**1. Introduction**

In recent decades, there has been an increase in concern about environmental disasters. This concern fosters the need for emergency management to mitigate socioeconomic consequences [1]. Disaster management is defined as the field of science that develops and applies technologies, planning, and management to deal with extreme events. Whether of natural or anthropic origin, events are managed that can kill or injure people and animals, as well as cause extensive damage to properties and communities [2].

Occurred in 25-01-2019, the catastrophe of Brumadinho released 43 million m<sup>3</sup> of iron ore tailings enters the list of the biggest mining disasters of history, being

classified by number of deaths: Bulgaria, 1966, lead-zinc tailings (488 deaths); Brazil (Brumadinho), 2019, iron ore tailings (363), Chile, 1965, copper tailings (300) and China, 2008, iron tailings (277) [3].

Several challenges arise when dealing with disasters, the main ones being the need to analyze large spatial extensions in a short time [4]. Both in the drafting of evacuation routes, or in the analysis of potential risks, managers deal with aspects related to space. Still, in the context of developing countries, they face challenges related to a lack of financial resources and trained analysts [5]. In this context, geographic information systems (GIS) and remote sensing translate as tools to support geographic analysis, through storage, processing, and access to spatialized information [6]. Several studies assessing the social, economic, and ecologic impacts over flood areas using GIS and orbital remotely sensed data have been deployed [7–9].

In this context, the normalized difference vegetation Index (NDVI) is a ratio index calculated using the difference between near-infrared reflectance and red reflectance to their sum, being widely used to assess vegetation dynamics before and after disasters [10–12]. Rotta et al. [13] assessed pre-disaster scenarios and the causes to the dam collapse using satellite-driven soil moisture index, multispectral high-resolution imagery, and Interferometric Synthetic Aperture Radar (InSAR) products to assess pre-disaster scenarios and the direct causes of the tailings dam collapse. Cheng et al. [14] used Landsat 8 operational land imager products to assess sediment concentration and found an increase of sediments in the Paraopeba River due to the mudflow. Gama et al. [15] using Sentinel 1 InSAR data were able to observe persistent trends of deformation on the crest, middle and bottom sectors of the dam, which may have caused the collapse.

Nonetheless, to the researchers' knowledge, there is a gap regarding the assessment of vegetation recovery for the impacted area for subsequent years and compared to the previous dynamic. This is due to the large quantity of images that would require a large amount of time and processing capacity. For example, for an entire year, there would be 73 Sentinel 2 images to process. In this chapter, we assessed the NDVI reflectance for 2018, 2019, and 2020, a total of 217 images with a spatial resolution of 10 meters. To tackle this problem, we used the Google Earth Engine cloud computing environment.

Therefore, the research question that led the discussion is "how has the vegetation recovered after two years of the disaster?" Therefore, the aim of this work is to assess vegetation dynamics by analyzing the reflectance values for the NDVI index for 2018, 2019, and 2020.

#### **1.1 Remote sensing applied to analyzes of environmental impacts of disasters**

In a post-disaster crisis situation, environmental impact managers and analysts need accurate information in the short term. The information needs to show the spatial and temporal scale of what has happened and what can happen. Based on this information, the necessary resources will be allocated to leverage immediate responses to contain the damage. Access to spatialized information allows the preparation of plans to anticipate contingencies; evaluation of possible scenarios; efficiently and effectively; and actions for recovery and reconstruction of damages [16].

Thus, GIS and remote sensing translate into a key tools in impact analysis and disaster management. This technology has the capacity to aggregate socioeconomic information; images of remote sensing; storage, manipulation, queries, and data analysis; and, more importantly, the visualization of the data [17].

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

The use of GIS for disaster management can support several aspects of decisionmaking, such as:


### **2. Materials and methods**

The disaster occurred in the Brumadinho Municipality, at Minas Gerais State, Brazil. The Córrego do Feijão Dam, the Paraopeba river, and the affected area by the dam break are presented in **Figure 1**. The impacted area was obtained by the researchers by setting a threshold of values less than 0, converting it in a Boolean image, converting it to a vectoral file, and by overlaying selecting the polygon that matched the crisis image.

#### **2.1 Socioeconomic and environmental description**

With an estimated population of 39,520 [25], a Municipal Human Development Index of 0.747 and GDP per capita of more than R \$ 40 thousand, the municipality of Brumadinho belongs to the Metropolitan Region of Belo Horizonte and is inserted in the Quadrilátero Ferrífero where the main economic activity is iron mining. It is crossed by the Paraopeba River and a member of the Aguas Claras and Rio Manso River Basin, a source of supply for about 28% of the population.

The municipality has as a predominant biome the Atlantic Forest and some remnants of Cerrado, protected by the Special Protection Area (APE) of the Rio Manso; Environmental Protection Area (APA) Sul—RMBH; APA Inhotim; APE Catarina; Serra do Rola-Moça State Park, Serra da Moeda Natural Monument, Serra do Rola-Moça Natural Monument and Private Reserves of Natural Heritage—RPPN. About its climate, Brumadinho is located in the zone of influence of the Climate Cwa—Tropical Altitude, with summer rains, hot summers, and dry winters.
