first laws granting rights to the environment, which they termed Mother Earth (Law No. 300 of 2012), and proposed an innovative framework that was expected to develop radical new conservation policies. The MAS conservationist discourse, policies, and productive practices, however, have since been in permanent tension. The government continues to guarantee food production through neo-extractivist methods by promoting the notion to expand agriculture from 3 to 13millionha, risking the tropical forests and their ecosystem services. These actions raise major environmental and social concerns, as the potential impacts of such interventions are still unknown. The objective of this study is to explore an innovative land use modeling approach to simulate how the growing demand for land could affect future deforestation trends in Bolivia. We use the LuccME framework to create a spatially-explicit land cover change model and run it under three different deforestation scenarios, spanning from the present–2050. In the Sustainability scenario, deforestation reaches 17,703,786ha, notably in previously deforested or degraded areas, while leaving forest extensions intact. In the Middle of the road scenario, deforestation and degradation move toward new or paved roads spreading across 25,698,327ha in 2050, while intact forests are located in Protected Areas (PAs). In the Fragmentation scenario, deforestation expands to almost all Bolivian lowlands reaching 37,944,434ha and leaves small forest patches in a few PAs. These deforestation scenarios are not meant to predict the future but to show how current and future decisions carried out by the neo-extractivist practices of MAS government could affect deforestation and carbon emission trends. In this perspective, recognizing land use systems as open and dynamic systems is a central challenge in designing efficient land use policies and managing a transition towards sustainable land use.

A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France)

Global climate and land use changes could strongly affect soil erosion and the capability of soils to sustain agriculture and in turn impact regional or global food security. The objective of our study was to develop a method to assess soil sustainability to erosion under changes in land use and climate. The method was applied in a typical mixed Mediterranean landscape in a wine-growing watershed (75 km2) within the Languedoc region (La Peyne, France) for two periods: a first period with the current climate and land use and a second period with the climate and land use scenarios at the end of the twenty-first century. The Intergovernmental Panel on Climate Change A1B future rainfall scenarios from the Météo France General circulation model was coupled with four contrasting land use change scenarios that were designed using a spatially-explicit land use change model. Mean annual erosion rate was estimated with an expert-based soil erosion model. Soil life expectancy was assessed using soil depth. Soil erosion rate and soil life expectancy were combined into a sustainability index. The median simulated soil erosion rate for the current period was 3.5 t/ha/year and the soil life expectancy was 273 years, showing a low sustainability of soils. For the future period with the same land use distribution, the median simulated soil erosion rate was 4.2 t/ha/year and the soil life expectancy was 249 years. The results show that soil erosion rate and soil life expectancy are more sensitive to changes in land use than to changes in precipitation. Among the scenarios tested, institution of a mandatory grass cover in vineyards seems to be an efficient means of significantly improving soil sustainability, both in terms of decreased soil erosion rates and increased soil life expectancies.

Land use compounds habitat losses under projected climate change in a threatened California ecosystem.

Given the rapidly growing human population in mediterranean-climate systems, land use may pose a more immediate threat to biodiversity than climate change this century, yet few studies address the relative future impacts of both drivers. We assess spatial and temporal patterns of projected 21st century land use and climate change on California sage scrub (CSS), a plant association of considerable diversity and threatened status in the mediterranean-climate California Floristic Province. Using a species distribution modeling approach combined with spatially-explicit land use projections, we model habitat loss for 20 dominant shrub species under unlimited and no dispersal scenarios at two time intervals (early and late century) in two ecoregions in California (Central Coast and South Coast). Overall, projected climate change impacts were highly variable across CSS species and heavily dependent on dispersal assumptions. Projected anthropogenic land use drove greater relative habitat losses compared to projected climate change in many species. This pattern was only significant under assumptions of unlimited dispersal, however, where considerable climate-driven habitat gains offset some concurrent climate-driven habitat losses. Additionally, some of the habitat gained with projected climate change overlapped with projected land use. Most species showed potential northern habitat expansion and southern habitat contraction due to projected climate change, resulting in sharply contrasting patterns of impact between Central and South Coast Ecoregions. In the Central Coast, dispersal could play an important role moderating losses from both climate change and land use. In contrast, high geographic overlap in habitat losses driven by projected climate change and projected land use in the South Coast underscores the potential for compounding negative impacts of both drivers. Limiting habitat conversion may be a broadly beneficial strategy under climate change. We emphasize the importance of addressing both drivers in conservation and resource management planning.

Multi-Criteria Spatial Decision Support System DECERNS

Land-use planning and environmental management often requires an implementation of both geoyspatial information analysis and value-driven criteria within the decision-making process. DECERNS (Decision Evaluation in Complex Risk Network Systems) is a web-based distributed decision support system for multicriteria analysis of a wide range of spatially-explicit land management alternatives. It integrates mainly basic and some advanced GIS functions and implements several Multi-Criteria Decision Analysis (MCDA) methods and tools. DECERNS can also be integrated with a model server containing generic and site specific models for in-depth analysis of project and environmental risks as well as other decision criteria under consideration. This paper provides an overview of the modeling approaches as well as methods and tools used in DECERNS. Application of the DECERNS WebSDSS (Web-based Spatial Decision Support System) for a housing site selection case study is presented.