| 1. |
- Diriye, Abdishakur W., et al.
(författare)
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Public preference for sustainable land use policies – Empirical results from multinomial logit model analysis
- 2022
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Ingår i: Land Use Policy. - : Elsevier BV. - 0264-8377. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- Public preferences for sustainable land use policy instruments and the motivations behind such preferences are important to make appropriate policies. Based on survey data (n = 309) from northeastern Somalia, we examined preferences for a set of land use policy instruments relative to no policy (i.e. the current status quo) and how cultural worldviews predict such preferences. We used a multinomial logit model to analyze the comparative evaluation of choices due to its interpretability and robustness to violations of normality. Overall, the results show that the respondents are likely to consent to all types of land use policy instruments relative to no policy and are more inclined to market-based and informational policy instruments. Specifically, preferences for regulatory policy instruments are positively associated with hierarchy and egalitarian worldviews and are negatively associated with fatalism and individualistic worldviews with only hierarchy and fatalism are significant. The market-based policy instrument is desirable to all cultural worldviews except fatalism, but only egalitarian and individual worldviews are significant. Preferences for informational policy instruments are positively associated with all cultural worldviews but only egalitarian worldviews showed a significant effect. Although there are some contradictions, these results are broadly consistent with the proposition of the cultural theory of risk. This study highlights that preferences for land use policies are heterogeneous with cultural worldviews mainly explaining the sources of this heterogeneity. It is evident that the respondents were willing to consent to land use policies relative to the status quo of no policy and indicates the need for concerted effort to reduce land degradation and deforestation in the country. We, therefore, recommend that policymakers incorporate the different ways that humans perceive and interpret social-environmental relations into policy decisions to achieve sustainable policy outcomes.
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| 2. |
- Abdi, Abdulhakim M., et al.
(författare)
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Satellite Remote Sensing of Savannas: Current Status and Emerging Opportunities
- 2022
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Ingår i: Journal of Remote Sensing. - : American Association for the Advancement of Science (AAAS). - 2694-1589. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.
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| 3. |
- Fang, Zhongxiang, et al.
(författare)
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Globally Increasing Atmospheric Aridity Over the 21st Century
- 2022
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Ingår i: Earth's Future. - 2328-4277. ; 10:10
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Tidskriftsartikel (refereegranskat)abstract
- Vapor pressure deficit (VPD) is of great importance to control the land-atmosphere exchange of water and CO2. Here we use in situ observations to assess the performance of monthly VPD calculated from state-of-the-art data sets including CRU, ERA5, and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2). We investigate trends in VPD at global scale and for different climatic zones for 1981–2020 and future trends (2021–2100) from Coupled Model Inter-comparison Project phase 6 (CMIP6) outputs. The results show that monthly VPD estimated from CRU, ERA5, and MERRA2 correlated well against in situ estimates from 15,531 World Meteorological Organization stations, with R2 ranging between 0.92 and 0.96. Moreover, robust correlations were also found across in situ stations and when analyzing different months separately. During 1981–2020, VPD increased in all climatic zones, with the strongest increase in the arid zone, followed by tropical, temperate, cold and polar zones. CMIP6 simulations show a continuously increasing trend in VPD (0.028 hPa year−1), with the largest increase in the arid zone (0.063 hPa year−1). The magnitudes of trends are found to increase following the magnitude of CO2 increases in the future emission scenarios. We highlight that atmospheric aridification will continue under global warming, which may pose an increasing threat to terrestrial ecosystems and particularly dryland agricultural systems.
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| 4. |
- Hosseiny, Benyamin, et al.
(författare)
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Urban land use and land cover classification with interpretable machine learning – A case study using Sentinel-2 and auxiliary data
- 2022
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Ingår i: Remote Sensing Applications: Society and Environment. - : Elsevier BV. - 2352-9385. ; 28
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Tidskriftsartikel (refereegranskat)abstract
- The European commission launch of the twin Sentinel-2 satellites provides new opportunities for land use and land cover (LULC) classification because of the readily availability of their data and their enhanced spatial, temporal and spectral resolutions. The rapid development of machine learning over the past decade led to data-driven models being at the forefront of high accuracy predictions of the physical world. However, the contribution of the driving variables behind these predictions cannot be explained beyond generalized metrics of overall performance. Here, we compared the performance of three shallow learners (support vector machines, random forest, and extreme gradient boosting) as well as two deep learners (a convolutional neural network and a residual network with 50 layers) in and around the city of Malmö in southern Sweden. Our complete analysis suite involved 141 input features, 85 scenarios, and 8 LULC classes. We explored the interpretability of the five learners using Shapley additive explanations to better understand feature importance at the level of individual LULC classes. The purpose of class-level feature importance was to identify the most parsimonious combination of features that could reasonably map a particular class and enhance overall map accuracy. We showed that not only do overall accuracies increase from shallow (mean = 84.64%) to deep learners (mean = 92.63%) but that the number of explanatory variables required to obtain maximum accuracy decreases along the same gradient. Furthermore, we demonstrated that class-level importance metrics can be successfully identified using Shapley additive explanations in both shallow and deep learners, which allows for a more detailed understanding of variable importance. We show that for certain LULC classes there is a convergence of variable importance across all the algorithms, which helps explain model predictions and aid the selection of more parsimonious models. The use of class-level feature importance metrics is still new in LULC classification, and this study provides important insight into the potential of more nuanced importance metrics.
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| 5. |
- Kazemzadeh, Majid, et al.
(författare)
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Four Decades of Air Temperature Data over Iran Reveal Linear and Nonlinear Warming
- 2022
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 36:3, s. 462-477
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Tidskriftsartikel (refereegranskat)abstract
- Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there is considerable research on air temperature change across the globe, most of it has been on linear trends and time series analysis of nonlinear trends has not received enough attention. Here, we analyze spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax) and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-year time period (1978–2017). We applied a polynomial fitting scheme (Polytrend) to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The highest magnitude of increasing trends were observed in the annual Tmin (0.47 °C per decade) and the lowest magnitude was for the annual Tmax (0.4°C per decade). Across the country, increasing trends (x̄ = 37.2%) had higher spatial coverage than the decreasing trends (x̄ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) were mainly observed in humid climate zone while warming trends in Tmax were in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend was predominant in all Tmean (56.7%), Tmax (67.8%) and Tmin (71.2%) and for both monthly and annual datasets. Further, the linear trends had the highest warming rate in annual Tmin (0.83°C per decade) and Tmean (0.46°C per decade) whereas the nonlinear trends had the highest warming rate in annual Tmax (0.52°C per decade). The linear trend type was predominant in humid climate zones whereas the nonlinear trends (quadratic and cubic) were mainly observed in the arid climate zones.
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