Comparison of classification algorithms of images for the mapping of the land covering in Tasso Fragoso municipality, Brazil.

Abstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate the areas, with rates of mistakes varying between 25 and 75%, being the main, the confusion with pastoral areas

Fire Occurrences and Greenhouse Gas Emissions from Deforestation in the Brazilian Amazon.

Abstract: This work presents the dynamics of fire occurrences, greenhouse gas (GHG) emissions, forest clearing, and degradation in the Brazilian Amazon during the period 2006?2019, which includes the approval of the new Brazilian Forest Code in 2012. The study was carried out in the Brazilian Amazon, Pará State, and the municipality of Novo Progresso (Pará State). The analysis was based on deforestation and fire hotspot datasets issued by the Brazilian Institute for Space Research (INPE), which is produced based on optical and thermal sensors onboard different satellites. Deforestation data was also used to assess GHG emissions from the slash-and-burn practices. The work showed a good correlation between the occurrence of fires in the newly deforested area in the municipality of Novo Progresso and the slash-and-burn practices. The same trend was observed in the Pará State, suggesting a common practice along the deforestation arch. The study indicated positive coefficients of determination of 0.72 and 0.66 between deforestation and fire occurrences for the municipality of Novo Progresso and Pará State, respectively. The increased number of fire occurrences in the primary forest suggests possible ecosystem degradation. Deforestation reported for 2019 surpassed 10,000 km2, which is 48% higher than the previous ten years, with an average of 6760 km2. The steady increase of deforestation in the Brazilian Amazon after 2012 has been a worldwide concern because of the forest loss itself as well as the massive GHG emitted in the Brazilian Amazon. We estimated 295 million tons of net CO2, which is equivalent to 16.4% of the combined emissions of CO2 and CH4 emitted by Brazil in 2019. The correlation of deforestation and fire occurrences reported from satellite images confirmed the slash-and-burn practice and the secondary effect of deforestation, i.e., degradation of primary forest surrounding the deforested areas. Hotspots? location was deemed to be an important tool to verify forest degradation. The incidence of hotspots in forest area is from 5% to 20% of newly slashed-and-burned areas, which confirms the strong impact of deforestation on ecosystem degradation due to fire occurrences over the Brazilian Amazon

ESTIMATES OF FOREST CHARACTERISTICS DERIVED FROM REMOTELY SENSED IMAGERY AND FIELD SAMPLES: APPLICABLE SCALES, APPROPRIATE STUDY DESIGN, AND RELEVANCE TO FOREST MANAGEMENT

Information and knowledge about a given forested landscape drives forest management decisions. Within forest management though, information that adequately describes various characteristics of the forested environment in the spatial detail desired to make fully informed management decisions is often limited. Key metrics such as species composition, tree basal area, and tree density are typically too expensive to collect using ground-based inventory methods alone across broad extents for forest level planning (thousands of ha) at fine spatial detail that permit use at tactical spatial scales (tens of ha). However, quantifying these metrics accurately, in spatial detail, across broad landscapes is important to inform the management process. While relating remotely sensed data to classical ground-based survey data through modeling has shown promise for describing landscapes at the spatial detail need to inform planning and tactical scale projects, questions remain related to integrating both sources of data, sample design, and linking plots to remotely sensed data. This dissertation addresses critical aspects of these questions by: quantifying and mitigating the impact of co-registration errors; comparing various sample designs and estimation techniques using simulated ground-based information, remotely sensed data, and a variety of modeling techniques; developing enhanced image normalization routines; and creating an ensemble approach to estimating various forest characteristics that describe species composition, basal area, and tree density. This dissertation address knowledge gaps in the fields of forestry, remote sensing, data science, and decision science that can be used to efficiently and effectively inform the natural resource management decision-making process at fine spatial resolutions across broad extents