Linking in situ LAI and Fine Resolution Remote Sensing Data to Map Reference LAI over Cropland and Grassland Using Geostatistical Regression Method

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bia

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

Analysis of Data of Different Spatial Support: A Multivariate Process Approach

Inherent to a spatial variable is the unit of support at which it is measured. In many studies, variables are observed at different support. For example, disease rates might be measured at an aggregated level while temperature is usually measured at specific points. It is still an interesting problem to study the relationship of variables having different support. However, it may be a different problem to statistically model the relationship of variables of different support, particularly when the supports do not have a hierarchical structure. Currently, cokriging, the use of one or more spatial variables to predict another variable, is applied to variables of the same support. In this work, I extend cokriging for use with variables of different support by constructing a nonparametric cross-covariance matrix. This method is flexible as it applies to any marginal spatial model and is suited to large datasets because it uses latent variables which can assist with dimension reduction. The proposed nonparametric method is demonstrated with two correlated variables which are measured at different spatial units. In addition, the method is implemented using two algorithms; one which yields an optimized matrix (Wang, 2011) and the other which produces an approximately optimized matrix but is computationally more efficient (Hu 2013). The results show that the method is appropriate for predicting data of different support and that it outperforms some competing methods with respect to predictive performance. Furthermore, as expected, the approximately optimized matrix does not perform as well as the alternative algorithm, but it performs better than the comparative methods

Agronomic management response in maize (Zea mays L.) production across three agroecological zones of Kenya

Maize (Zea mays L.) productivity in Kenya has witnessed a decline attributed to the effects of climate change and biophysical constraints. The assessment of agronomic practices across agroecological zones (AEZs) is limited by inadequate data quality, hindering a precise evaluation of maize yield on a large scale. In this study, we employed the DSSAT-CERES-Maize crop model (where CERES is Crop Environment Resource Synthesis and DSSAT is Decision Support System for Agrotechnology Transfer) to investigate the impacts of different agronomic practices on maize yield across different AEZs in two counties of Kenya. The model was calibrated and evaluated with observed grain yield, biomass, leaf area index, phenology, and soil water content from 2-year experiments. Remote sensing (RS) images derived from the Sentinel-2 satellite were integrated to delineate maize areas, and the resulting information was merged with DSSAT-CERES-Maize yield simulations. This facilitated a comprehensive quantification of various agronomic measures at pixel scales. Evaluation of agronomic measures revealed that sowing dates and cultivar types significantly influenced maize yield across the AEZs. Notably, AEZ II and AEZ III exhibited elevated yields when implementing combined practices of early sowing and cultivar H614. The impacts of optimal management practices varied across the AEZs, resulting in yield increases of 81, 115, and 202 kg ha-1 in AEZ I, AEZ II, and AEZ III, respectively. This study underscores the potential of the CERES-Maize model and high-resolution RS data in estimating production at larger scales. Furthermore, this integrated approach holds promise for supporting agricultural decision-making and designing optimal strategies to enhance productivity while accounting for site-specific conditions

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Multiscale Soil Carbon Distribution in Two Sub-Arctic Landscapes

In recent years, concern has grown over the consequences of global warming. The arctic region is thought to be particularly vulnerable to increasing temperatures, and warming is occurring here substantially more rapidly than at lower latitudes. Consequently, assessments of the state of the Arctic are a focus of international efforts. For the terrestrial Arctic, large datasets are generated by remote sensing of above-ground variables, with an emphasis on vegetation properties, and, by association, carbon fluxes. However, the terrestrial component of the carbon (C) cycle remains poorly quantified and the below-ground distribution and stocks of soil C can not be quantified directly by remote sensing. Large areas of the Arctic are also difficult to access, limiting field surveys. The scientific community does know, however, that this region stores a massive proportion (although poorly quantified, soil C stocks for tundra soils vary from 96 to 192 Gt C) of the global reservoir of soil carbon, much of it in permafrost (900 Gt C), and these stocks may be very vulnerable to increased rates of decomposition due to rising temperatures. The consequences of this could be increasing source strength of the radiatively forcing gases carbon dioxide (CO2) and methane (CH4). The principal objective of this project is to provide a critical evaluation of methods used to link soil C stocks and fluxes at the usual scales spanned by the field surveys (centimetre to kilometre) and remote sensing surveys (kilometre to hundreds of kilometres). The soil C distribution of two sub-arctic sites in contrasting climatic, landscape/geomorphologic and vegetation settings has been described and analysed. The transition between birch forest and tundra heath in the Abisko (Swedish Lapland) field site, and the transition between mire and birch forest in the Kevo (Finnish Lapland) field site span several vegetation categories and landscape contexts. The natural variability of below-ground C stocks (excluding coarse roots > 2 mm diameter), at scales from the centimetre to the kilometre scale, is high: 0.01 to 18.8 kg C m-2 for the 0 - 4 cm depth in a 2.5 km2 area of Abisko. The depths of the soil profiles and the soil C stocks are not directly linked to either vegetation categories or Leaf Area Index (LAI), thus vegetation properties are not a straightforward proxy for soil C distribution. When mapping soil or vegetation categories over large areas, it is usually necessary to aggregate several vegetation or soil categories to simplify the output (both for mapping and for modelling). Using this approach, an average value of 2.3 kg C m-2 was derived both for soils beneath treeless areas and forest understorey. This aggregated value is potentially misleading, however, because there is significant skew resulting from the inclusion of exposed ridges (with very low soil C stocks) in the ‘treeless’ category. Furthermore, if birch trees colonise tundra heath and other ‘open’ plant communities in the coming decades, there will likely be substantial shifts in soil C stocks. This will be both due to direct climate effects on decomposition, but also due to changes in above- and below-ground C inputs (both in quantity and quality) and possibly changes in so-called root ‘priming’ effects on the decomposition of existing organic matter. A model of soil respiration using parameters from field surveys shows that soils of the birch forest are more sensitive to increases in mean annual temperature than soils under tundra heath. The heterogeneity of soil properties, moisture and temperature regimes and vegetation cover in ecotone areas means that responses to climate change will differ across these landscapes. Any exercise in upscaling results from field surveys has to indicate the heterogeneity of vegetation and soil categories to guide soil sampling and modelling of C cycle processes in the Arctic

Overland flow resistance & flood generation in semi-arid environments: explaining the restrained draining of the rain in Spain

Resistance equations developed for pipe flows and open channel flows cannot be applied to model overland flows uncritically. The formulation of these equations employs several assumptions that are specific to the conditions in which they were developed and cannot be universally applied. The hydraulic behaviour of overland flow is distinct from that of pipe and channel flows and can be characterised by a high degree of variability both over space and over time as roughness elements are progressively inundated with increasing depth. A novel methodology of measuring overland flows in the field at a high- resolution permits examination of the interaction between flow variables and surface roughness. Reconstructing the water surface from elevation data and flow extent provides an estimation of the distribution of flow depths and offers a complementary perspective to more conventional approaches. Overland flows are observed to be highly variable both across and between hillslopes. The distribution of flow depths can be modelled using a two-parameter gamma distribution; both parameters show distinct variations with distance downslope and represent the progressive inundation of roughness elements with increasing depth. The flow interacts with soil surface form where it is capable of eroding its bed and the observed slope- independence of rill velocity can be explained by a feedback between flow state (as characterised by the Froude number) and surface roughness. While the existence of this interaction is affected by soil-type, the soil is observed to have little influence on the relationship between surface roughness and overland flow. Resistance is found to be spatially variable; some of this variability could be explained by the classification of areas of similar microtopogiaphy as identified in the field. This classification can be approximated by a thresholded index-based classification and provides a tool for up-scaling to the hillslope scale. Relating roughness to resistance is not straightforward. Complex natural soil surfaces vary in innumerable ways. Traditional roughness measures fall short of providing an adequate description of the complex soil surfaces observed in the field. A variety of alternative measures are developed, each of which captures a different attribute of surface form. These measures are tested to examine their influence on overland flow resistance and a suite of roughness-resistance models is developed which includes the effect of hillslope position to different degrees. Modelled flow resistance can be separated into a constant term and a depth-dependent term and can be easily incorporated into models of hillslope hydrology. This resistance is observed to decline where a hydrological connection, once established, is then maintained. Examination of the concept of hydrological connectivity in a semi-arid context suggests that the interaction between runoff generation and transfer determines not just flood peaks but also total flow amount. It is suggested that flow resistance and hence runoff transfer should be afforded the same detailed consideration as infiltration parameters, i.e. a spatially distributed and variable value (as a function of depth) that can be organised into discrete units akin to those developed for runoff generation. The parameterisation of both infiltration and resistance in this way provides a crucial interaction through the redistribution of soil moisture and runoff over hillslope surfaces. Through this mechanism, the observed complex and nonlinear runoff response to storm events may be explained as these attributes interact with rainfall characteristics and flow network development. Further understanding of this interaction could have practical implications for catchment management and affect the prioritisation of land management decisions

Vegetation Change and Water, Sediment and Carbon Dynamics in Semi-Arid Environments

This study develops understanding of vegetation change and water, sediment and carbon dynamics in semi-arid environments. Objectives were addressed using an integrated ecohydrological and biogeochemical approach. Fieldwork, over two contrasting grass-woody transitions at the Sevilleta National Wildlife Refuge, New Mexico, USA; quantified vegetation structure, soil structure and the spatial distribution of soil carbon resources. Over both transitions; woody sites showed a lower percentage vegetation cover and a greater heterogeneity in vegetation pattern, soil properties and soil carbon. Soil organic carbon differed in both quantity and source across the sites; with levels higher under vegetation, particularly at the woody sites. Biogeochemical analysis revealed soil organic carbon to be predominantly sourced from grass at the grassland sites. In contrast, at the woody sites soil organic carbon under vegetation patches was predominantly sourced from woody vegetation, whilst inter-patch areas exhibited a strong grass signature. Investigation of function focussed on the hydrological response to intense rainfall events. Rainfall-runoff monitoring showed woody sites to exhibit greater; runoff coefficients, event discharge, eroded sediment and event carbon yields. In contrast to grass sites, biogeochemical analysis showed the loss of organic carbon from woody sites to exhibit a mixed source signal, reflecting the loss of carbon originating from both patch and interpatch areas. To examine the linkages between vegetation structure and hydrological function, a flow length metric was developed to quantify hydrological connectivity; with woody sites shown to have longer mean flow pathways. Furthermore, in addition to rainfall event characteristics, flow pathway lengths were shown to be a significant variable for explaining the variance within fluxes of water, sediment and carbon. Results demonstrating increased event fluxes of sediment and carbon from woody sites have important implications for the quality of semi-arid landscapes and other degrading ecosystems globally. It is thus necessary to translate the understanding of carbon dynamics developed within this study to the landscape scale, so changing fluvial carbon fluxes can be incorporated into carbon budgets, research frameworks and land management strategies at policy-relevant scales.University of ExeterRothamsted Research at North Wyk

Linking Scales: Investigations on the interaction between environmental conditions, humans, and a fossorial rodent species across space and time

Interactions between species and their environment build the backbone of biodiversity and ecosystem stability. Species-environment interactions shape species' spatial distributions and population dynamics, with present and past environmental conditions, as well as evolutionary mechanisms such as dispersal ability playing pivotal roles. Understanding how species are shaped by the environment and human activities across spatial scales and time, is a precondition to predict and reverse the ongoing decline of global biodiversity. Fossorial species usually exhibit a tight interaction with the environment. Their burrowing activity shapes ecosystems processes but at the same time, limits their ability to disperse. This may be exacerbated in mountain ecosystems where the geomorphology of the landscape further restricts dispersal and distribution ranges. Additionally, human induced habitat degradation and environmental change in mountain ecosystems may affect the persistence of many species. In order to disentangle complex species-environment interactions under human activities, a combination of methods is required covering varying spatial scales, including how the environment over time has shaped the species we observe today. This thesis explores the case of the giant root-rat (Tachyoryctes macrocephalus), an endemic, fossorial rodent species with a limited range in the afro-alpine Bale Mountains in southern Ethiopia. By combining spatially explicit ecological and genetic analyses, I assessed the intricate interplay between the environmental conditions and human activities across time that have shaped the species’ local and range-wide distribution at the landscape scale, as well as its population genetic structure, diversity, and demography. Through a combination of methods, my results revealed a scale dependency of species-environment interactions, with historical and evolutionary factors shaping interactions differently on local and landscape scales. Ecological field studies revealed (Chapter II) a tight interaction between the giant root-rat, the local environmental conditions and human land use. Giant root-rat activity reduced vegetation cover, while the local species' activity increased with decreasing vegetation cover and elevated livestock grazing, indicating the species' preference for open habitats. However, extending our inquiry to the landscape scale using satellite-based remote sensing and vegetation data (Chapter III), we found that texture metrics describing topographic differences across the landscape determined the species' range-wide distribution. Hence, environmental conditions shaping the local activities, differed from those influencing the species' overall distribution. Population genetic studies of genetic subdivision and diversity (Chapter IV), further demonstrated the effect of topography on the species distribution and dispersal ability. I found a pronounced subdivision of the species into a northern and southern population, with no sign of gene flow between them. Landscape genetic analyses revealed that topographic barriers were the driving force on the landscape scale, hindering dispersal between north and south. Environmental conditions played a subordinate role, at least for local species’ genetic substructure and dispersal within populations. With the analyses of giant root-rat subfossil remains from the Late Pleistocene era (Chapter V), I expanded the examination of the species’ interaction with the environment and humans on a temporal scale. Notably, radiocarbon dating of these subfossils provided insights into human presence in the Bale Mountains, indicating nearly continuous human habitation in the region from 47,000 to 31,000 years ago. Ancient DNA studies revealed that both environmental changes and human activities played pivotal roles in driving phylogenetic lineage divergence and shaping the demographic history of the species over thousands of years. The last glaciation of the Bale Mountains and human hunting practices during that period likely led to a population decline in the northern and southern regions, respectively. Additionally, I observed an ongoing population decline and reduced nucleotide diversity in the northern population since the end of the last glacial period, possibly resulting from habitat reduction caused by environmental changes. The presented studies in this thesis collectively demonstrate the direct effect of local environmental conditions and human activity on the occurrence of the species. The joint consideration of my research findings emphasized that the giant root-rat may have a limited ability to shift its range under changing environments due to its limited dispersal ability, which hinders the traversal of pronounced topographic barriers in the landscape. Understanding these complex relationships is essential for effective conservation management for preserving mountain biodiversity and ecosystem functionality, of the afro-alpine ecosystem and other similar environments where species and their habitats are deeply interconnected