On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization

High-resolution soil maps are urgently needed by land managers and researchers for a variety of applications. Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. In this study, a legacy soil dataset was used to train a machine learning algorithm in order to predict the particle size distribution within the catchment of the Bode River in Saxony-Anhalt (Germany). The random forest ensemble learning method was used to predict soil texture based on environmental covariates originating from a digital elevation model, land cover data and geologic maps. We studied the usefulness of clustering applications in addressing various aspects of the DSM procedure. To improve areal representativity of the legacy soil data in terms of spatial variability, the environmental covariates were used to cluster the landscape of the study area into spatial units for stratified random sampling. Different sampling strategies were used to create balanced training data and were evaluated on their ability to improve model performance. Clustering applications were also involved in feature selection and stratified cross-validation. Under the best-performing sampling strategy, the resulting models achieved an R2 of 0.29 to 0.50 in topsoils and 0.16-0.32 in deeper soil layers. Overall, clustering applications appear to be a versatile tool to be employed at various steps of the DSM procedure. Beyond their successful application, further application fields in DSM were identified. One of them is to find adequate means to include expert knowledge. © Copyright

Predicting Distributions of Estuarine Associated Fish and Invertebrates in Southeast Alaska

Thesis (Ph.D.) University of Alaska Fairbanks, 2013Estuaries in Southeast Alaska provide habitat for juveniles and adults of several commercial fish and invertebrate species; however, because of the area's size and challenging environment, very little is known about the spatial structure and distribution of estuarine species in relation to the biotic and abiotic environment. This study uses advanced machine learning algorithms (random forest and multivariate random forest) and landscape and seascape-scale environmental variables to develop predictive models of species occurrence and community composition within Southeast Alaskanestuaries. Species data were obtained from trawl and seine sampling in 49 estuaries throughout the study area. Environmental data were compiled and extracted from existing spatial datasets. Individual models for species occurrence were validated using independent data from seine surveys in 88 estuaries. Prediction accuracy for individual species models ranged from 94% to 63%, with 76% of the fish species models and 72% of the invertebrate models having a predictive accuracy of 70% or better. The models elucidated complex species-habitat relationships that can be used to identify habitat protection priorities and to guide future research. The multivariate models demonstrated that community composition was strongly related to regional patterns of precipitation and tidal energy, as well as to local abundance of intertidal habitat and vegetation. The models provide insight into how changes in species abundance are influenced by both environmental variation and the co-occurrence of other species. Taxonomic diversity in the region was high (74%) and functional diversity was relatively low (23%). Functional diversity was not linearly correlated to species richness, indicating that the number of species in the estuary was not a good predictor of functional diversity or redundancy. Functional redundancy differed across estuary clusters, suggesting that some estuaries have a greater potential for loss of functional diversity with species removal than others

Mine landform design using natural analogues

Current practice for landscape reconstruction following opencast mining relies on topographic reconstruction, adaptive land management and botanical characterisation. Environmental processes may be altered where reconstructed landforms have significant relief. Consequently, environmental outcomes in cases where there is large scale land forming are unpredictable. Moreover, landscape restoration lacks an integrated methodology, and while many mine closures have detailed ecosystem and biodiversity objectives based on natural analogue areas there has been no reliable way to design these objectives into mine landforms. The methods used in landscape restorations to describe reference conditions are based on generalised environmental factors using regional information and incorporating conceptual models. Such models lack the precision and accuracy required to understand and restore hillslope environmental pattern at mine sites. However, methodological integration and statistical inference models underpinning the spatial inference methods in conservation and landscape ecology, and pedology may be applied to solve this problem. These inference models utilise digital terrain models as the core environmental data incorporating ecological theory to predict biodiversity and species distribution. Also, numerical mass balance models such as water and solute balance, which have been applied to understand environmental processes in landscapes, can be used to assess mine landform design. The objective of the work reported here was to investigate environmental variation, with sufficient accuracy and precision, in natural landscapes to design mature mine landforms and to demonstrate the capacity to predict ecological outcomes. This would extend current best practice - designing mine landforms with predictable hydrological and geotechnical outcomes needed to protect off-site environmental conditions – to the on-site environment after closure. The specific aims of this thesis were to: (i) evaluate the predictability of ecosystems based on regional ecological mapping: (ii) develop and evaluate quantitative, site specific environmental mapping and natural analogue selection methodology; (iii) evaluate a trial final landform cover (reconstructed soil) using water balance, water chemistry monitoring; (iv) design and evaluate a conceptual mine landform through the assessment of environmental processes in natural analogue areas; and (v) make valid predictions of revegetation outcomes on the conceptual landform. In meeting these aims, links between ecological theory, landscape analysis and the current practice in mine landform design were identified. The first phase of the thesis involved environmental investigations and surveys of extensive savanna environments on the Tiwi Islands (7320 km-2) and similar environments in the vicinity of Ranger uranium mine (150 km-2) in northern Australia. This first phase, reported in Chapter 3, investigated the reliability of conceptual landscape models used in regional ecological mapping in predicting ecological patterns in terms of vegetation and soil. The Tiwi Islands was selected because of the relatively uniform parent material and its simplified climate. This allowed the study of physiographic control of soil and vegetation patterns. The results identified correlations between vegetation pattern and landform that were confounded by a subjective and complex land unit model of ecosystems. This investigation enabled the development methodological approach to analogue selection and ecological modelling at Ranger uranium mine – a site that will require a restoration approach so as to meet environmental closure objectives. The second phase is the methodological development – involving an initial reconnaissance, is presented in Chapter 4. This phase was aimed at selecting natural analogue areas for mined land restoration. Environmental pattern recognition involving classification, ordination and network analysis was implemented based on methods of conservation ecology. This led to quantitative landscape model to identify natural analogue areas and design ecosystem surveys. This quantitative landscape model incorporated a grid survey of vegetation and soil variation into a nearby analogue landform that matched the area of mine disturbance. This analogue landform encapsulates the entire ecosystem types observed on rocky substrates in the broader reconnaissance survey. The natural analogue selection incorporated a combination of digital terrain analysis and k-means clustering of primary and secondary terrain variables to classify habitat variation on hillslopes. Landscapes with similar extent to the mine landscape were identified from numerical similarity measures (Bray-Curtis) of fine grained habitat variation and summarised using a dendrogram. The range in hillslope ecosystem types were described from stratified environmental surveys of vegetation and soils along environmental gradients in selected analogue landforms. The results show that the mapped environmental factors in close correlation with water and sediment distribution were strongly associated with observed vegetation patterns in analogue areas at Ranger uranium mine. Environmental grain size and landform extent concepts were therefore introduced using landscape ecology theory to integrate different scales of environmental variation in a way that provides direct context with the area impacted by mining. Fine-grained environmental terrain attributes that describe runoff, erosion and sediment deposition were derived from a digital elevation model and classified using non-hierarchical multivariate methods to create a habitat class map. Patch analysis was used to aggregate this fine-grained environmental pattern into a grid that matched the scale of the mine landform. The objective was to identify landforms that were similar in extent to the reconstructed mine landscape. Ecosystem support depends on soil as well as geomorphic factors. An investigation into critical environmental processes, water balance and solute balance, on a waste rock landform at Ranger uranium mine is presented in Chapter 5 to characterise waste rock soils and investigate cover design options that affect environmental support. This involved monitoring of water balance of a reconstructed soil cover on a waste rock landform for four years and the solute loads for two years. A one dimensional water balance model was parameterised and run based on 21 years of rainfall records so as to assess the long-term effects of varying cover thickness and surface compactness on cover performance. The results show that the quality of runoff and seepage water did not improve substantially after two years as large amount of dissolved metal loads persisted. Also, tree roots interacted with the subsoil drainage-limiting layer at one metre below the land surface in just over two years - and thus altering the hydraulic properties of the layer. Further, the results of water balance simulations indicate that increasing the depth to, and thickness of, the drainage-limiting layer would reduce drainage flux. Increasing layer thickness could also limit tree root penetration. It was also found that surface compaction was the most effective means of limiting deep drainage, which contained high concentrations of dissolved metals. However, surface compaction creates an ecological desert. Therefore long-term rehabilitation of the cover will be required to allow water to infiltrate for it to be available for ecosystems. A cover that can store and release sufficient water to support native savanna eucalypt woodland may need to be three to five metres deep, including a drainage limiting layer at depth so as to slow vertical water movement and comprise a well graded mix of hard rock and weathered rock to provide water storage and erosion resistance. The resulting waste rock soils would be similar, morphologically to the gradational, gravelly soils found in natural analogue areas. The study then shifted from mined land back to a selected natural analogue landscape at Ranger mine in Chapter 6. The fine grained variation in terrain attributes is described to support a landform design that allowed for mine plan estimates of waste rock volumes and pit void volumes. A process of developing and evaluating the landform design was put forward, in the case of Ranger, that begins with key stakeholder consultation, followed by an independent scientific validation using published landform evolution and integrated, surface-groundwater water balance modelling. The natural analogue and draft final landforms were compared in terms of terrain attributes, landform evolution and eco-hydrological processes to identify where improvements could be required. The results of the independent design reviews are contained in confidential reports to Ranger mine and in conference proceedings that are referenced in Chapter 6. Independent validation will be a key element of an ecological landform design process and the application of published eco-hydrological and landform evolution models at the Ranger mine case study site are presented as an example of current best practice. Also, detailed assessment was made of environmental variation and soil and geomorphic range in the selected analogue landscape to support the landform design process with the mining department. Ecological modelling of the distributions of framework species in the reconstructed landscape is proposed as an additional assessment tool in this thesis to validate an ecological landform design methodology. To this end, a detailed environmental survey is presented in Chapter 6 of the soils and vegetation in a selected natural analogue area of Ranger mine to identify common and abundant plant species and their distribution in a similar landscape context to the mined land. This work supported ecological modelling of species distributions in reconstructed and natural landscapes in the following chapter. The results of species distribution models for reconstructed and natural landscapes at the Ranger mine site are reported in Chapter 7. The aim was to predict the distribution of common and abundant native woodland species across a landscape comprising a sculpted, post mining landform within a natural landscape. Species distribution models were developed from observations of species presence-absence at 102 sites in the grid survey of the natural analogue area that was reported in Chapter 6. Issues related to optimising predictor selection and the range of environmental support were investigated by introducing survey sites from the broad area reconnaissance survey reported in Chapter 4. Added to these are the published species abundance data from an independent regional biodiversity survey of rocky, well drained eucalypt woodlands, used as analogues of mined land. Plant species responses to continuous and discrete measures of environmental variation were then analysed using multivariate detrended correspondence analysis and canonical correspondence analysis to select independent variables and assess the relative merits of abundance versus presence absence observations of species. Then, generalised additive statistical methods were used to predict species distributions from primary and secondary terrain variables across the natural analogue area and a reconstructed post-mining landform. This analysis was completed with an assessment of the effect that survey support has on model formulation and accuracy. The scale of the mine landscape was found to provide important context for the stratified environmental surveys needed to support predictive modelling. Extending the geographic range of survey support did not improve model performance, while survey sites remote from the mine introduced some degree of spatial autocorrelation that could reduce the prediction accuracy of species distributions in the mine landscape. Further work is needed to address uncommon species or species with highly constrained environmental ranges and aspects of landform cover design and land management that affect woodland type and vigour. The combined studies reported in this thesis show that the predictability of mine land restorations is dependent on the landscape models used to characterise the natural analogue areas. It is demonstrated that conceptual ecological models developed for regional land resources survey, commonly used to select natural analogue areas, are subjective, complex and unreliable predictors of vegetation and soil patterns in hillslope environments at particular sites. It was recognised that environmental patterns are subject to terrain and hillslope environmental variation across an extensive areas. The landform model for selecting natural analogues was refined by introducing grain size and ecological extent concepts, used to describe ecological scale in landscape ecology, to address these effects. These refined concepts were adapted to define environmental variation in the context of natural analogue selection for mining restoration, rather than home range habitat conditions for native animals as was their original purpose. It is demonstrated here that the grain size and extent of environmental variation in the natural landscape can be used to select natural analogue landforms, develop ecological design criteria and design field surveys that support the capacity to predict the distributions of common and abundant woodland species in a reconstructed landscape. In conclusion, it is worth noting that an integrated ecological approach to landscape design can be applied to closure planning at mine sites where cultural and ecological objectives are critical to the success of the mine rehabilitation. Furthermore final landform trials could be used to support a restoration approach — providing an understanding of the interactions between critical physical and ecological processes in the soil layers and environmental processes at catchment scales. The accuracy of the inferences made is dependent on the understanding of hydrological processes in natural and constructed landforms. However, the natural analogue approach provides a clear landscape context for these trials. In a world where species extinction resulting from habitat loss is one of the most important global ecological issues, mine rehabilitation offers unique experimental opportunities to develop capability in ecosystem rehabilitation

Informing tropical mammal conservation in human-modified landscapes using remote technologies and hierarchical modelling

The aggressive expansion of anthropogenic activities is placing increasing pressure on biodiversity, particularly in tropical regions. Here, conservation efforts are hindered by poor understanding of species ecology and the failure of policy instruments to account for multiple stressors of land-use change. While protected areas are central to conservation strategies, there is a general consensus that the future of tropical biodiversity will be determined by how well modified landscapes are managed. In this thesis I advance our understanding of biodiversity persistence in modified tropical landscapes to inform emerging incentive-based policy mechanisms and supply-chain initiatives. Capitalising on recent advances in remote-sensing and hierarchical occupancy modelling, I provide a spatial appraisal of biodiversity in a modified landscape in Sabah, Malaysian Borneo. Fieldwork was conducted at the Stability of Altered Forest Ecosystems (SAFE) project, a large-scale landscape modification experiment, comprising a degradation gradient of old growth forest, selectively logged forest, remnant forest patches and oil palm plantations. The assessment focused on camera-trapping of tropical mammals, as they are sensitive to anthropogenic stressors, occupy key trophic positions, and prioritised in conservation. In Chapter 2 I link mammal occupancy data to airborne multispectral remote-sensing information to show how the conservation value of modified landscapes is dictated by the intensity of the underlying land-use. Logged forests retained appreciable levels of mammal diversity, and oil palm areas were largely devoid of forest specialists and threatened taxa. Moreover, many mammal species disproportionately occupied forested areas that retained old growth structural characteristics. The most influential structural measures accounted for vertical and horizontal components in environmental space, which cannot currently be derived from conventional satellite data. Using a novel application of ecological threshold analysis, I demonstrate how multispectral data and multi-scale occupancy models can help identify conservation and restoration areas in degraded forests. In Chapter 3 I assess the potential for carbon-orientated policy mechanisms (High Carbon Stock, HCS, Approach and REDD+) to prioritise high carbon areas with corresponding biodiversity value in highly modified landscapes. The areas of highest carbon value prioritised via HCS supported comparable species diversity to old growth forest. However, the strength, nature and extent of the biodiversity co-benefit was dependent on how carbon was characterised, the spatial resolution of carbon data, and the species considered. In Chapter 4 I further scrutinised HCS protocols to evaluate how well they delineated high priority forest patches that safeguard species most vulnerable to land-use change (i.e. IUCN threatened species). The minimum core area required to define a high priority patch (100 ha) supported only 35% of the mammal community. In fact the core area criterion would need to increase to 3,199 ha in order to sustain intact mammal assemblages, and an order of magnitude higher if hunting pressure was considered. These findings underline the importance of integrating secondary disturbance impacts into spatial conservation planning. Provided landscape interventions are directed to where they will have the greatest impact, they can be financially sustaining and garner local support for conservation. To this end I provide recommendations to guide policy implementation in modified tropical landscapes to support holistic conservation strategies

Water Framework Directive Intercalibration: Central-Baltic Lake Fish fauna ecological assessment methods

The European Water Framework Directive (WFD) requires the national classifications of good ecological status to be harmonised through an intercalibration exercise. In this exercise, significant differences in status classification among Member States are harmonized by comparing and, if necessary, adjusting the good status boundaries of the national assessment methods. Intercalibration is performed for rivers, lakes, coastal and transitional waters, focusing on selected types of water bodies (intercalibration types), anthropogenic pressures and Biological Quality Elements. Intercalibration exercises are carried out in Geographical Intercalibration Groups - larger geographical units including Member States with similar water body types - and followed the procedure described in the WFD Common Implementation Strategy Guidance document on the intercalibration process (European Commission, 2011). The Technical report on the Water Framework Directive intercalibration describes in detail how the intercalibration exercise has been carried out for the water categories and biological quality elements. The Technical report is organized in volumes according to the water category (rivers, lakes, coastal and transitional waters), Biological Quality Element and Geographical Intercalibration group. This volume addresses the intercalibration of the Lake Central-Baltic Fish ecological assessment methods. Part A: This document comprises an overview and detailed descriptions of fish-based lake ecological assessment methods. Part B describes the construction of multiple pressure index in the Central-Baltic region. Part C describes the procedure and results of the boundary harmonisation of national fish-based lake assessment systemsJRC.D.2-Water and Marine Resource

Does Soil Carbon support Climate Resilient Agricultural Systems? Searching for Evidence and Developing New Measurement Tools

2022Increasing soil organic carbon (SOC) is frequently promoted as a “win-win” strategy for agricultural management in the face of a changing climate. This framing is based on the notion that building SOC both reduces yield losses/variability by improving soil water dynamics, and that building SOC can contribute to climate change mitigation by reducing atmospheric carbon. While this framing may be useful, relationships between SOC and such outcomes are often poorly described and not quantitative. That is, it’s unclear how much of an improvement to SOC is needed to reduce yield losses, whether or not that effect translates across soil types and agricultural systems, and how achievable carbon sequestration goals really are. As efforts to increase SOC in agricultural systems develop, there is a need to both better synthesize our current understanding of how it supports resilience in agricultural systems and to better monitor changes in SOC to understand its impacts on climate change adaptation and resilience. My research focuses on two broad topic areas: 1.) exploring the links between SOC, soil water dynamics, and yield outcomes, particularly under drought conditions; and 2.) developing accessible, robust measurement systems and protocols for quantifying SOC stocks at landscape scales (\u3e100 ha) that utilize visible/near-infrared (VNIR) spectrometry

Data-driven model development in environmental geography - Methodological advancements and scientific applications

Die Erfassung räumlich kontinuierlicher Daten und raum-zeitlicher Dynamiken ist ein Forschungsschwerpunkt der Umweltgeographie. Zu diesem Ziel sind Modellierungsmethoden erforderlich, die es ermöglichen, aus limitierten Felddaten raum-zeitliche Aussagen abzuleiten. Die Komplexität von Umweltsystemen erfordert dabei die Verwendung von Modellierungsstrategien, die es erlauben, beliebige Zusammenhänge zwischen einer Vielzahl potentieller Prädiktoren zu berücksichtigen. Diese Anforderung verlangt nach einem Paradigmenwechsel von der parametrischen hin zu einer nicht-parametrischen, datengetriebenen Modellentwicklung, was zusätzlich durch die zunehmende Verfügbarkeit von Geodaten verstärkt wird. In diesem Zusammenhang haben sich maschinelle Lernverfahren als ein wichtiges Werkzeug erwiesen, um Muster in nicht-linearen und komplexen Systemen zu erfassen. Durch die wachsende Popularität maschineller Lernverfahren in wissenschaftlichen Zeitschriften und die Entwicklung komfortabler Softwarepakete wird zunehmend der Fehleindruck einer einfachen Anwendbarkeit erzeugt. Dem gegenüber steht jedoch eine Komplexität, die im Detail nur durch eine umfassende Methodenkompetenz kontrolliert werden kann. Diese Problematik gilt insbesondere für Geodaten, die besondere Merkmale wie vor allem räumliche Abhängigkeit aufweisen, womit sie sich von "gewöhnlichen" Daten abheben, was jedoch in maschinellen Lernanwendungen bisher weitestgehend ignoriert wird. Die vorliegende Arbeit beschäftigt sich mit dem Potenzial und der Sensitivität des maschinellen Lernens in der Umweltgeographie. In diesem Zusammenhang wurde eine Reihe von maschinellen Lernanwendungen in einem breiten Spektrum der Umweltgeographie veröffentlicht. Die einzelnen Beiträge stehen unter der übergeordneten Hypothese, dass datengetriebene Modellierungsstrategien nur dann zu einem Informationsgewinn und zu robusten raum-zeitlichen Ergebnissen führen, wenn die Merkmale von geographischen Daten berücksichtigt werden. Neben diesem übergeordneten methodischen Fokus zielt jede Anwendung darauf ab, durch adäquat angewandte Methoden neue fachliche Erkenntnisse in ihrem jeweiligen Forschungsgebiet zu liefern. Im Rahmen der Arbeit wurde eine Vielzahl relevanter Umweltmonitoring-Produkte entwickelt. Die Ergebnisse verdeutlichen, dass sowohl hohe fachwissenschaftliche als auch methodische Kenntnisse unverzichtbar sind, um den Bereich der datengetriebenen Umweltgeographie voranzutreiben. Die Arbeit demonstriert erstmals die Relevanz räumlicher Überfittung in geographischen Lernanwendungen und legt ihre Auswirkungen auf die Modellergebnisse dar. Um diesem Problem entgegenzuwirken, wird eine neue, an Geodaten angepasste Methode zur Modellentwicklung entwickelt, wodurch deutlich verbesserte Ergebnisse erzielt werden können. Diese Arbeit ist abschließend als Appell zu verstehen, über die Standardanwendungen der maschinellen Lernverfahren hinauszudenken, da sie beweist, dass die Anwendung von Standardverfahren auf Geodaten zu starker Überfittung und Fehlinterpretation der Ergebnisse führt. Erst wenn Eigenschaften von geographischen Daten berücksichtigt werden, bietet das maschinelle Lernen ein leistungsstarkes Werkzeug, um wissenschaftlich verlässliche Ergebnisse für die Umweltgeographie zu liefern

Adaptive management of Ramsar wetlands

Abstract The Macquarie Marshes are one of Australia’s iconic wetlands, recognised for their international importance, providing habitat for some of the continent’s more important waterbird breeding sites as well as complex and extensive flood-dependent vegetation communities. Part of the area is recognised as a wetland of international importance, under the Ramsar Convention. River regulation has affected their resilience, which may increase with climate change. Counteracting these impacts, the increased amount of environmental flow provided to the wetland through the buy-back and increased wildlife allocation have redressed some of the impacts of river regulation. This project assists in the development of an adaptive management framework for this Ramsar-listed wetland. It brings together current management and available science to provide an informed hierarchy of objectives that incorporates climate change adaptation and assists transparent management. The project adopts a generic approach allowing the framework to be transferred to other wetlands, including Ramsar-listed wetlands, supplied by rivers ranging from highly regulated to free flowing. The integration of management with science allows key indicators to be monitored that will inform management and promote increasingly informed decisions. The project involved a multi-disciplinary team of scientists and managers working on one of the more difficult challenges for Australia, exacerbated by increasing impacts of climate change on flows and inundation patterns