QUANTIFYING ILLEGAL DEFORESTATION IN FRONT OF THE FOREST CODE: POTENTIALITY AND CHALLENGE

Brazil confronts a challenge to implement the Forest Code, now called Native Vegetation Protection Law (LPVN), issued in 2012 under the number 12.651/12. The law introduced new mechanisms to quantified environmental liabilities in Permanent Protection Areas (APP) and Legal Reserve Areas (RL). Thus, this study presents a methodological proposal for calculation of environmental liabilities in areas of "water" permanent preservation and legal reserve using geoprocessing tools. This way, a complex analysis was required, based on the size of the private rural properties, the type of land use/cover, and “temporal cut”, for which there is no methodology defined. The “temporal cut” was defined to fine cancel those who practiced illegal deforestation prior to 22 July 2008, thus creating the figure of the "Consolidated Productive Areas”. This methodology was tested and applied in the municipality of São Félix do Xingu-PA and the results pointed to a total environmental liability of the municipality of 178,835 hectares by 2010. According to requirements established in article 61-A, the settlements were considered rural properties with consolidated productive areas, and thus benefited by law. Despite this, it is important to improve environmental education techniques and the recovering of environmental liabilities of settlements, mainly for sustainable production purposes

UAV-based doline mapping in Brazilian karst. A cave heritage protection reconnaissance

Abstract Dolines are depressions in the soluble ground that indicates the degree of karstification. They may also act as connection points (vulnerability spots) between the surface and underground for the transmission of runoff, sediments, and pollutants. The delineation of these spots (dolines) is a crucial step in environmental management through land use planning to protect the karst underground, which is rich in flora and fauna. This requirement can benefit from a cost-effective, accessible, and non-invasion high-resolution investigation generating digital elevation models (DEMs) from unmanned aerial vehicle (UAV) imagery and automated object detection techniques. This study examines the capabilities of UAV-based DEM in detecting dolines across 50 km2 in the environmentally protected area of river Vermelho (APANRV – Área de Proteção Ambiental das Nascentes do Rio Vermelho). Initially, an automatic objects (doline and no-doline) detection algorithm was applied to the DEM, followed by a visual inspection to differentiate doline from possible dolines in orthomosaic photos, topographic profiles, and shaded UAV-based relief (digital terrain model; DTM and DSM). For the redundancy checking, a cluster analysis with four tests was conducted. The objects generated from the best clusters and morphological analysis were gathered in the same base for visual inspection. Out of a total of 933 objects identified, 41% were obtained from the DSM base, 25% from the perimeter-to-area ratio, and 34% through convergence between the two-analyses. Subsequently, the resulting doline typologies are discussed in reference to their proximity to hydrogeological features and their impacts on underground vulnerability. The findings aligned with the previous research as dolines were highly concentrated near sites where carbonates come in contact with siliciclastic sediments

LAND USE/ COVER (LULC) MAPPING IN BRAZILIAN CERRADO USING NEURAL NETWORK WITH SENTINEL-2 DATA

The Sentinel-2a and 2B satellites form a multispectral imaging mission for Earth observation. They have promising characteristics for the study of soils and vegetation cover, and their data can be applied for land use/cover (LULC) mapping. To this end, neural networks have shown good results in pattern recognition tasks in orbital images. In this sense, the study aimed to evaluate the use of Sentinel 2 (ESA) image for LULC mapping in the Cerrado Biome, through the application of artificial neural network methodology. Among the classes of use and occupation examined, 8 classes were selected, 4 of which were natural (water bodies, savanna, forest and field formation) and 4 anthropic (Pasture, Urban areas, Silviculture and Seasonal Crop). The classification system by artificial neural network (ANN) was considered successful, with thematic accuracy (Kappa coefficient) of 0.77. Although there are still some thematic confusions during the classification process, the classification results were considered superior when compared to the MaxVer classifier. The Sentinel-2 image, together with the use of a neural network, was shown a good input for carrying out this type of mapping.Key words: Orbital Remote Sensing System, Supervised Classification Techniques, LULC classes

DETERMINATION OF POTENTIAL AREAS FOR RESETTLEMENT OF FAMILIES AFFECTED BY THE SÃO FRANCISCO RIVER INTEGRATION PROJECT USING GEOTECHNOLOGIES

The São Francisco River Integration Project is an infrastructure work conducted by the Brazilian federal government, aiming to guarantee water resources security to 390 municipalities, benefitting about 12 million people that suffer with water scarcity in one of the driest regions in the country. This work presents the method and tools used to evaluate land suitability for rural resettlement of displaced families. All with the intent of mitigating socioeconomic impacts for one of the annex channels in the main project axis, considering legal and technical criteria. Using geoprocessing tools, 4490 hectares of land were effectively identified as best suited for this purpose, helping public managers to promptly decide the adequate course of action

ANALYSIS OF THE EFFECTS OF ATMOSPHERIC CORRECTION ON ORBITAL IMAGES FOR STUDIES IN INTERIOR WATER BODIES

The water reservoirs, in addition to their significance in electricity generation, serve as vital resources for various other requirements of the population. Images from orbital sensors have been applied to complement the monitoring of these environments and thus overcome the deficiency of spatial and temporal coverage of traditional techniques. However, studies involving water quality are still a great challenge due to the low signal coming from the water body and the interference of external factors (or environmental factors). Image correction/improvement procedures are often proposed, mainly to reduce atmospheric interference. In this study the best available atmospheric correction techniques were evaluated in order to indicate the technique that most closely matches the spectral response of remotely sensed images obtained in the field. During the study six atmospheric correction algorithms were applied (FLAASH, Second simulation of a Satellite Signal in the Solar Spectrum (6S), L8SR, Aquatic Reflectance (NASA/USGS), ACOLITE and Sen2Cor) that, based on the statistical analysis of discriminant analysis and covariance, indicated the 6S for Landsat and Sentinel images and ACOLITE for Landsat images as the most accurate. Although 6S showed a response close to the reference data, low variability in spectral response was observed. For time series, ACOLITE showed better capacity to correct the data. The type of application is also a preponderant factor, since it was evident that the use of time series indicated a different atmospheric correction technique when compared to the analysis of the scenes individually

Classificação orientada a objeto em associação às ferramentas reflectância acumulada e mineração de dados

The objective of this work was to use the accumulated reflectance technique and data mining application, followed by object-oriented classification, in images of Operational Land Imager (OLI) sensor, Landsat 8, for the classification of native vegetation and agricultural coverage of Cerrado. Four reflectance images were used for the discrimination of six classes – agriculture, livestock, wetland, savannah, forest, and grassland –, for classification of Parque Nacional das Emas and surrounding areas in the state of Goiás, Brazil. The images were segmented for the extraction of sample spectral attributes and application of attribute combinations (mean + mode, all attributes) on data mining. The Weka software was used to construct the decision trees. This methodology indicated that the differentiation among targets increased from the temporal accumulation of the reflectance in all bands and classes, and that the optimal image was that of the sum of the four dates. The classification based on the attribute associations mean + mode showed no restraints in the decision rules processing, unlike the association of all attributes. The mean + mode classification showed a satisfactory accuracy (global accuracy, 69%; Kappa, 58%; and TAU, 63%). The integration of these techniques shows potential to differentiate native and anthropogenic vegetation in the Cerrado.O objetivo deste trabalho foi utilizar as técnicas de reflectância acumulada e mineração de dados, seguidas por classificação orientada a objeto, em imagens do sensor Operational Land Imager (OLI), satélite Landsat 8, para a classificação de vegetação nativa e cobertura agropecuária do Cerrado. Quatro imagens de reflectância foram utilizadas para a discriminação de seis classes – agricultura, pecuária, campo limpo úmido, savana, floresta e campo –, para a classificação do Parque Nacional das Emas, no Estado de Goiás, e adjacências. As imagens foram segmentadas para a extração de atributos espectrais de amostras e a aplicação de combinações de atributos (média + moda, todos os atributos) na mineração de dados. O programa Weka foi utilizado para a construção das árvores de decisão. Essa metodologia indicou que a diferenciação entre alvos aumentou a partir da acumulação temporal da reflectância, em todas as bandas e as classes, e a melhor imagem foi aquela do somatório das quatro datas. A classificação baseada na associação de atributos média + moda não apresentou impedimentos no processamento das regras de decisão, diferentemente da associação de todos os atributos. A classificação média + moda apresentou acurácia satisfatória (exatidão global, 69%; Kappa, 58%; e TAU,  63%). A integração dessas técnicas apresenta potencial para a diferenciação de vegetação nativa e antrópica do Cerrado

Object-oriented classification in association with accumulated reflectance and data mining tools

O objetivo deste trabalho foi utilizar as técnicas de reflectância acumulada e mineração de dados, seguidas por classificação orientada a objeto, em imagens do sensor Operational Land Imager (OLI), satélite Landsat 8, para a classificação de vegetação nativa e cobertura agropecuária do Cerrado. Quatro imagens de reflectância foram utilizadas para a discriminação de seis classes – agricultura, pecuária, campo limpo úmido, savana, floresta e campo –, para a classificação do Parque Nacional das Emas, no Estado de Goiás, e adjacências. As imagens foram segmentadas para a extração de atributos espectrais de amostras e a aplicação de combinações de atributos (média + moda, todos os atributos) na mineração de dados. O programa Weka foi utilizado para a construção das árvores de decisão. Essa metodologia indicou que a diferenciação entre alvos aumentou a partir da acumulação temporal da reflectância, em todas as bandas e as classes, e a melhor imagem foi aquela do somatório das quatro datas. A classificação baseada na associação de atributos média + moda não apresentou impedimentos no processamento das regras de decisão, diferentemente da associação de todos os atributos. A classificação média + moda apresentou acurácia satisfatória (exatidão global, 69%; Kappa, 58%; e TAU,  63%). A integração dessas técnicas apresenta potencial para a diferenciação de vegetação nativa e antrópica do Cerrado.The objective of this work was to use the accumulated reflectance technique and data mining application, followed by object-oriented classification, in images of Operational Land Imager (OLI) sensor, Landsat 8, for the classification of native vegetation and agricultural coverage of Cerrado. Four reflectance images were used for the discrimination of six classes – agriculture, livestock, wetland, savannah, forest, and grassland –, for classification of Parque Nacional das Emas and surrounding areas in the state of Goiás, Brazil. The images were segmented for the extraction of sample spectral attributes and application of attribute combinations (mean + mode, all attributes) on data mining. The Weka software was used to construct the decision trees. This methodology indicated that the differentiation among targets increased from the temporal accumulation of the reflectance in all bands and classes, and that the optimal image was that of the sum of the four dates. The classification based on the attribute associations mean + mode showed no restraints in the decision rules processing, unlike the association of all attributes. The mean + mode classification showed a satisfactory accuracy (global accuracy, 69%; Kappa, 58%; and TAU, 63%). The integration of these techniques shows potential to differentiate native and anthropogenic vegetation in the Cerrado

Implementação de Corredores Ecológicos no Distrito Federal e Entorno Baseado em Critérios Ponderados

De acordo com o Sistema Nacional de Unidades de Conservação, os corredores ecológicos representam a conectividade entre manchas de vegetação naturais ou seminaturais que ligam unidades de conservação. Corredores ecológicos melhoram o fluxo genético, a circulação de espécies e a recolonização de áreas degradadas, dentre outros serviços ecológicos. Até o presente momento, desconhecem-se as áreas aptas a serem corredores ecológicos no Distrito Federal. O objetivo desse trabalho é definir áreas para implementação de corredores ecológicos ao longo do Parque Nacional de Brasília, da Reserva Biológica da Contagem e da Estação Ecológica de Águas Emendadas no Distrito Federal. O estudo foi baseado na integração de dados de declividade, uso e cobertura da terra, sistema viário, limites de propriedades rurais e áreas de proteção permanente e reserva legal. Foram estabelecidos pesos para cada classe (mapa) e subclasse (categoria), expressos em termo de escala que variou continuamente entre 1 (maior impedância) e 9 (menor impedância). Foi estabelecido o grau de importância de cada camada para a implementação do corredor ecológico a partir da metodologia denominada Processo de Análise Hierárquica (AHP), gerando-se o dado de potencialidade para criação de corredores. Com a imagem matricial de potencialidade (custo total), foi possível produzir as imagens de distância e direção de custo. Por meio da ferramenta de caminho de custo, foram determinados os caminhos de menor custo por célula e o melhor caminho único. Foram definidas quatro áreas de corredores ecológicos. O corredor ecológico mais indicado possui 38,61 km de extensão e área total de 14.779 hectares. Esse corredor pode ser uma opção atrativa para os gestores interessados em preservação da fauna e flora ou interessados em incentivar o turismo na região

MAPEAMENTO DE SUSCETIBILIDADE À INUNDAÇÃO UTILIZANDO O MÉTODO DA RAZÃO DE FREQUÊNCIA APLICADO À BACIA DO RIACHO FUNDO - DISTRITO FEDERAL

O mapeamento de suscetibilidade à inundação é importante para o manejo da dinâmica do uso do solo e, consequentemente, da hidrologia urbana local. O presente estudo produziu o mapa de suscetibilidade à inundação na Bacia do Riacho Fundo, Distrito Federal, utilizando o método estatístico bivariado Razão de Frequência (Frequency Ratio), com 30 pontos de inundação observados em 2018 como pontos de treinamento (71%) e outros 12 pontos de inundação (29%) como pontos de validação para desenvolvimento do modelo. O modelo é composto de 12 fatores de influência: declividade, curvatura, aspecto, hipsometria, distância dos rios, índice de potência de escoamento, índice de transporte de sedimento, índice topográfico de umidade, índice de rugosidade do terreno, índice de escoamento superficial, uso e cobertura do solo e geologia. Todas as variáveis com um tamanho de pixel de 12,5 m x 12,5 m. Os fatores de uso e cobertura do solo e geologia local mostraram-se os mais influentes no modelo. A validação do modelo foi realizada utilizando o método da área sob a curva, com uma acurácia de 85,75%. O estudo mostra que o método pode ser usado para auxiliar no estudo de planos de controle e mitigação de inundação em centros urbanos, como a locação preliminar de bacias de detenção.Palavras-chave: suscetibilidade, inundação, mapeamento, razão de frequência, geoprocessamento. FLOOD SUSCEPTIBILITY MAPPING USING THE FREQUENCY RATIO METHOD APPLIED TO THE RIACHO FUNDO BASIN - FEDERAL DISTRICTAbstractFlood susceptibility mapping is important to the management of the urban hydrological dynamic and to the studies conducted to prevent the flood-based problems. This study has produced a flood susceptibility map using a bivariate statistical analysis named frequency ratio (FR) model applied in the Riacho Fundo catchment, with 30 flooding locations (71%) for statistical analysis as training dataset and 12 remaining points (29%) were applied to validate the developed model. Twelve conditioning factors were considered in this study: slope, curvature, aspect, elevation, distance to river, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), terrain roughness index (TRI), superficial runoff index, land use/land cover (LULC) and geology. All these variables were resampled into 12.5×12.5 m pixel size. The model showed LULC and geology as the most influential factors in flooding. The AUC for success rate was 85.75% with the training points. The study shows the method can be used in studies of plans to mitigate and control flooding in urban centers, as preliminary lease of ponds.Keywords: susceptibility, flooding, mapping, frequency ratio, geoprocessing