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

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

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

CARACTERIZAÇÃO ESPECTRAL DA ÁGUA DO RESERVATÓRIO DE ITUPARARANGA, SP, A PARTIR DE IMAGENS HIPERESPECTRAIS HYPERION E ANÁLISE DERIVATIVA

A atual disponibilidade de imagens hiperespectrais do sensor orbital Hyperion/EO1 trouxe novas perspectivas para estudos de ambientes aquáticos por possibilitar a estimativa remota de diferentes constituintes opticamente ativos (COAs) no corpo d’água. As variações na composição e concentração de COAs provocam diferentes padrões de absorção e espalhamento da radiação eletromagnética, passíveis de serem detectados usando dados hiperespectrais. Nesse contexto, foi realizada uma investigação visando a caracterização espectral da água de um reservatório destinado ao abastamento público (Reservatório de Itupararanga), a partir de imagens Hyperion/EO1 e da técnica de análise derivativa aplicada à curvas espectrais geradas. Para isso, simultaneamente à tomada de uma imagem Hyperion/EO1, foram feitas mensurações “in situ” e coleta de água para análise laboratorial em pontos amostrais georreferenciados. Após a correção radiométrica da imagem, foram extraídos os espectros de reflectância dos pixels, para cada estação de amostragem e as curvas obtidas foram submetidas à técnica de análise derivativa, a qual evidenciou feições de absorção e espelhamento associadas, principalmente, à presença de pigmentos fotossintetizantes. Os resultados obtidos com a imagem hiperespectral mostraram presença de fitoplâncton e atividade algal no reservatório de Itupararanga, consistente com as observações de campo

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

CARACTERIZAÇÃO ESPECTRAL DA ÁGUA DO RESERVATÓRIO DE ITUPARARANGA, SP, A PARTIR DE IMAGENS HIPERESPECTRAIS HYPERION E ANÁLISE DERIVATIVA

A atual disponibilidade de imagens hiperespectrais do sensor orbital Hyperion/EO1 trouxe novas perspectivas para estudos de ambientes aquáticos por possibilitar a estimativa remota de diferentes constituintes opticamente ativos (COAs) no corpo d’água. As variações na composição e concentração de COAs provocam diferentes padrões de absorção e espalhamento da radiação eletromagnética, passíveis de serem detectados usando dados hiperespectrais. Nesse contexto, foi realizada uma investigação visando a caracterização espectral da água de um reservatório destinado ao abastamento público (Reservatório de Itupararanga), a partir de imagens Hyperion/EO1 e da técnica de análise derivativa aplicada à curvas espectrais geradas. Para isso, simultaneamente à tomada de uma imagem Hyperion/EO1, foram feitas mensurações “in situ” e coleta de água para análise laboratorial em pontos amostrais georreferenciados. Após a correção radiométrica da imagem, foram extraídos os espectros de reflectância dos pixels, para cada estação de amostragem e as curvas obtidas foram submetidas à técnica de análise derivativa, a qual evidenciou feições de absorção e espelhamento associadas, principalmente, à presença de pigmentos fotossintetizantes. Os resultados obtidos com a imagem hiperespectral mostraram presença de fitoplâncton e atividade algal no reservatório de Itupararanga, consistente com as observações de campo

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