Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees

Data aggregation is a necessity when working with big data. Data reduction steps without loss of information are a scientific and computational challenge but are critical to enable effective data processing and information delineation in data-rich studies. We investigated the effect of four spatial aggregation schemes on Landsat imagery on prediction accuracy of green photosynthetic vegetation (PV) based on fractional cover (FCover). To reduce data volume we created an evenly spaced grid, overlaid that on the PV band and delineated the arithmetic mean of PV fractions contained within each grid cell. The aggregated fractions and the corresponding geographic grid cell coordinates were then used for boosted regression tree prediction models. Model goodness of fit was evaluated by the Root Mean Squared Error (RMSE). Two spatial resolutions (3000 m and 6000 m) offer good prediction accuracy whereas others show either too much unexplained variability model prediction results or the aggregation resolution smoothed out local PV in heterogeneous land. We further demonstrate the suitability of our aggregation scheme, offering an increased processing time without losing significant topographic information. These findings support the feasibility of using geographic coordinates in the prediction of PV and yield satisfying accuracy in our study area.</jats:p

Traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data

Scene perception and traversability analysis are real challenges for autonomous driving systems. In the context of off-road autonomy, there are additional challenges due to the unstructured environments and the existence of various vegetation types. It is necessary for the Autonomous Ground Vehicles (AGVs) to be able to identify obstacles and load-bearing surfaces in the terrain to ensure a safe navigation (McDaniel et al. 2012). The presence of vegetation in off-road autonomy applications presents unique challenges for scene understanding: 1) understory vegetation makes it difficult to detect obstacles or to identify load-bearing surfaces; and 2) trees are usually regarded as obstacles even though only trunks of the trees pose collision risk in navigation. The overarching goal of this dissertation was to study traversability analysis in unstructured forested terrains for off-road autonomy using LIDAR data. More specifically, to address the aforementioned challenges, this dissertation studied the impacts of the understory vegetation density on the solid obstacle detection performance of the off-road autonomous systems. By leveraging a physics-based autonomous driving simulator, a classification-based machine learning framework was proposed for obstacle detection based on point cloud data captured by LIDAR. Features were extracted based on a cumulative approach meaning that information related to each feature was updated at each timeframe when new data was collected by LIDAR. It was concluded that the increase in the density of understory vegetation adversely affected the classification performance in correctly detecting solid obstacles. Additionally, a regression-based framework was proposed for estimating the understory vegetation density for safe path planning purposes according to which the traversabilty risk level was regarded as a function of estimated density. Thus, the denser the predicted density of an area, the higher the risk of collision if the AGV traversed through that area. Finally, for the trees in the terrain, the dissertation investigated statistical features that can be used in machine learning algorithms to differentiate trees from solid obstacles in the context of forested off-road scenes. Using the proposed extracted features, the classification algorithm was able to generate high precision results for differentiating trees from solid obstacles. Such differentiation can result in more optimized path planning in off-road applications

Drivers of small mammals' abundance patterns in a South African landscape: the contexts of management intensity and functional groups

Tese de mestrado, Biologia da Conservação, Universidade de Lisboa, Faculdade de Ciências, 2019A extinção de espécies no passado foi resultado de um processo natural que ocorreu sem qualquer intervenção por parte do Homem. No entanto, o aumento da taxa de extinção das espécies no Antropoceno é maioritariamente de causa humana. Uma das barreiras planetárias que a humanidade já ultrapassou foi a perda de biodiversidade (Rockstrom et al. 2009), devido a atividades como a desflorestação, sobrepesca e pastoreio excessivo (Vitousek et al. 1997; Chapin et al. 2000). Todas estas atividades têm impacto no habitat pois levam à alteração do uso da terra devido à degradação e conversão de habitats (Vitousek et al 1997). Consequentemente, a perda de espécies conduz à redução da eficiência dos serviços e funções do ecossistema, dos quais o ser humano depende (Sala et al. 2000; Cardinale et al. 2012; Mace et al. 2012). Por exemplo, a redução do estrato herbáceo influencia negativamente os pequenos mamíferos, visto que estes dependem fortemente da vegetação para abrigo e comida, o que por sua vez pode reduzir a eficácia do ciclo dos nutrientes, pois os pequenos mamíferos contribuem ativamente para o ciclo do azoto através das suas fezes (Bakker et al. 2004, Clark et al. 2005). Em África, ao longo das últimas décadas, ocorreram grandes alterações no uso do solo devido ao aumento da desflorestação e de áreas de pasto (Stephenne and Lambin 2001). A maioria das paisagens foram convertidas em quintas de gado, terras agrícolas e aglomerados urbanos, levando a uma diminuição dos ecossistemas naturais (Maitima et al. 2009). A descentralização das políticas públicas de conservação em África do Sul, garantiu aos donos das terras direitos sobre a vida selvagem (Pitman et al. 2016) o que levou à conversão dos antigos usos da terra em atividades relacionas com a vida selvagem, tais como ranchos de animais de caça e reservas privadas para ecoturismo. Cada um destes tipos de gestão tem objetivos distintos, o que induz diferentes consequências no ecossistema. Enquanto nas quintas, o principal objetivo é maximizar a produção de ungulados para carne, nas reservas privadas o foco é a conservação do património natural, com o objetivo de maximizar o lucro da exploração, através da atração de caçadores e turistas. A presença destes e as suas atividades custeiam os gastos de manutenção de um habitat o mais natural possível e fomentam a presença de animais carismáticos e altamente valorizados economicamente como os denominados “Big 5” – Elefante, Rinoceronte (Preto e Branco), Búfalo, Leão e Leopardo. Coexistindo com estes dois tipos de gestão da paisagem, podemos encontrar na África austral zonas rurais, que incluem não só os aglomerados urbanos mas também áreas dedicadas à agricultura e pecuária e, por isso, possuem a maior densidade de população humana e abundância de gado doméstico comparativamente aos restantes usos do solo (Parsons et al. 1997). Os pequenos mamíferos são fundamentais para o bom funcionamento do ecossistema pois contribuem para diversos serviços ecossistémicos (Avenant and Cavallini 2007). O facto de serem consumidores primários (Avenant and Cavallini 2007) faz com que sejam elos vitais na estruturação da cadeia trófica (Cameron and Scheel 2001), visto que consomem material vegetal e, paralelamente, dão suporte a uma grande comunidade de predadores, desde aves a mamíferos (Anderson and Erlinge 1977). O curto tempo geracional que os caracteriza faz com que reajam rapidamente às alterações no meio ambiente, o que os torna bons indicadores do funcionamento do ecossistema (Avenant and Cavallini 2007). Devido à diversidade e variabilidade ecológica dos pequenos mamíferos, diversos fatores já foram identificados como importantes e modeladores da estrutura populacional deste taxa, os quais podem ser, maioritariamente, determinados pelas opções de gestão (Blaum et al. 2006). Apesar de muitos estudos terem investigado os padrões espaciais de populações de pequenos mamíferos, poucos investigaram a comunidade de roedores de África do Sul, e existe uma lacuna de conhecimento referente ao efeito de diferentes opções de gestão na variação espacial dos padrões de abundância das espécies ou diferentes grupos funcionais. Com o objetivo de obter esta informação, o presente estudo teve como objetivos: 1) determinar os padrões de abundância da comunidade de pequenos mamíferos residentes em KwaZulu-Natal (África do Sul); 2) determinar os fatores ambientais que mais afetam os padrões de ocupação e abundância, através de uma abordagem funcional baseada em dois grupos de roedores (grandes e pequenos); 3) identificar quais os fatores ambientais que melhor explicam a abundância relativa de roedores a nível local/regional, de forma a prever os padrões de abundância à escala da paisagem; e 4) avaliar a influência do tipo de gestão da paisagem nos padrões encontrados de forma a compreender as consequências (ecológicas e relacionadas com a conservação) de uma gestão heterogénea da paisagem no grupo em estudo. Previu-se inicialmente que: a abundância de roedores seria mais elevada em zonas onde o estrato herbáceo é mais alto (H1), visto que confere proteção contra predadores (Bond et al. 1980; Delcros et al. 2015); a heterogeneidade do habitat terá uma influência positiva na abundância de roedores (H2), assumindo que os roedores maiores são mais influenciados, porque exploram a paisagem a uma escala maior (Sutherland et al. 2000, Peles and Barrett); as opções de gestão influenciam os padrões detetados, nomeadamente áreas cuja gestão permite a existência de um maior número de ungulados (Quintas e Comunidades Rurais) irão suportar comunidades de roedores menos abundantes e estes estarão mais heterogeneamente distribuídos (H3), visto que grandes abundâncias de ungulados tendem a decrescer a cobertura do solo por herbáceas e fragmentar as unidades de paisagem devido à pressão de pastoreio (Hoffman and Zeller 2005; Rautenbach 2013). No entanto, se a manutenção de pastos for uma medida de gestão das Quintas, os pequenos mamíferos podem beneficiar dessa característica apesar da competição com ungulados (Blaum et al. 2006) (H4). Este estudo foi implementado na região de Maputaland em KwaZulu-Natal, África do Sul, mais concretamente em Phinda Private Game Reserve e nas áreas circundantes compostas por um mosaico de paisagens dominadas pelo homem, tal como quintas de gado selvagem, doméstico e terras Zulus. Os roedores foram amostrados com recurso a ink tracking tunnels que permitem aos indivíduos marcarem as suas pegadas para posterior identificação. Foram utilizadas boosted regression trees (Elith et al. 2008) que permitiram analisar a influência nas variáveis ambientais na abundância de roedores, tal como avaliar o comportamento da abundância em função das mesmas. Com base nestes resultados, foi elaborado um mapa preditivo da distribuição dos roedores para as três áreas de estudo. Os resultados demonstraram que os fatores que mais influenciam os padrões de distribuição dos roedores são mais determinados pelos grupos funcionais em estudo (grandes e pequenos roedores) do que pela área em si. No que toca às previsões, confirma-se a importância da vegetação para os pequenos mamíferos (H1) bem como a influência negativa da presença de ungulados (H3). Apenas não foi possível corroborar a importância da heterogeneidade do habitat para os grupos em estudo (H2). Foi possível verificar, através da análise da abundância, que Phinda é o habitat mais adequado para os pequenos mamíferos, seguido pelas quintas. As zonas rurais, estando visivelmente mais degradadas, suportam a menor abundância de roedores. É importante reconhecer as quintas e as reservas como locais importantes para a conservação de pequenos mamíferos, pois ambas suportam abundâncias elevadas de roedores. Os padrões de distribuição diferem, provavelmente por existir competição ou partição do nicho entre os dois grupos funcionais, sendo os grandes roedores o grupo dominante. No geral, o presente estudo demonstra que os diferentes tipos de gestão em África do Sul afetam diferencialmente a comunidade de roedores estudada, e que a divisão em grupos funcionais revela diferenças ecológicas que devem ser consideradas aquando de definição dos planos de gestão e conservação destes taxa.In the past, the extinction of species was the result of a natural process that occurred without any intervention by Man. However, the increase in extinction rate of species in the Anthropocene is mostly of human cause. One of the planetary boundaries that humanity has already exceeded is biodiversity loss (Rockstrom et al. 2009) due to activities such as deforestation, overfishing and overgrazing (Vitousek et al. 1997; Chapin et al. 2000). All these activities have an impact on habitat as they lead to land use change, due to habitat degradation and conversion (Vitousek et al. 1997).Consequently, species loss leads to the reduction of ecosystem services and functions efficiency on which humans depend (Sala et al. 2000; Cardinale et al. 2012; Mace et al. 2012). For example, reducing herbaceous stratum negatively influences small mammals as they rely heavily on vegetation for shelter and food, which in turn can reduce the effectiveness of the nutrient cycle, as small mammals actively contribute to the nitrogen cycle through their faeces (Bakker et al. 2004, Clark et al. 2005). In Africa, over recent decades, major changes in land use have occurred due to increase in deforestation and grazing areas (Stephenne and Lambin 2001). Most landscapes have been converted to cattle ranches, farmland and urban settlements, leading to a decline in natural ecosystems (Maitima et al. 2009). The decentralization of public conservation policies in South Africa gave landowners rights over wildlife (Pitman et al. 2016) which led to the conversion of former land uses into wildlife-related activities such as game ranches and private reserves for ecotourism. Each of these types of management has different objectives, which induce different consequences on the ecosystem. While in farms, the main objective is to maximize the production of ungulates meat, in private reserves the focus is on the conservation of the natural heritage, with the aim of maximizing the profitability of exploration through the attraction of hunters and tourists whose presence and activities support the cost of maintaining a habitat as natural as possible, foster the presence of charismatic and highly valued animals such as the so-called “Big 5” - Elephant, Rhino (Black and White), Buffalo, Lion and Leopard. Coexisting with these two types of landscape management, rural areas can be found in southern Africa, which include not only urban settlements but also areas devoted to agriculture and livestock, thus having the highest human population density and abundance of domestic livestock, compared to other land uses (Parsons et al. 1997). Small mammals are critical to the proper functioning of the ecosystem as they contribute to various ecosystem services (Avenant and Cavallini 2007). Being primary consumers (Avenant and Cavallini 2007) makes them vital links in structuring the food chain (Cameron and Scheel 2001) as they consume plant material and in parallel support a large community of predators, from birds to mammals (Anderson and Erlinge 1977). The short generational time that characterizes them, makes them react quickly to changes in the environment, which makes them good indicators of ecosystem functioning (Avenant and Cavallini 2007). Due to the diversity and ecological variability of small mammals, several factors have already been identified as important and modellers of population structure of this taxa, which can be largely determined by management options (Blaum et al. 2006). Although many studies have investigated the spatial patterns of small mammal populations, few have investigated the South African rodents’ community, and there is a knowledge gap regarding the effect of different management options on spatial variation in species abundance patterns or different functional groups. In order to obtain this information, the present study aimed to: 1) determine the abundance patterns of small mammals’ community living in Kwazulu-Natal region; 2) determine the main environmental factors affecting observed occupancy and abundance patterns, and how these vary between areas with distinct management goals and between functional groups (big and small rodents); 3) use the variability of the environmental factors that best explain the local/regional relative abundance of rodents groups to predict the abundance patterns at the landscape scale; 4) evaluate the influence of landscape management options on the detected abundance patterns and drivers of importance in order to understand the consequences (ecological and conservation-wise) of heterogeneous management over the focal taxa. It was initially predicted that: rodent abundance would be higher in areas where the herbaceous stratum is taller (H1), as it provides protection against predators (Bond et al. 1980; Delcros et al. 2015); habitat heterogeneity will have a positive influence on rodent abundance (H2), assuming that larger rodents are more influenced because they explore the landscape on a larger scale (Sutherland et al. 2000, Peles and Barrett 1996); management options influence the detected patterns, namely areas whose management allows a larger number of ungulates (Farms and Rural Communities) will support less abundant rodent communities and these will be more heterogeneously distributed (H3), as large abundances of ungulates tend to decrease herbaceous land cover and fragment landscape units due to grazing pressure (Hoffman and Zeller 2005) ; Rautenbach 2013) However, if grazing is a farm management measure, small mammals may benefit from this feature despite competition with ungulates (Blaum et al. 2006) (H4). This study was carried out in the Maputaland region of KwaZulu-Natal, South Africa, more specifically in Phinda Private Game Reserve and the surrounding areas made up of a mosaic of human-dominated landscapes such as farms with wild and domestic ungulates and Zulus lands. Rodents were sampled using ink tracking tunnels that allow individuals to mark their tracks for later identification. Boosted regression trees (Elith et al. 2008) were used to analyse the influence of environmental variables on rodent abundance, as well as to evaluate abundance behaviour in relation to them. Based on these results, a predictive map of rodent distribution for the three study areas was prepared. The results showed that the factors that most influence rodent distribution patterns are more determined by the functional groups under study (big and small rodents) than by the area itself. As for my predictions, the importance of vegetation for small mammals (H1) as well as the negative influence of the presence of ungulates (H3) is confirmed. It was not possible to corroborate the importance of habitat heterogeneity for the study groups (H2). Through abundance analysis it was possible to verify that Phinda is the most suitable habitat for small mammals, followed by Farms. Rural Communities, being noticeably more degraded, bear the least abundance of rodents. It is important to recognize farms and reserves as important places for the conservation of small mammals, as both bear high rodent abundances. Distribution patterns differ, probably because there is competition or niche partitioning between the two functional groups, with big rodents being the dominant group. Overall, the present study demonstrates that the different types of management in South Africa differentially affect the rodent community studied, and that the division into functional groups reveals ecological differences that should be considered when defining management and conservation plans for these taxa

A phytolith supported biosphere-hydrosphere predictive model for Southern Ethiopia:Insights into paleoenvironmental changes and human landscape preferences since the last glacial maximum

During the past 25 ka, southern Ethiopia has undergone tremendous climatic changes, from dry and relatively cold during the Last Glacial Maximum (LGM, 25–18 ka) to the African Humid Period (AHP, 15–5 ka), and back to present-day dry conditions. As a contribution to better understand the effects of climate change on vegetation and lakes, we here present a new Predictive Vegetation Model that is linked with a Lake Balance Model and available vegetation-proxy records from southern Ethiopia including a new phytolith record from the Chew Bahir basin. We constructed a detailed paleo-landcover map of southern Ethiopia during the LGM, AHP (with and without influence of the Congo Air Boundary) and the modern-day potential natural landcover. Compared to today, we observe a 15–20% reduction in moisture availability during the LGM with widespread open landscapes and only few remaining forest refugia. We identify 25–40% increased moisture availability during the AHP with prevailing forests in the mid-altitudes and indications that modern anthropogenic landcover change has affected the water balance. In comparison with existing archaeological records, we find that human occupations tend to correspond with open landscapes during the late Pleistocene and Holocene in southern Ethiopia

Yield prediction by machine learning from UAS‑based mulit‑sensor data fusion in soybean

16 p.Nowadays, automated phenotyping of plants is essential for precise and cost-effective improvement in the efficiency of crop genetics. In recent years, machine learning (ML) techniques have shown great success in the classification and modelling of crop parameters. In this research, we consider the capability of ML to perform grain yield prediction in soybeans by combining data from different optical sensors via RF (Random Forest) and XGBoost (eXtreme Gradient Boosting). During the 2018 growing season, a panel of 382 soybean recombinant inbred lines were evaluated in a yield trial at the Agronomy Center for Research and Education (ACRE) in West Lafayette (Indiana, USA). Images were acquired by the Parrot Sequoia Multispectral Sensor and the S.O.D.A. compact digital camera on board a senseFly eBee UAS (Unnamed Aircraft System) solution at R4 and early R5 growth stages. Next, a standard photogrammetric pipeline was carried out by SfM (Structure from Motion). Multispectral imagery serves to analyse the spectral response of the soybean end-member in 2D. In addition, RGB images were used to reconstruct the study area in 3D, evaluating the physiological growth dynamics per plot via height variations and crop volume estimations. As ground truth, destructive grain yield measurements were taken at the end of the growing season.SI"Development of Analytical Tools for Drone-based Canopy Phenotyping in Crop Breeding" (American Institute of Food and Agriculture

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making

Challenges in Big Data analysis arise due to the way the data are recorded, maintained, processed and stored. We demonstrate that a hierarchical, multivariate, statistical machine learning algorithm, namely Boosted Regression Tree (BRT) can address Big Data challenges to drive decision making. The challenge of this study is lack of interoperability since the data, a collection of GIS shapefiles, remotely sensed imagery, and aggregated and interpolated spatio-temporal information, are stored in monolithic hardware components. For the modelling process, it was necessary to create one common input file. By merging the data sources together, a structured but noisy input file, showing inconsistencies and redundancies, was created. Here, it is shown that BRT can process different data granularities, heterogeneous data and missingness. In particular, BRT has the advantage of dealing with missing data by default by allowing a split on whether or not a value is missing as well as what the value is. Most importantly, the BRT offers a wide range of possibilities regarding the interpretation of results and variable selection is automatically performed by considering how frequently a variable is used to define a split in the tree. A comparison with two similar regression models (Random Forests and Least Absolute Shrinkage and Selection Operator, LASSO) shows that BRT outperforms these in this instance. BRT can also be a starting point for sophisticated hierarchical modelling in real world scenarios. For example, a single or ensemble approach of BRT could be tested with existing models in order to improve results for a wide range of data-driven decisions and applications

Integration of remotely sensed soil sealing data in landslide susceptibility mapping

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC(area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter "soil sealing aggregation" significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures

A Study of African Savanna Vegetation Structure, Patterning, and Change

African savannas cover roughly half of the continent, are home to a great diversity of wildlife, and provide ecosystem services to large populations. Savannas showcase a great diversity in vegetation structure, resulting from variation in climatic, edaphic, topographic, and biological factors. Fires play a large role as savannas are the most frequently burned ecosystems on Earth. To study how savanna vegetation structure shifts with environmental factors, it is necessary to gather site data covering the full gradient of climatic and edaphic conditions. Several earlier studies have used coarse resolution satellite remote sensing data to study variation in woody cover. These woody cover estimates have limited accuracy in drylands where the woody component is relatively small, and the data cannot reveal more detailed information on the vegetation structure. We therefore know little about how other structural components, tree densities, crown sizes, and the spatial pattern of woody plants, vary across environmental gradients. This thesis aimed to examine how woody vegetation structure and change in woody cover vary with environmental conditions. The analyses depended on access to very high spatial resolution (\u3c1 \u3em) satellite imagery from sites spread across African savannas. The high resolution data combined with a crown delineation method enabled me to estimate variation in tree densities, mean crown size and the level of aggregation among woody plants. With overlapping older and newer imagery at most of the sites, I was also able to estimate change in woody cover over a 10-year period. I found that higher woody plant aggregation is associated with drier climates, high rainfall variability, and fine-textured soils. These same factors were also indicative of the areas where highly organized periodic vegetation patterns were found. The study also found that observed increases in woody cover across the rainfall gradient is more a result of increasing crown sizes than variation in tree density. The analysis of woody cover change found a mean increase of 0.25 % per year, indicating an ongoing trend of woody encroachment. I could not attribute this trend to any of the investigated environmental factors and it may result from higher atmospheric CO₂ concentrations, which has been proposed in other studies. The most influential predictor of woody cover change in the analysis was the difference between potential woody cover and initial woody cover, which highlights the role of competition for water and density dependent regulation when studying encroachment rates. The second most important predictor was fire frequency. To better understand and explain the dominant ecosystem processes controlling savanna vegetation structure, I constructed a spatially explicit model that simulates the growth of herbaceous and woody vegetation in a landscape. The model reproduced several of the trends in woody vegetation structure earlier found in the remote sensing analysis. These include how tree densities and crowns sizes respond differently to increases in precipitation along the full rainfall range, and the factors controlling the spatial pattern of trees in a landscape

Relating severity of a mountain pine beetle outbreak to forest management history

2014 Summer.Includes bibliographical references.The availability of remote sensing imagery before, during, and after the recent mountain pine beetle (Dendroctonus ponderosae Hopkins) epidemic in the southern Rocky Mountains presents exciting opportunities for assessing the current state of forests and how forest management in previous decades influenced outbreak severity across the landscape. I mapped outbreak severity at a 30-m resolution using integrative spatial modeling. I predicted that: 1) outbreak severity can be accurately predicted and mapped at Fraser Experimental Forest, Colorado using stand characteristics with a boosted regression tree model, Landsat imagery, geographic information system (GIS) data, and field data; and 2) forest stands that were unmanaged since the 1950s will have higher outbreak severity compared to stands that were treated since the 1950s. Outbreak severity, measured by the ratio of dead lodgepole pine (Pinus contorta) basal area to the basal area of all trees, was mapped across Fraser Experimental Forest with a cross-validation correlation of 0.86 and a Spearman correlation with independently observed values of 0.64. The outbreak severity at stands harvested between 1954 and 1985 was lower than comparable uncut stands. Lessons learned about past treatments will inform forest management for future mountain pine beetle outbreaks