A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Spatial modelling of grazing pressure by small ruminants in Bragança Region

Mestrado de dupla diplomação com o Institute Agronomic and Veterinary Hassan IIExtensive grazing systems are characterised by low stocking densities, with positive impacts on the landscape, promoting diversity and heterogeneity. In order to manage this type of systems, we have implemented a robust tool which is the evaluation of the grazing pressure. This latter can establish the relationship between the ruminant and the pasture. This study is made in Bragança region, situated in the northeast part of Portugal. We used available databases such as: land use and cover (LUC) map of Portugal (COS2018), parishes’ administrative boundaries (CAOP2012) and sheep and goats’ locations and headcounts of the study area (OTSA). We define eight LUC classes: permanent crops (PC), annual crops (AC), grasslands (G), shrublands (S), grazed (GF) and ungrazed forests (UF), urban (U) and water bodies (W). The stocking densities and the distribution of the grazing pressure over the LUC classes was done by GIS geoprocessing techniques involving multiple ring buffer zones, data overlapping and spatial interpolation. We used two different methods for spatial interpolation of stocking densities; the weighted inverse distance (IDW) and the ordinary kriging (OK), with better results for the latter, with average prediction errors of 0.0003. Overlapping the grazing areas of the LUC map and the stocking densities, it allows us to obtain the grazing pressure (GP). The most common GP in Bragança is about 1-1.5 sheep or goats/ha. The LUC class with the highest grazing pressure is annual crops (2.22 sheep or goat/ha), the less grazed class is shrublands (1.42 sheep or goat/ha). Regarding the availability of LUC, shrublands have the highest coverage in Bragança region (26.8%), followed by PC (20.5%), GF (18.5%), AC (15.7%), UF (9.2%), G (5.5%), U (3.1%) and W (0.7%). The herds in the study area are globally composed of 11.42% goats and 88.58% sheep. The grazing pressure is related to the food preferences of each species and has been taken into account in this assessment in order to increase the accuracy of the results obtained.Os sistemas de pastoreio extensivos caracterizam-se por baixos encabeçamentos, com impactos positivos sobre a paisagem, promovendo a diversidade e a heterogeneidade. A fim de gerir este tipo de sistemas, implementámos uma ferramenta robusta que é a avaliação da pressão de pastoreio. Este último pode estabelecer a relação entre o ruminante e o pasto. Este estudo é realizado na região de Bragança, situada na parte nordeste de Portugal. Utilizámos bases de dados disponíveis, tais como: mapa de uso e cobertura do solo (LUC) de Portugal (COS2018), limites administrativos das freguesias (CAOP2012) e localizações e efectivos pecuários de ovinos e caprinos da área de estudo (OTSA). Definimos oito classes de LUC: culturas permanentes (PC), culturas anuais (AC), prados (G), matos (S), florestas pastoreadas (GF) e florestas não pastoreadas (UF), áreas urbanas (U) e massas de água (W). As densidades de pastoreio e a distribuição da pressão de pastoreio sobre as classes LUC foram feitas por técnicas de geoprocessamento GIS envolvendo “multiple ring buffer zones”, sobreposição de dados e interpolação espacial. Utilizámos dois métodos diferentes para a interpolação espacial das densidades de pastoreio; a distância inversa ponderada (IDW) e o kriging normal (OK), com melhores resultados para este último, com erros de predição médios de 0,0003. Sobrepondo as áreas de pastagem do mapa LUC e as densidades de pastoreio, permite-nos obter a pressão de pastoreio (GP). O GP mais comum em Bragança é cerca de 1-1,5 ovelhas ou cabras/ha. A classe LUC com maior pressão de pastoreio é a de culturas anuais (2,22 ovelhas ou cabras/ha), a classe menos pastoreada é a de matos (1,42 ovelhas ou cabras/ha). Relativamente à disponibilidade do LUC, os matos têm a maior cobertura na região de Bragança (26,8%), seguidos pelo PC (20,5%), GF (18,5%), AC (15,7%), UF (9,2%), G (5,5%), U (3,1%) e W (0,7%). Os rebanhos na área de estudo são compostos globalmente por 11,42% de caprinos e 88,58% de ovinos. A pressão de pastoreio está relacionada com as preferências alimentares de cada espécie e foi tomada em consideração nesta avaliação a fim de aumentar a exactidão dos resultados obtidos

MAPPING SOIL ORGANIC CARBON DYNAMICS OVER THE LAST DECADES IN MEDITERRANEAN AGRO-ECOSYSTEMS WITH LEGACY DATA

Summary Soil organic carbon (SOC) represents the biggest carbon pool of the biosphere, bigger than the living plant pool. In agriculture, SOC is of pivotal importance for sustainable soil management and is a main soil fertility indicator. As soils are responsible for food production and the provision of various ecosystem services, there is a sturdy interest in understanding how land use and management affect natural plant and crop growth, and ecosystem resilience and functioning. These processes require time and soil sustainability is to be evaluated in a long-term economic perspective by policy makers with the aim of maintaining adequate, and likely improved, conditions of the soil and the whole farm for the future. Thus, long-term actions for crop sustainability could also admit little short-time yield reduction if yield potential, stability and environmental health are maintained at the long-time. Food production and ecosystem services provision depend on the maintenance, or increase, of SOC in agricultural soil, since SOC act as a short-term nutrient reservoir, increase water holding capacity and soil infiltration rate, reduce soil compaction, and favour soil resilience against pollutants. These effects should be taken into account at both a narrow and broad geographical breadth. When aiming to manage SOC at broad geographical extent, a detailed knowledge of SOC distribution and likely change in time is required. However, such a knowledge relies on correct sampling method and modelling procedures that in turn depend on the environmental variability of the area under study. Mediterranean areas are frequently variable as an harbour, the area has been subjected to a high share of soil and above-ground biodiversity and experienced long cultivation history and intensification since the last century, which increased their fragility. In this environment, the acquisition of reliable information on SOC can require a highly dense sampling, which can also negatively affect some relict environment. In addition, sampling can imply a high cost for field work and laboratory analyses. The aim of my Ph.D. work was thus to investigate the main factors related to SOC spatial distribution in agricultural land under various pedoclimatic conditions in semiarid Mediterranean areas, using a legacy soil database (1968-2008) of SOC and soil bulk density. The dissertation is structured in six chapters: the first one is a general introduction where the rationale of the dissertation is explained, and the research questions are stated. The second chapter is a novel approach to systematically collecting literature from international peer-review issues, namely systematic map. The third one is an analysis of the legacy soil database, which intends to make the database ready to be used for the SOC assessment and for the digital soil mapping. The fourth chapter touches an issue dealing with SOC stock mapping with the boosted regression tree and a set of covariates to produce local SOC benchmarks to be compared with European and Global SOC maps. The fifth chapter fits in the same modelling frame and it is addressed at the SOC dynamics using the most widespread legacy sampling campaign. A high number of available spatial data were collected and computed and used to calibrate the SOC models. At this stage, due to the ungridded structure of the data, a machine learning based model has been used (Boosted Regression Trees). The last chapter is a comparison of models (geostatistical, machine learning and linear), and shows useful information about the way that the error is reported by each algorithm. Soil maps are not just produced for the sake of creating attractive geographical visualizations: they have a very precise task to fulfil, i.e. provide accurate and reliable information on soil properties that decision makers can use to plan interventions of any kind. The use of the Regression Kriging and Boosted Regression Trees models, which resulted in the best prediction performance in terms of R2 and RMSE, highlighted the SOC dependence on environmental factors, and the prediction of the agricultural land covers. All land cover groups were studied in the preliminary stage of this study (chapter 2), while only the cropland identified with the legacy data was the candidate for the development of the final models which lead to the detection of a positive SOC trend. The last chapter aimed at the comparison between geostatistical, machine learning and linear models to predict SOC in agricultural lands, and an improvement in local uncertainty estimation. The outstanding result was that SOC at the monitoring sites were accurately simulated, being in full agreement with observed data. Once more, actual data will be available and the model will be calibrated and validated, a model of SOC potential sequestration regional scale can be produced. The results of this dissertation has led to a clear and shared vision in the community regarding the selection of the estimation methods for SOC prediction needs to be based on careful considerations. It is good practice to test algorithms already used in literature for similar purposes, but it may be counterproductive to only look at an algorithm because it is new and never used before in a particular field. This sometimes happens in science where methods are selected only because fashionable and not based on real and tested experiments. In the dissertation the origin of the data was sometimes know and sometimes it has been data driven based. In particular, sampling design was based on geostatistics only in the 2008 campaign and it may well be that looking at very advanced methods like deep-learning could be interesting, but still less accurate than the geostatistical kriging based algorithms, which can also provide robust and well tested uncertainty estimations. In summary, even though we have now access to advanced algorithms it does not mean that we need to use them blindly without fully considering what we are trying to achieve with our working hypothesis and research question

Anthropogenic modifications to fire regimes in the wider Serengeti‐Mara ecosystem

Fire is a key driver in savannah systems and widely used as a land management tool. Intensifying human land uses are leading to rapid changes in the fire regimes, with consequences for ecosystem functioning and composition. We undertake a novel analysis describing spatial patterns in the fire regime of the Serengeti‐Mara ecosystem, document multidecadal temporal changes and investigate the factors underlying these patterns. We used MODIS active fire and burned area products from 2001 to 2014 to identify individual fires; summarizing four characteristics for each detected fire: size, ignition date, time since last fire and radiative power. Using satellite imagery, we estimated the rate of change in the density of livestock bomas as a proxy for livestock density. We used these metrics to model drivers of variation in the four fire characteristics, as well as total number of fires and total area burned. Fires in the Serengeti‐Mara show high spatial variability—with number of fires and ignition date mirroring mean annual precipitation. The short‐term effect of rainfall decreases fire size and intensity but cumulative rainfall over several years leads to increased standing grass biomass and fuel loads, and, therefore, in larger and hotter fires. Our study reveals dramatic changes over time, with a reduction in total number of fires and total area burned, to the point where some areas now experience virtually no fire. We suggest that increasing livestock numbers are driving this decline, presumably by inhibiting fire spread. These temporal patterns are part of a global decline in total area burned, especially in savannahs, and we caution that ecosystem functioning may have been compromised. Land managers and policy formulators need to factor in rapid fire regime modifications to achieve management objectives and maintain the ecological function of savannah ecosystems

Quantification du risque incendie par métamodélisation de la propagation de feux de forêt

This work addresses the quantification of wildfire risk by relying on simulations of fire spread. The objectives are to compute the probability distribution of burned surfaces that could result from wildfire ignition and quickly generate maps to assess which areas should receive focused protection against wildfires. This probability distribution should represent the uncertainty in the simulations. First, an ensemble of wildland fire spread simulations accounting for sources of uncertainty is generated following a Monte Carlo approach, and probabilistic evaluation of the predictions with observations is carried out. Then, the underlying probability distributions are calibrated based on the observations by adapting the Wasserstein distance to the comparison of burned surfaces to improve prediction performance in presence of uncertainty. Subsequently, a deep learning approach is followed to train a ``hybrid'' neural network with a convolutional part, thus building an emulator of ``potential'' fire size simulated by the fire spread model allowing to considerably reduce the computational time implied by the large amount of simulations required for high-resolution maps. Eventually, this emulator is applied to derive fire danger mapping from daily weather forecasts and applied to assess relatively large fire events.Ce travail porte sur la quantification du risque incendie en se fondant sur des simulations de propagation de feux de forêt. Les objectifs sont de calculer la distribution de probabilité des surfaces brûlées pouvant résulter d'un départ de feu et de générer des cartes permettant d'estimer quelles zones doivent être protégées en priorité. Les simulations pouvant donner lieu à des erreurs de prévision, la distribution de probabilité en question doit représenter l'incertitude associée aux simulations. Dans un premier temps, un ensemble de simulations de propagation de feux de forêt prenant en compte les sources d'incertitude est généré selon une approche Monte Carlo, et les prévisions, probabilistes, sont comparées à des observations selon des critères adaptés. Ensuite, les distributions de probabilité sous-jacentes sont calibrées à partir des observations en adaptant la distance de Wasserstein à la comparaison de surfaces brûlées afin d'améliorer la qualité des prévisions, tout en tenant compte de l'incertitude. Par la suite, une approche d'apprentissage profond est mise en œuvre pour entraîner un réseau de neurones ``hybride'' avec une partie convolutionnelle, élaborant ainsi un émulateur de taille de feu ``potentielle'' simulée par le modèle de propagation afin de diminuer considérablement le temps de calcul associé au grand nombre de simulations nécessaires à l'élaboration de cartes à haute résolution. Enfin, l'émulateur est utilisé pour générer des cartes de danger incendie à partir de vraies prévisions météorologiques générées pour des jours où des feux relativement grands ont eu lieu

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Climate change mitigation: annual carbon balance accounting and mapping in the national forest ecosystems (continental Portugal)

We present in this article the carbon balance accounting and mapping in the Portugal continental forests (Mediterranean forest), which occupies 36% of the national territory, mostly private (93%). These forests are characterized by their economic, social and environmental importance values, but during these last years, they are undergoing natural and anthropogenic disturbances and also a strong wood demand for supplying the industry sector. The first goal of this study was to quantify the different components of the carbon (C) cycle, gain and losses, using atmospheric flow approach (gain-loss approach) developed by Intergovernmental Panel on Climate Change (IPCC). The carbon gain reflects the yearly photosynthetic sequestration. The carbon losses reflect the different yearly disturbances like fires, forest logging, pests and diseases attacks. This method allows us to assess the carbon balance evolution from 1995 until 2014 and to identify the most important species in climate change mitigation regarding the air purification or the greenhouse gases emissions contribution. Our second purpose is mapping the carbon-density areas with two different approaches, firstly the direct Remote Sensing (DRS) approach using MODIS images, secondly the indirect approach named Combine and Assign (CA) Approach. MODIS images allow the accounting of Net Primary Productivity (NPP) which presents the quantity of carbon absorbed by vegetation cover during a period of time as a key indicator of ecosystem performance. The CA Approach combines remote sensing and field data in GIS environment to assess the yearly carbon sequestration for each ecozone and the carbon losses by fires in 2010, using the atmospheric flow proposed by IPCC. Our third objective is to link the NPP in 2017 derived from MOD17A3 (MODIS product) with abiotic factors (precipitation, temperature, elevation), to find the best conditions for carbon sequestration. Several geostatistical technics were tested to interpolate climatic factors for all the country. Towards the end, mitigation measures will be proposed.Apresentamos nesta tese a quantificação e o mapeamento do balanço de carbono nas florestas de Portugal continental, que ocupa 36% do território nacional, maioritariamente privado (93%). A floresta portuguesa possui elevado valor económico, social e ambiental, mas durante os últimos anos tem sofrido distúrbios naturais e antropogénicos. Tem também crescido a procura de madeira para suprir o sector industrial. O primeiro objectivo deste estudo foi quantificar os diferentes componentes do ciclo de carbono (C), ganhos e perdas, utilizando a abordagem de fluxo atmosférico (abordagem ganho-perda) desenvolvida pelo Painel Intergovernamental sobre Mudanças Climáticas (IPCC). O ganho de carbono reflecte o sequestro fotossintético anual. As perdas de carbono reflectem as diferentes perturbações anuais, como incêndios, extracção de madeira, insectos e ataques de doenças. Este método permite-nos avaliar a evolução do balanço de carbono de 1995 até 2014 e identificar as espécies mais importantes na mitigação da mudança climática em relação à purificação do ar ou a contribuição das emissões de gases de efeito estufa. Nosso segundo objectivo foi mapear a densidade de carbono por áreas homogéneas com duas abordagens diferentes, em primeiro lugar a abordagem directa de Detecção Remota (DR) usando imagens MODIS, em segundo lugar a abordagem indirecta denominada “Combine and Assign” (CA). As imagens MODIS permitem a quantificação da Produtividade Primária Líquida (NPP) que apresenta a quantidade de carbono absorvida pela cobertura vegetal durante um período de tempo como um indicador chave do desempenho do ecossistema. A abordagem CA combina dados de DR e dados de campo em ambiente SIG para avaliar o sequestro anual de carbono para cada zona ecológica homogénea considerada e as perdas de carbono por incêndios em 2010, usando o fluxo atmosférico proposto pelo IPCC. O terceiro objectivo foi vincular a NPP de 2017 obtida a partir do MOD17A3 (produto MODIS) com factores abióticos (precipitação, temperatura, elevação), para pesquisar as condições mais favoráveis para o sequestro de carbono. Diversas técnicas geoestatísticas foram testadas para interpolar factores climáticos para todo o país. No final, algumas medidas de mitigação foram propostas