•Evaluates methods for identifying land-use conflict from participatory spatial data.•Methods using land-use preferences performed best across multiple land uses.•Results were sensitive to the ...quantity of spatial data and participants.•Operationalises confidence levels to assess inferential quality of results.•Suggests prioritisation to areas with high potential conflict and high confidence.
Spatial social data collected through participatory mapping are increasingly used to assess social dimensions for land use planning and management. However, there has been limited research to evaluate alternative approaches to identify potential land-use conflict. Using data from Queensland, Australia, we applied multiple approaches (land-use preferences, weighted preferences, combined place values and land-use preferences, and value compatibility scoring to identify land-use conflict potential and to assess these methods for four different land uses (residential development, tourism development, mining, and conservation). The performance of these approaches were evaluated using selected reference sites in the study area to determine which spatial attributes and methods were most predictive of conflict potential. Weighted preferences, and combined place values and land-use preferences were most effective for all land use types. The conflict mapping results for mining and conservation were sensitive to the number of place value and land-use preference points available for analysis and the number of individuals participating in the mapping process. To determine the inferential quality of conflict mapping results, we operationalised confidence levels based on the number of unique participants that mapped preferences in a given location. Overall, the highest confidence in mapped results was observed for tourism development, followed by mining, conservation, and residential development. Confidence levels varied across the study area and by reference sites. The findings of this study increase the external validity of preference-based conflict mapping methods while demonstrating a means to assess the inferential quality of conflict mapping results. The generation of confidence levels can assist in the prioritization and allocation of planning resources to places with both high conflict potential and high confidence.
Land use consolidation aims to address food insecurity challenges in Rwanda. However, there is contradictory evidence on whether this tool has met food security objectives or not. This study ...addresses two questions: How has the land use consolidation improved (or not improved) food security at the local level? How can food security challenges be addressed using a renewed approach to land use consolidation that adopts a tenure responsive land use planning procedure? We investigate these questions in Nyange Sector (in the Musanze District) of Rwanda using mixed research methods. The study generates theoretical and policy relevant outcomes. Theoretically, it links the concept of tenure responsive land-use planning to food security improvements. Policy wise, it provides an operational framework for implementing land use consolidation to make it more responsive to food security (based on tenure responsive land-use planning measures) in Rwanda.
Planners, engineers, and policymakers take a great interest in effective ways to mitigate carbon emissions by land use planning. Hence, the interrelationship between land use and carbon emissions has ...become a mutually concerned issue with the rapid economic growth and urbanization in the world. As the second largest source of carbon emissions, it has been argued whether transport-related emissions could be reduced by spatial planning and land use policy, since land use pattern, which affects urban form and configuration, is the origin of travel behaviors and has a close interaction with transport system. In this study, a traffic assignment model is incorporated with carbon emission evaluation at an individual trip level based on Origin-Destination person trip data in Changzhou, China, taking into account road types, capacity, velocity, and volume. On the other hand, land use pattern and landscape metrics are analyzed based on Traffic Analysis Zones (TAZ) which are the basic geographic unit for person trip survey and transport planning. Then, by using the traffic assignment results we investigate the spatial distribution pattern of carbon emitted by person trips at the level of road link, and examine whether transport-related carbon emissions are correlated to land use pattern. Our study observes that the distribution of land use pattern and transport-related carbon emissions varies considerably between urban and rural areas, and arterial and collector streets inside built-up area are the major contributors of carbon emissions rather than major road, highway, or expressway. A regression analysis demonstrates that land use and landscape pattern are significantly correlated with transport-related carbon emissions. The correlations between carbon emission intensities, land for residential and commercial uses, and patch density (PD) shape inverted-U curves, which are consistent with the trends shown in the Kuznets Curve. Findings of this study are further developed to provide policy implications to resolve China’s low-carbon urban development. Since the Kuznets Curves do exist for the observed correlations between carbon emissions and land use, corresponding land use strategies like mixed high-density land use, compact city, etc. deserve more attention.
During past four decades, in post economic reforms period, Delhi and its surrounding regions has attracted a large number of populations which led to the rapid transformation of its LULC pattern. ...Therefore, this study is aimed to analyze the LULC changes during 1990-2018 as well as the growth and pattern of built-up surfaces in relation to the population growth and migration in the suburbs of Delhi metropolitan city which is also known as the National Capital Region (NCR). The Landsat 5 (TM) and Landsat 8 (OLI/TIRS) data has been used for the LU/LC classification of Delhi NCR. The K means clustering technique was applied on the Landsat data for the LULC classification and then the change detection technique was used to quantify the LULC change. The result shows that the considerable changes in LULC have occurred with continuous increase in built-up area and open/fallow land and decrease in agriculture land and vegetation over the study time period. Built-up area increased by about 326 percent and open/fallow land by 44 percent while the agricultural land and vegetation cover have decreased by 12 percent and 34 percent of the total area of study respectively during the study period. Built-up area has mostly increased at the expense of agricultural land and vegetation cover while vegetation cover has been transformed into Built-up area, Ridge and Agriculture. The statistical analysis shows that the association between built-up expansion and the population and migrants varies from weak to high but the coefficient of determination was always positive.
Agricultural drainage of organic soils has resulted in vast soil subsidence and contributed to increased atmospheric carbon dioxide (CO₂) concentrations. The Sacramento‐San Joaquin Delta in ...California was drained over a century ago for agriculture and human settlement and has since experienced subsidence rates that are among the highest in the world. It is recognized that drained agriculture in the Delta is unsustainable in the long‐term, and to help reverse subsidence and capture carbon (C) there is an interest in restoring drained agricultural land‐use types to flooded conditions. However, flooding may increase methane (CH₄) emissions. We conducted a full year of simultaneous eddy covariance measurements at two conventional drained agricultural peatlands (a pasture and a corn field) and three flooded land‐use types (a rice paddy and two restored wetlands) to assess the impact of drained to flooded land‐use change on CO₂and CH₄fluxes in the Delta. We found that the drained sites were net C and greenhouse gas (GHG) sources, releasing up to 341 g C m⁻² yr⁻¹as CO₂and 11.4 g C m⁻² yr⁻¹as CH₄. Conversely, the restored wetlands were net sinks of atmospheric CO₂, sequestering up to 397 g C m⁻² yr⁻¹. However, they were large sources of CH₄, with emissions ranging from 39 to 53 g C m⁻² yr⁻¹. In terms of the full GHG budget, the restored wetlands could be either GHG sources or sinks. Although the rice paddy was a small atmospheric CO₂sink, when considering harvest and CH₄emissions, it acted as both a C and GHG source. Annual photosynthesis was similar between sites, but flooding at the restored sites inhibited ecosystem respiration, making them net CO₂sinks. This study suggests that converting drained agricultural peat soils to flooded land‐use types can help reduce or reverse soil subsidence and reduce GHG emissions.
In many developing countries property rights over rural land are maintained through continuous personal use instead of by land titles. We show that removing the link between land use and land rights ...through the issuance of ownership certificates can result in large-scale adjustments to labor and land allocations. Using the rollout of the Mexican land certification program from 1993 to 2006, we find that households obtaining certificates were subsequently 28 percent more likely to have a migrant member. We also show that even though land certification induced migration, it had little effect on cultivated area due to consolidation of farm units.
•Multifunctional land use directly affects regional sustainability.•A classification framework for sustainable land use was established.•An evaluation index system for identifying and quantifying ...land use functions at grid scale was established.•Tradeoff/synergy analysis was employed to explore the interrelations among land use functions.•Policy implications about land use zoning and management were proposed.
Land use function (LUF) has sparked widely attention of researchers and policymakers who are focusing on sustainable development. Identifying the interrelations among multiple LUFs is of great significance for land use sustainability. A conceptual classification framework was proposed and a set of spatialization models were employed to assess, identify, quantify and visualize LUFs in spatial grid context. Upon the works aforementioned, Mechanical Equilibrium Model in physics was referenced to explore the tradeoffs/synergies among LUFs. The findings suggest that the indices of economic, social and ecological functions ranged from 0.00 to 0.99, 0.00 to 0.98 and 0.00 to 0.95, respectively and they displayed obvious heterogeneity in spatial distribution. Strong tradeoffs between socio-economic function and ecological function and synergies between economic function and social function mainly occurred in economic prosperous regions, especially in urban agglomerations, whereas areas with high ecological function and low socio-economic function are spatially agglomerated in the west part of China and the periphery of urbanized regions and farming areas. Essentially, spatial incompatibilities of land use caused by different demands are the roots of the land use conflicts. Hence, optimizing management options from the perspective of multifunctional land use based on tradeoff/synergy analysis, which can demonstrate the functional complementation and conflicts, can provide a reference for land use zoning and sustainable land management.
Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. In ...this paper, for the first time, a highly novel Joint Deep Learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neural network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representations. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration. The proposed JDL method provides a general framework within which the pixel-based MLP and the patch-based CNN provide mutually complementary information to each other, such that both are refined in the classification process through iteration. Given the well-known complexities associated with the classification of very fine spatial resolution (VFSR) imagery, the effectiveness of the proposed JDL was tested on aerial photography of two large urban and suburban areas in Great Britain (Southampton and Manchester). The JDL consistently demonstrated greatly increased accuracies with increasing iteration, not only for the LU classification, but for both the LC and LU classifications, achieving by far the greatest accuracies for each at around 10 iterations. The average overall classification accuracies were 90.18% for LC and 87.92% for LU for the two study sites, far higher than the initial accuracies and consistently outperforming benchmark comparators (three each for LC and LU classification). This research, thus, represents the first attempt to unify the remote sensing classification of LC (state; what is there?) and LU (function; what is going on there?), where previously each had been considered separately only. It, thus, has the potential to transform the way that LC and LU classification is undertaken in future. Moreover, it paves the way to address effectively the complex tasks of classifying LC and LU from VFSR remotely sensed imagery via joint reinforcement, and in an automatic manner.
•Joint Deep Learning (JDL) was first proposed for land cover and land use classification.•JDL incorporated patch-based CNN and pixel-based MLP with joint reinforcement and mutual complementarity.•The joint distributions between LC and LU were formulated into a Markov process through iterative updating.•Increased accuracies were achieved for both LC and LU in an automatic fashion with iteration.•The JDL framework is readily generalisable to hierarchical representations at multiple levels and scales.
Analyses were carried out on financial compensation to avoid loss of tropical forests and related carbon (C) emissions when marginal financial yield declined for land-use options with extended areas, ...and when a risk-averting perspective (modeled according to financial theory around the capital asset pricing model) is assumed. The approach in this study was to consider natural forest, forest plantation, pasture, and cropland simultaneously to investigate how an optimized land-use distribution may reduce the amount of compensation necessary to avoid C emissions from forest loss.
The financial compensations derived were as high as US$ 176 per hectare per year when comparing natural forests only with the most profitable alternative (croplands). However, compensation decreased to US$ 124 for risk-neutral decision-makers, who would strive for optimized land-use allocation, and to only US$ 47 per hectare per year for risk-avoiders, who would look to maximize the reward-to-variability ratio. Sensitivity analyses indicated that the compensation under risk-aversion increased much less than under risk-ignoring when increased productivity of agricultural land-use or growing demand for agricultural products was simulated. It was concluded that considering appropriate diversification strategies and the well documented human behavior to avoid risks is an important step in developing cost-effective compensation policies.
► Necessary compensation to avoid the loss of natural forests is probably smaller than expected. ► The introduction of “Optimized Land-Use Diversification” produced declining compensation. ► Compensation reduced from US$ 176 to US$ 124/ha per year under ignorance of risks. ► Modeling risk-avoidance further lowered compensation to only US$ 47/ha per year.
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