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The potential for climate change mitigation by bioenergy crops and terrestrial carbon sinks has been the object of intensive research in the past decade. There has been much debate about whether energy crops used to offset fossil fuel use, or carbon sequestration in forests, would provide the best climate mitigation benefit. Most current food cropland is unlikely to be used for bioenergy, but in many regions of the world, a proportion of cropland is being abandoned, particularly marginal croplands, and some of this land is now being used for bioenergy. In this study, we assess the consequences of land-use change on cropland. We first identify areas where cropland is so productive that it may never be converted and assess the potential of the remaining cropland to mitigate climate change by identifying which alternative land use provides the best climate benefit: C 4 grass bioenergy crops, coppiced woody energy crops or allowing forest regrowth to create a carbon sink. We do not present this as a scenario of land-use change -we simply assess the best option in any given global location should a land-use change occur. To do this, we use global biomass potential studies based on food crop productivity, forest inventory data and dynamic global vegetation models to provide, for the first time, a global comparison of the climate change implications of either deploying bioenergy crops or allowing forest regeneration on current crop land, over a period of 20 years starting in the nominal year of 2000 AD. Globally, the extent of cropland on which conversion to energy crops or forest would result in a net carbon loss, and therefore likely always to remain as cropland, was estimated to be about 420.1 Mha, or 35.6% of the total cropland in Africa, 40.3% in Asia and Russia Federation, 30.8% in Europe-25, 48.4% in North America, 13.7% in South America and 58.5% in Oceania. Fast growing C 4 grasses such as Miscanthus and switch-grass cultivars are the bioenergy feedstock with the highest climate mitigation potential. Fast growing C 4 grasses such as Miscanthus and switch-grass cultivars provide the best climate mitigation option on %485 Mha of cropland worldwide with~42% of this land characterized by a terrain slope equal or above 20%. If that land-use change did occur, it would displace %58.1 Pg fossil fuel C equivalent (C eq oil). Woody energy crops such as poplar, willow and Eucalyptus species would be the best option on only 2.4% (%26.3 Mha) of current cropland, and if this land-use change occurred, it would displace %0.9 Pg C eq oil. Allowing cropland to revert to forest would be the best climate mitigation option on %17% of current cropland (%184.5 Mha), and if this land-use change occurred, it would sequester %5.8 Pg C in biomass in the 20-year-old forest and %2.7 Pg C in soil. This study is spatially explicit, so also serves to identify the regional differences in the efficacy of different climate mitigation options, informing policymakers developing regionally or nationally appropriate mitigation actions.

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We propose a method for spatial downscaling of Landsat-8 derived LST maps from 100(30m) resolution down to 2-4m with the use of the Multiple Adaptive Regression Splines (MARS) models coupled with very high resolution auxiliary data derived from hyperspectral aerial imagery and large-scale topographic maps. We applied the method to four Landsat 8 scenes, two collected in summer and two in winter, for three British towns collectively representing a variety of urban form. We used several spectral indices as well as fractional coverage of water and paved surfaces as LST predictors, and applied a novel method for the correction of temporal mismatch between spectral indices derived from aerial and satellite imagery captured at different dates, allowing for the application of the downscaling method for multiple dates without the need for repeating the aerial survey. Our results suggest that the method performed well for the summer dates, achieving RMSE of 1.40-1.83K prior to and 0.76-1.21K after correction for residuals. We conclude that the MARS models, by addressing the non-linear relationship of LST at coarse and fine spatial resolutions, can be successfully applied to produce high resolution LST maps suitable for studies of urban thermal environment at local scales.

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Context Relationships between land surface temperature (LST) and spatial configuration of urban form described by landscape metrics so far have been investigated with coarse resolution LST imagery within artificially superimposed land divisions. Citywide micro-scale observations are needed to better inform urban design and help mitigate urban heat island effects in warming climates. Objectives The primary objective was to sub-divide an existing high-resolution land cover (LC) map into groups of patches with distinct spatial and thermal properties suitable for urban LST studies relevant to micro-scales. The secondary objective was to provide insights into the optimal analytical unit size to calculate class-level landscape metrics strongly correlated with LST at 2 m spatial resolution. Methods A two-tiered unsupervised k-means clustering analysis was deployed to derive spatially distinct groups of patches of each major LC class followed by further subdivisions into hottest, coldest and intermediary sub-classes, making use of high resolution class-level landscape metrics strongly correlated with LST. Results Aggregation class-level landscape metrics were consistently correlated with LST for green and grey LC classes and the optimal search window size for their calculations was 100 m for LST at 2 m resolution. ANOVA indicated that all Tier 1 and most of Tier 2 subdivisions were thermally and spatially different. Conclusions The two-tiered k-means clustering approach was successful at depicting subdivisions of major LC classes with distinct spatial configuration and thermal properties, especially at a broader Tier 1 level. Further research into spatial configuration of LC patches with similar spatial but different thermal properties is required.

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