The spatial dynamics of landscapes are the consequence of a multiplicity of relations among physical, biological and social forces. So, it is essential the assessment of the driving forces related to Land Use and Land Cover changes (LULC) to understand the change process. The stochastic modeling technique in Geographical Information System (GIS) - Markov Chain (MC) analysis and Cellular Automata (CA) allows the predictions of future changes based on changes that have occurred in the past. This chapter aims to present a dynamic simulation model for LULC changes in ‘Sado Estuary’ and ‘Comporta-Galé’ Natura 2000 Sites (Portugal) for the beginning of the second half of the XXI century by using MC and CA. Regarding the quantification of the fragmentation processes and LULC changes in ‘Sado Estuary’ and ‘Comporta- Galé’ Natura 2000 Sites, these models are able to reveal non-obvious trends in the data and to describe ecological patterns. From an applied research point of view, this approach is useful to identifying adequate planning and management strategies for coastal ecosystems, for monitoring and planning natural and protected environments.

AIM: The aim was to identify the primary drivers of compositional change in breeding bird assemblages over a 40-year period. LOCATION: Britain. TIME PERIOD: From 1970 to 2010. MAJOR TAXA STUDIED: Birds. METHODS: Using morphological trait measurements and a dataset of presence-absence data for British breeding birds surveyed in 10 km × 10 km hectads during two time periods, we calculated temporal taxonomic and functional beta diversity for each hectad alongside the change in species richness, mean nearest taxon distance (MNTD) and mean pairwise distance (MPD). We also estimated potential drivers of beta diversity, including climatic and land-use and land-cover (LULC) change variables, elevation and assemblage species richness in 1970 (1970rich). We used random forest regressions to test which variables best explained compositional change in the assemblages. We also assessed spatial taxonomic and functional change by analysing multiple-site beta diversity and pairwise dissimilarities between time periods. RESULTS: Initial (1970) species richness was the most important predictor (highest importance score) across all models, with areas characterized by higher initial richness experiencing less assemblage change overall. The coordinates included to capture spatial autocorrelation in the data were also important predictors of change. Most cli-mate and LULC variables had relatively low explanatory power; elevation and average temperature were the most influential. All metrics increased slightly with increasing elevation, except for species richness change and MPD, which decreased. MAIN CONCLUSIONS: The composition of British breeding bird assemblages changed substantially between 1970 and 2010. Spatial heterogeneity increased, both taxonomically and functionally. We show evidence that hectads with larger assemblages have been buffered from temporal diversity change and that those at higher elevations changed more in composition than those at lower elevations. Overall, coarse-resolution climate and LULC explained only small to moderate amounts of variation, suggesting that stochastic assembly change or finer-scale drivers might be drivers of temporal changes in assemblage composition.

Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao’s Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements.

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km. Several satellite-based high-resolution products are currently available for implementation into RCMs. One of the most recent products is the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) dataset. While the ESA CCI LC has been assessed globally, an evaluation against regional, independent LULC datasets is necessary to identify LULC inaccuracies in the respective region of interest and to give regional climate modelers estimates for the uncertainty in the land use forcing. In the present work the ESA CCI LC dataset is compared to the COoRdination and INformation on the Environment (CORINE) Land Cover (CLC). Agreement between the datasets is assessed by proportional area comparison (PAC). The resulting agreement measures are compared to the results of a majority approach (MA) to explore possible differences between the methods. Three timesteps of ESA CCI LC matching the timesteps of CLC are assessed to take a change in agreement over time into account. In addition to the quantification of agreement, spatial patterns of possible issues with ESA CCI LC are identified through utilization of geospatial information systems (GIS). Using the PAC, the agreement of ESA CCI LC with CLC is found to be ∼76 % for the research area (RA). Although the agreement decreases slightly using the PAC, no substantial differences in agreement measures were found compared to the results of the MA. Dominant LULC categories agriculture and forest show an agreement of over 80 % with CLC. A few major issues were found for grassland, wetlands, settlements, and water bodies in the RA of which some might influence RCM performance if the dataset is implemented without adjustment. We highly recommend to apply the PAC to other regions in Europe and further globally to investigate if the found issues are also found elsewhere. The use of more independent regional and specified datasets for validation but also for possible improvement of the ESA CCI LC dataset is suggested.

The aim of this paper is to perform a preliminary analysis of the compatibility and quality of the available time series of land cover data available for continental Portugal, in particular, Climate Change Initiative Land Cover maps, which are available annually from 1992 to 2015; CORINE Land Cover and the Urban Atlas for 2006 and 2012; and the Portuguese Carta de Ocupação do Solo for 2007 and 2010. Changes were first identified per product between the different data sets for consecutive dates and then a comparison was made between products. This was followed by validation of two study areas using the COS and UA as reference products. The results show that increases in urbanization are visible in all pairs of products but that the amount of change varies. Moreover, some changes are not in the same direction but may be attributable to classes with small areas and the coarser resolution of the CCI LC maps compared to the other products. The CCI LC maps also overestimate the forest/natural vegetation class by 11-13%, which is also the largest class in Portugal.

The present study aimed to analyze the effects of soil cover in Permanent Preservation Areas (PPAs) downstream of the HPP Pedra Furada Dam, in Sirinhaem river in Brazil (Ribeirão-PE), in the occurrence of flows for return periods of 10 and 10,000 years. Field survey data such as orthophotos, topography and bathymetric information were used provided by the company that owns the dam, also public hydrological data from the National Water Agency (Agência Nacional de Águas-ANA). Through the simulation of the two-dimensional hydrodynamic model with the software HEC-RAS 5.0.7, in transient regime, the roughness coefficients (Manning) of the margins of Sirinhaém River were varied, making analyzes in distant sections 5, 15 and 25 km from the dam. The results showed that the occupation of these PPAs by native/tree vegetation increased the roughness coefficient. At best, it provided an 8-fold increase of this coefficient, reducing the time of arrival of the wave in 2h50 min for the scenario of 10-year flood and in 2h10min for the scenario of 10,000-year flood in the most distant section of the dam. For the nearest section, this increase in Manning’s value led to an increase in water depth of, approximately, 2 meters inside the river channel, for both flow scenarios. Therefore, it is concluded that well-vegetated PPAs reduce the height of the water depth over flood plains closer to the dam, in addition to delaying the wave's arrival in more distant points, contributing to the implementation of contingency in case of emergency.

Este trabalho analisa e discute a contribuição das áreas verdes sobre variáveis meteorológicas, a partir de uso de sensores integrados a nós-sensores desenvolvidos para esse estudo, visando, dessa forma, a qualidade ambiental no meio urbano. Nesse sentido, foi proposto e implementado nós-sensores para o monitoramento em uma área próxima a uma APP abrangendo pontos de coleta nos municípios de Paulínia (SP) e Campinas (SP), dentro dos limites da bacia do Rio Anhumas. Ambos os nós-sensores foram compostos por um microprocessador, sensor (DHT22 ou MG811), bateria, adaptador de cartão de memória e cartão SD. Os parâmetros a serem estudados a partir desses sensores foram os de temperatura do ar e da umidade relativa do ar com o DHT22, e concentração de dióxido de carbono (CO2) com MG811. Diante dos resultados obtidos observou-se a necessidade do encapsulamento e proteção dos mesmos, visando a contenção de possíveis impactos da radiação direta sob os sensores. Atestam, também, que a APP contribuiu de forma efetiva, na parcela estudada em Campinas, enquanto para o município de Paulínia, pode- se observar pouca influência da APP em decorrência de sua distância em relação a região estudada em Paulínia. Além dessas interações, identificou-se influências no ambiente exercidas por áreas de vegetação e com atividades agrícolas, podendo aumentar ou reduzir o nível de CO2 na região, e contribuir para boas médias de temperatura e umidade do ar.

Traditional geographic information has been produced by mapping agencies and corporations, using high skilled people as well as expensive precision equipment and procedures, in a very costly approach. The production of land use and land cover databases are just one example of such traditional approach. On the other side, The amount of Geographic Information created and shared by citizens through the Web has been increasing exponentially during the last decade, resulting from the emergence and popularization of technologies such as the Web 2.0, cloud computing, GPS, smart phones, among others. Such comprehensive amount of free geographic data might have valuable information to extract and thus opening great possibilities to improve significantly the production of land use and land cover databases. In this thesis we explored the feasibility of using geographic data from different user generated spatial content initiatives in the process of land use and land cover database production. Data from Panoramio, Flickr and OpenStreetMap were explored in terms of their spatial and temporal distribution, and their distribution over the different land use and land cover classes. We then proposed a conceptual model to integrate data from suitable user generated spatial content initiatives based on identified dissimilarities among a comprehensive list of initiatives. Finally we developed a prototype implementing the proposed integration model, which was then validated by using the prototype to solve four identified use cases. We concluded that data from user generated spatial content initiatives has great value but should be integrated to increase their potential. The possibility of integrating data from such initiatives in an integration model was proved. Using the developed prototype, the relevance of the integration model was also demonstrated for different use cases.

Analisar espacialmente a cartografia de ocupação da terra do distrito de Marracuene e áreas circunvizinhas a partir de imagens do satélite Landsat 5 significa interpretar as imagens tidas dos sensores a bordo nestes satélites de épocas diferentes. A metodologia usada baseou-se na revisão bibliográfica, uso de técnicas de Detecção Remota (DR) e Sistemas de Informação Geográfica (SIG), que além da aquisição e análise espacial permitiram a realização da classificação das imagens da área de estudo, o manuseamento da base de dados (BDCOV250) existente do CENACARTA (Centro Nacional de Cartografia e Teledetecção) e da DNTF (Direcção Nacional de Terras e Florestas). Neste trabalho foi feita a classificação das imagens de Landsat 5 para os anos de 1991 e 2011 seguindo-se da validação (matriz de confusão) que mostra a qualidade do modelo gerado. Como produto final foram elaborados mapas de uso e cobertura da terra para os dois anos em análise. Com base nestes resultados foi possível posteriormente fazer a detecção de mudanças o que permitiu verificar as classes que com o tempo mudaram e as que se mantiveram inalteradas. A cartografia de uso e cobertura da terra baseada em imagens de satélite permitirá aos planificadores tomarem conhecimento sobre a situação actual do distrito, para acções que permitam a criação de meios para conservação, uso racional e consciente dos recursos florestais existentes no distrito.