Análise de risco da barragem de minério de ferro na Serra Norte no Pará

Hellem Cristina Teixeira Rodrigues; Isabela Lopes de Oliveira; Patrícia Silva dos Santos; Halison Felipe Pimenta Almeida; Luan da Silva Freitas; Francimary da Silva Carneiro; Ana Paula Magno do Amaral; Klewton Adriano Oliveira Pinheiro; Mayra Piloni Maestri; Marcio Braga Amorim

doi:10.33448/rsd-v10i13.18611

Análise de risco da barragem de minério de ferro na Serra Norte no Pará

Research Society and Development ◽

10.33448/rsd-v10i13.18611 ◽

2021 ◽

Vol 10 (13) ◽

pp. e326101318611

Author(s):

Hellem Cristina Teixeira Rodrigues ◽

Isabela Lopes de Oliveira ◽

Patrícia Silva dos Santos ◽

Halison Felipe Pimenta Almeida ◽

Luan da Silva Freitas ◽

...

Keyword(s):

Alos Palsar

A mineração é um dos setores responsáveis pelo uso do solo e ocupação do território brasileiro, tem sido essencial para o crescimento econômico do país. O principal crescimento da Região Norte, no estado do Pará, localiza -se na Serra dos Carajás, onde situa – se uma das maiores jazidas de minério de ferro puro do mundo. Por isso é importante a análise de um futuro impacto socioambiental em caso de rompimento da barragem que recebe o estéril da produção de ferro, por meio do fluxo dos rejeitos em um raio de 50 km da área afetada, utilizando como base às imagens do sensor ALOS PALSAR, com resolução de 12,5 metros. Ao analisar, verifica – se que em caso de rompimento a área afetada primeiramente será Parauapebas devido sua localização, logo após as cidades de Marabá e a região de Canaã, conforme os tipos de elevação demonstrados no mapa, ocasionando impactos ambientais, sociais e até mesmo econômicos, tais efeitos que podem ser irreversíveis e de difícil gestão. De tal modo, para mitigar e prevenir os danos, deve- se fortalecer a legislação através de melhores práticas de governança, programas de monitoramento estruturados, rigorosidade nas leis ambientais, fiscalizações de órgãos ambientais competentes as atividades de barragens e liberação de licenças ambientais.

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Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

Remote Sensing ◽

10.3390/rs12193226 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3226

Author(s):

Daniel Cunningham ◽

Paul Cunningham ◽

Matthew E. Fagan

Keyword(s):

Costa Rica ◽

Forest Cover ◽

Agricultural Land ◽

Forest Area ◽

Tree Cover ◽

Agricultural Crop ◽

Forest Change ◽

Alos Palsar ◽

Tropical Regions ◽

The Impact

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

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Countrywide Monitoring of Ground Deformation Using InSAR Time Series: A Case Study from Qatar

Remote Sensing ◽

10.3390/rs13040702 ◽

2021 ◽

Vol 13 (4) ◽

pp. 702

Author(s):

Mustafa Kemal Emil ◽

Mohamed Sultan ◽

Khaled Alakhras ◽

Guzalay Sataer ◽

Sabreen Gozi ◽

...

Keyword(s):

Time Series ◽

Arabian Peninsula ◽

Ground Deformation ◽

Anthropogenic Activities ◽

Urban Centers ◽

Alos Palsar ◽

Groundwater Levels ◽

Temporal Correlations ◽

Time Series Approach ◽

Sbas Insar

Over the past few decades the country of Qatar has been one of the fastest growing economies in the Middle East; it has witnessed a rapid increase in its population, growth of its urban centers, and development of its natural resources. These anthropogenic activities compounded with natural forcings (e.g., climate change) will most likely introduce environmental effects that should be assessed. In this manuscript, we identify and assess one of these effects, namely, ground deformation over the entire country of Qatar. We use the Small Baseline Subset (SBAS) InSAR time series approach in conjunction with ALOS Palsar-1 (January 2007 to March 2011) and Sentinel-1 (March 2017 to December 2019) synthetic aperture radar (SAR) datasets to assess ground deformation and conduct spatial and temporal correlations between the observed deformation with relevant datasets to identify the controlling factors. The findings indicate: (1) the deformation products revealed areas of subsidence and uplift with high vertical velocities of up to 35 mm/yr; (2) the deformation rates were consistent with those extracted from the continuously operating reference GPS stations of Qatar; (3) many inland and coastal sabkhas (salt flats) showed evidence for uplift (up to 35 mm/yr) due to the continuous evaporation of the saline waters within the sabkhas and the deposition of the evaporites in the surficial and near-surficial sabkha sediments; (4) the increased precipitation during Sentinel-1 period compared to the ALOS Palsar-1 period led to a rise in groundwater levels and an increase in the areas occupied by surface water within the sabkhas, which in turn increased the rate of deposition of the evaporitic sediments; (5) high subsidence rates (up to 14 mm/yr) were detected over landfills and dumpsites, caused by mechanical compaction and biochemical processes; and (6) the deformation rates over areas surrounding known sinkhole locations were low (+/−2 mm/yr). We suggest that this study can pave the way to similar countrywide studies over the remaining Arabian Peninsula countries and to the development of a ground motion monitoring system for the entire Arabian Peninsula.

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High-resolution Alos Palsar for the Characterization of Water Storage at the Fountaine Des Gazelles Dam in Biskra, Eastern Algeria

Journal of the Indian Society of Remote Sensing ◽

10.1007/s12524-021-01365-y ◽

2021 ◽

Author(s):

Abdelhalim Bendib

Keyword(s):

High Resolution ◽

Water Storage ◽

Alos Palsar ◽

Eastern Algeria

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Building Damage Detection Based on OPCE Matching Algorithm Using a Single Post-Event PolSAR Data

Remote Sensing ◽

10.3390/rs13061146 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1146

Author(s):

Yuliang Nie ◽

Qiming Zeng ◽

Haizhen Zhang ◽

Qing Wang

Keyword(s):

Damage Detection ◽

Texture Features ◽

Classification Performance ◽

Building Damage ◽

Alos Palsar ◽

Yushu Earthquake ◽

Matching Algorithm ◽

2011 Tohoku Tsunami ◽

Collapsed Buildings ◽

Study Sites

Synthetic aperture radar (SAR) is an effective tool in detecting building damage. At present, more and more studies detect building damage using a single post-event fully polarimetric SAR (PolSAR) image, because it permits faster and more convenient damage detection work. However, the existence of non-buildings and obliquely-oriented buildings in disaster areas presents a challenge for obtaining accurate detection results using only post-event PolSAR data. To solve these problems, a new method is proposed in this work to detect completely collapsed buildings using a single post-event full polarization SAR image. The proposed method makes two improvements to building damage detection. First, it provides a more effective solution for non-building area removal in post-event PolSAR images. By selecting and combining three competitive polarization features, the proposed solution can remove most non-building areas effectively, including mountain vegetation and farmland areas, which are easily confused with collapsed buildings. Second, it significantly improves the classification performance of collapsed and standing buildings. A new polarization feature was created specifically for the classification of obliquely-oriented and collapsed buildings via development of the optimization of polarimetric contrast enhancement (OPCE) matching algorithm. Using this developed feature combined with texture features, the proposed method effectively distinguished collapsed and obliquely-oriented buildings, while simultaneously also identifying the affected collapsed buildings in error-prone areas. Experiments were implemented on three PolSAR datasets obtained in fully polarimetric mode: Radarsat-2 PolSAR data from the 2010 Yushu earthquake in China (resolution: 12 m, scale of the study area: ); ALOS PALSAR PolSAR data from the 2011 Tohoku tsunami in Japan (resolution: 23.14 m, scale of the study area: ); and ALOS-2 PolSAR data from the 2016 Kumamoto earthquake in Japan (resolution: 5.1 m, scale of the study area: ). Through the experiments, the proposed method was proven to obtain more than 90% accuracy for built-up area extraction in post-event PolSAR data. The achieved detection accuracies of building damage were 82.3%, 97.4%, and 78.5% in Yushu, Ishinomaki, and Mashiki town study sites, respectively.

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