**2.4 3D modeling**

For hypsometry mapping and 3D modeling, the Shuttle Radar Topography Mission (SRTM) data were used. The SRTM data were acquired from the Topodata [27] web platform, a Brazilian program that resampled the SRTM data to 30 meters resolution.

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*

#### **Figure 2.** *Research procedures flowchart.*


**Table 1.**

*Sentinel-2 images characteristics.*

The SRTM provided by the Topodata program comes in WGS1984 datum. For analytic and standardization purposes, all data were converted to SIRGAS2000 Universal Transverse Mercator (UTM) zone 21S datum.

### **2.5 NDVI calculation using google earth engine**

For this study, researchers deployed a Google Earth Engine script to calculate normalized difference vegetation index (NDVI) values for the periods of January 1th, 2018 to December 31th, 2018; January 1th, 2019 to December 31th, 2019; and January 1th, 2020 to December 31th, 2020, according to the sentinel 2 constellation revisit period of 5 days. Level 2 (L2) orbital remote sensing products from the Sentinel-2 satellite were used, the L2 algorithm generates ortho corrected, atmospherically corrected, and with bottom-of-atmosphere reflectance. The constellation of two satellites—Sentinel-2A and Sentinel-2B—orbit the Earth at an altitude of 786 km but are separated by 180° to optimize global coverage and revisit times.

The NDVI, proposed by Rouse et al. [28], is the most common ratio index for remote sensing environmental analysis. It allows us to identify areas with dense vegetation and areas with no vegetation. It is calculated using the near infrared (NIR) and red bands, applying the following equation:

$$\text{NDVI} = \frac{(\rho \text{NIR} - \rho \text{Red})}{(\rho \text{NIR} + \rho \text{Red})} \tag{1}$$

The NDVI presents values that vary from �1 to 1, where values below 0 are considered non-vegetated areas, such as bare soil, mud, or water, and as closer to 1 get, the healthier the vegetation.

This index presents high accuracy for comparisons in time and spatial scales, considered suitable for analyses of areas affected by floods. Flooded areas generally present the characteristics of water or mud presence. These surfaces present low reflectance patterns in the red and NIR spectrum, while vegetated areas present higher reflectance patterns [7].

#### **2.6 Statistical analysis**

The descriptive statistics were calculated and confidence interval was obtained using the Bootstrap method with 9999 repetitions. The Shapiro–Wilk test was used to verify normality, results (*p* < 0.001) show no adherence to normality. The Dwass-Steel-Crichlow-Fligner test, described by Dwass [29], Steel [30, 31], Douglas and Michael [32] for independent nonparametric samples, was used to test if there was a significant difference between years. The pairwise comparison was deployed using NDVI values for 2018, 2019, and 2020. The test assumed alpha equals 0.05.

### **3. Results and discussion**

#### **3.1 Hypsometry of the study area**

The Córrego do Feijão dam was located at the right top corner of **Figure 3**. As it can be noticed, the dam was located at a higher altitude than the Paraopeba river. This river provides drinking water for several municipalities along its course, among other types of usage such as industries, irrigation, and for livestock. The presented data are key for the study of tailing dam location, since it provides information for decisionmakers to prevent the installation of these dams near human settlements. The SRTM data can be used to model the impacts of dam breaks and the main areas that will be affected. This method can be used in the environmental licensing of tailing dams.

The digital elevation model (DEM) deployed using SRTM data is key for flow accumulation analysis [33]. Mubareka et al. [34] present a study, using SRTM data to predict settlement location and density at a 90 m resolution. The terrain representation in 3D allows decision-makers to tackle priority areas to focus efforts.

#### **3.2 Environmental impacts in Brumadinho, MG, Brazil**

In addition to the 363 confirmed deaths, the mud devastated homes, buildings, businesses, and families, leaving only the trail of destruction and uncertainties. Some of the families that lived there had small land where they developed subsistence

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*

**Figure 3.** *3D digital elevation model representation of the study area.*

agriculture as their main source of income, just as fishermen and rural communities lost all their material possessions.

Following the catastrophe, the National Human Rights Commission carried out a mission aimed at promoting qualified listening and proposing emergency actions for the affected populations. During the mission, the possible impacts resulting from the rupture of Córrego do Fundão tailings dam were raised, among them the mortality of specimens in all trophic chains; harming the conservation status of species already listed as endangered and the entry of new species into the list of threatened species, and undermining the structure and function of associated aquatic and terrestrial ecosystems [35].

**Figure 4** presents the archive image acquired 01/22/2019 from Sentinel MSI (left) and the crisis image from 02/29/2019 before the Córrego do Fundão tailing dam failure (right).

#### **3.3 Vegetation loss due to Córrego do Fundão tailings dam failure**

The Atlantic Forest biome prevailed in the area affected by the tailings Dam I failure. This biome holds up to 8% of the world's species and is recognized as a hotspot for biodiversity conservation. As a result of anthropic activities, in recent years, its occupied territory has plummeted to less than 15% [36], thus being a global conservation priority [37].

Although mining activity is predominant in the area, besides the Atlantic Forest parks protection, activities, such as agriculture and livestock, both for subsistence, were responsible for a small portion of land use and occupation [38]. Of the affected area, the major affected area corresponds to Atlantic Forest vegetation, and the rest are activities carried out by the population, such as housing and agriculture. It should be noted that food production, food security, and community health were strongly

#### **Figure 4.**

*Archive image from 22 to 01-2019 on the left panel and crisis image from 01 to 02-2019 on the right panel of the study area.*

and directly affected since natural resources such as soil, water, and ecosystem interactions were compromised.

With the Vale S/A tailings dam failure in Brumadinho (MG), 11.7 million cubic meters of high silica and iron sludge were dumped under an area of Atlantic Forest native vegetation.

**Figure 5** presents the area affected by the dam break, representing a vegetation loss of 2662 ha. The difference can be identified due to high reflectance values in the NIR band by mud-covered areas. Vegetated areas have high absorbance of the green spectrum. As seen in the NDVI Archive histogram, peaks of reflectance can be noticed in 0.8 μm, which shows a high quantity of green vegetation in the area, after the dam break, it can be noticed a significant decrease in peak values, now in 0.1 μm.

The NDVI is the most used remote sensing index for vegetation loss studies [39], for example, in the studies of Mariana environmental disaster and analysis of native Atlantic Forest loss [13]. We used Google Earth Engine to calculate the NDVI, using the affected area polygon, for years 2018, 2019, and 2020. It can be noticed, in **Figure 6**, lower values for NDVI in the crisis year.

The mean values, with lower and upper bootstrap confidence intervals, for NDVI for year 2017 were 0.314003 [0.31028; 0.317564], for NDVI in year 2018 values were 0.339887 [0.336591; 0.343231], for year 2019 the values were 0.145814 [0.144004; 0.1476], for year 2020 values were 0.1495 [0.147676; 0.15128], and for year 2021 the values were 0.15572 [0.153727; 0.15774]. The Dwass–Steel–Crichlow–Fligner test for nonparametric data presented a statistically significant (*p* < 0.001) decrease in vegetation when comparing 2018–2019 and 2020 values. The descriptive and inferential statistics are presented in **Tables 2** and **3**.

**Table 3** displays the inferential statistics for the NDVI values for the period between 2018 and 2021. The results clearly show that the vegetation did not recover from the disaster in January 2021, with significant difference between the years pre and post the disaster, with exception of the year 2018.

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*

**Figure 5.** *Vegetation dynamics and histograms of NDVI values for years 2017–2021.*

**Figure 6.** *NDVI values for years 2017–2021.*



**Table**

**2.**

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*


#### **Table 3.**

*Inferential statistics for NDVI values pairwise comparison to the period of 2018–2021.*

**Figure 7.**

*NDVI and precipitation (mm.Month*�*<sup>1</sup> ) for the period between 2017 and 2021.*

Silveira et al. [40] used Landsat 8 images of before and after the Mariana, MG disaster to detect vegetation loss in the affected area. Their conclusions were that the index produced "highly accurate maps of areas affected by post-dam-failure flooding in the region. This approach can be used in many other contexts for rapid and accurate assessment of such land-cover change." (**Figure 7**).

#### **3.4 Disaster management**

Disasters are events that escape to normality, involving large negative environmental, economic, and social impacts. Their environmental and socio-environmental consequences can be reversible or not [41]. Its origin can be natural or anthropogenic, it is currently considered that they are, in general, products of interrelation between human activities and natural phenomena [42–44] as an example of the disaster that occurred in the municipality of Brumadinho, MG in February 2019.

Disaster management presents itself in a cycle divided into mitigation, preparation, response, and recovery. Mitigation implements measures that can eliminate or reduce the degree of risks and hazards. Preparation is where actions are taken in

advance in order to develop effective mechanisms to respond to the event. It is followed by the response that is the actions to be implemented as soon as the event occurs minimizing the damage and the recovery phase, that is, the reestablishment of the area and its actions to reach normality [45–48].

In the last 10 years, according to the Université Catholique de Louvain's EM-DAT [49], 10 disasters were registered in Brazil between 2010 and 2019 including industrial, transportation, and assorted. Among them is the disaster in the municipality of Mariana (MG) with a total of 25 deaths and the Brumadinho disaster in early 2019 with 363 deaths. Furthermore, it must be accounted for the loss of Atlantic Forest native vegetation, water quality degradation, and livestock deaths as environmental developments to this catastrophe.

In response to these events, Brazil has a specific legislation for disasters, where the Federation, states, municipalities, and organized society have defined roles. The Law 12,608—2012 [50] establishes the responsibilities, goals, and directives of planning with the objective of reducing disasters. This law foresaw the zoning for land use and occupation, creation of a database for risk areas monitoring, prevention, response, and mitigation plans. Nevertheless, its efficiency was absent in disasters such as those

**Figure 8.** *Tailing dams risk categories according to the Brazilian National dam Security Plan.*

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*

mentioned above, it is exposed that there is a need for structuring and implementing disaster management plans.

#### **3.5 Environmental regulation regarding the tailings dam I**

The Tailings Dam I, broken in January 2019, belonged to the Córrego do Feijão Dam Complex, it was 87 meters high, medium size, and stored iron ore. According to the Brazilian National Dam Security Plan (PNSB), the dam was categorized as low risk and had high potential damage. This complex belongs to Vale, the largest Brazilian mining company. In this complex, there are still five small dams classified as low risk, as seen in **Figure 8**, with potential damage between medium and high, **Figure 9** [51]. The dam's potential damage is a function of the human life's potential loss and economic, social, and environmental impacts. In addition to these, the municipality of Brumadinho has another 20 small and medium-sized dams, between low and medium risk [52].

In 2017, the Environmental Impact Report (EIA/RIMA) was registered at the State Environmental Foundation of Minas Gerais.

The EIA/RIMA is a direct product of the Environmental Licensing process. It is a regulatory instrument in which the government, represented by environmental

**Figure 9.** *Tailing dams damage potential according to the Brazilian National dam Security Plan.*

agencies, authorizes and monitors the installation and operation of activities that appropriate from natural resources or that are considered effective or potentially polluting. It is mandatory for entrepreneurship, to seek environmental licensing from the competent agency, from the initial stages of its planning and installation until its effective operation.

Environmental impact study and the environmental impact report are included in this context as a regulatory requirement, established by the National Environmental Council (CONAMA) Resolution 001/86. It consists of in situ studies in soil, water, and air to verify if the prospective area contains environmental liabilities. Furthermore, present studies regarding the socio-economic-environmental relationship will be affected by the implementation of the enterprise.

The EIA/RIMA proposed licensing the operational continuity of the Córrego do Feijão dams until the year 2029. It was approved in November 2018 by the Superintendency of Priority Projects (SUPPRI) linked to the State Secretariat of Environment and Sustainable Development of the State of Minas Gerais. Palagi and Javernick-Will [53] states that a major constraint for policy-making and application is "institutional norms and cultural beliefs considerably narrowed the range of options post-disaster decision-makers perceived as viable, appropriate, or compassionate."

Vale has expressed in an official note regarding the risk management actions that had been implemented before and during the event, such as alarms, leakage routes, and maintenance and monitoring of dams. Regardless of these actions, it is not yet known exactly what triggered the rupture of the dam. As in the Mariana disaster that occurred in 2015, some hypotheses were raised about the disaster in Brumadinho. To solve any doubts regarding this and other possible dam breaks, not only of Vale but other mining companies, the Public Prosecutor's Office continues to follow the developments, as well as the documents issued where possible problems regarding the dam structure were confirmed.

As a consequence of this act, a stronger posture is expected by the government agencies. On the contrary, the rural and economic development-focused parties in the Brazilian senate propose the flexibilization of the environmental laws. For example, reducing the bureaucratic criteria for the environmental licensing, main regulation instrument in Brazil. In this sense, integrating technology and decision-making is key for disaster management.

### **4. Conclusions**

The vegetation in the area affected by the dam break in Brumadinho did not present a statistically significant recovery according to NDVI values. The total area affected by the dam brake was 2662 ha. The NDVI values presented a statistically significant decrease from 2017 to 2019, with little increase until 2021, NDVI values for year 2017 were 0.314003 [0.31028; 0.317564], for year 2018 the NDVI values were 0.339887 [0.336591; 0.343231], for year 2019 the NDVI values were 0.145814 [0.144004; 0.1476], for year 2020 the NDVI values were 0.1495 [0.147676; 0.15128], and for year 2021 the NDVI values were 0.15572 [0.153727; 0.15774]. The study shows that GIS technology is key for post disaster impacts assessment and monitoring of vegetation recovery and aiding strategic planning of decision-makers. GIS presents low cost and rapid response tools for disaster management, allowing it to focus efforts in priority areas. Furthermore, GIS must be integrated in disaster prevention policies, integrating environmental licensing development processes.

*Integrating Google Earth Engine and Decametric Sentinel 2 Images for Analysis of Vegetation... DOI: http://dx.doi.org/10.5772/intechopen.108286*

Regarding the information gathered during the study, it is concluded that although Brazil has regulatory instruments, they must be revised for efficient application.

Once the company responsible had employed the disaster management tools, such as GIS-based decision-making, with prevention actions, rapid response to events, and recovery, much of the damage would be minimized and even avoided. Still on disaster management, this is also government responsibility. Decisions must be community driven, where agile responses to events and community well-being are guaranteed, regardless of responsibility.

Approaching all environmental impacts, besides the loss of Atlantic Forest vegetation, directed studies on aquatic and terrestrial ecosystems are of great relevance to explain and elucidate all the damage caused, supporting future discussions about legislation of dam failures and other projects that endanger environmental health. These discussions must focus on prevention other than remediation.

### **Author details**

Rodrigo Martins Moreira<sup>1</sup> \* and Maria Paula Cardoso Yoshii<sup>2</sup>

1 Geomatics and Statistics Laboratory, Department of Environmental Engineering, Federal University of Rondönia, Ji-Paraná, Rondônia, Brazil

2 University of São Paulo, São Carlos, São Paulo, Brazil

\*Address all correspondence to: rodrigo.moreira@unir.br

© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
