Amazon deforestation: A dangerous future indicated by patterns and trajectories in a hotspot of forest destruction in Brazil.

In recent years, the loss of forest in the Brazilian Amazon has taken on alarming proportions, with 2021 recording the largest increase in 13 years, particularly in the Abunã-Madeira Sustainable Development Reserve (SDR). This has significant environmental, social, and economic repercussions globally and for the local communities reliant on the forest. Analyzing deforestation patterns and trends aids in comprehending the dynamics of occupation and deforestation within a critical Amazon region, enabling the inference of potential occupation pathways. This understanding is crucial for identifying deforestation expansion zones and shaping public policies to curb deforestation. Decisions by the Brazilian government regarding landscape management will have profound environmental implications. We conducted an analysis of deforestation patterns and trends up to 2021 in the municipality (county) of Lábrea, located in the southern portion of Amazonas state. Deforestation processes in this area are likely to spread to the adjacent "Trans-Purus" region in western Amazonas, where Amazonia's largest block of remaining rainforest is at risk from planned highways. Annual deforestation polygons from 2008 to 2021 were categorized based on occupation typologies linked to various actors and processes defined for the region (e.g., diffuse, linear, fishbone, geometric, multidirectional, and consolidated). These patterns were represented through 10 × 10 km grid cells. The findings revealed that Lábrea's territory is predominantly characterized by the diffuse pattern (initial occupation stage), mainly concentrated in protected areas. Advanced occupation patterns (multidirectional and consolidated) were the primary contributors to deforestation during this period. Observed change trajectories included consolidation (30.8%) and expansion (19.6%) in the southern portion of the municipality, particularly along the Boi and Jequitibá secondary roads, providing access to large illegal landholdings. Additionally, non-change trajectories (67%) featured initial occupation patterns near rivers and in protected areas, likely linked to riverine and extractive communities. Tailoring measures to control deforestation based on actor types and considering stages of occupation is crucial. The techniques developed in this study provide a comprehensive approach for Amazonia and other tropical regions.

Forest fires and deforestation in the central Amazon: Effects of landscape and climate on spatial and temporal dynamics.

Forest fires and deforestation are the main threats to the Amazon forest. Extreme drought events exacerbate the impact of forest fire in the Amazon, and these drought events are predicted to become more frequent due to climate change. Fire escapes into the forest from agriculture and pasture areas. We assessed the potential drivers of deforestation and forest fires in the central Brazilian Amazon and show that over a period of 31 years (1985-2015) forest fires occurred only in years of extreme drought induced by El Niño (1997, 2009 and 2015). The association of forest fires with strong El Niños shows the vulnerability of forest to climate change. The areas deforested were closely associated with navigable rivers: 62% of the total deforestation from 2000 to 2018 was located within the 2 km of rivers. There was a notable increase in deforestation and forest fire during the 2015 El Niño in comparison to previous years. Only a small part of the forest that burned was deforested in the years following the wildfires: 7% (1997), 3% (2009) and 1.5% (2015). Forest close to roads, rivers and established deforestation is susceptible to deforestation and fire since these areas are attractive for agriculture and pasture. Indigenous land was shown to be important in protecting the forest, while rural settlement projects attracted both forest fire and deforestation. Of the total area in settlement projects, 40% was affected by forest fires and 17% was deforested. Rivers are particularly important for deforestation in this part of Amazonia, and efforts to protect forest along the rivers are therefore necessary. The ability to predict where deforestation and fires are most likely to occur is important for designing policies for preventative actions.

Burning in southwestern Brazilian Amazonia, 2016-2019.

Fire is one of the most powerful modifiers of the Amazonian landscape and knowledge about its drivers is needed for planning control and suppression. A plethora of factors may play a role in the annual dynamics of fire frequency, spanning the biophysical, climatic, socioeconomic and institutional dimensions. To uncover the main forces currently at play, we investigated the area burned in both forested and deforested areas in the outstanding case of Brazil's state of Acre, in southwestern Amazonia. We mapped burn scars in already-deforested areas and intact forest based on satellite images from the Landsat series analyzed between 2016 and 2019. The mapped burnings in already-deforested areas totalled 550,251 ha. In addition, we mapped three forest fires totaling 34,084 ha. Fire and deforestation were highly correlated, and the latter occurred mainly in federal government lands, with protected areas showing unprecedented forest fire levels in 2019. These results indicate that Acre state is under increased fire risk even during average rainfall years. The record fires of 2019 may continue if Brazil's ongoing softening of environmental regulations and enforcement is maintained. Acre and other Amazonian states must act quickly to avoid an upsurge of social and economic losses in the coming years.

Deforestation dynamics in Brazil's Amazonian settlements: Effects of land-tenure concentration.

Brazil's Amazon deforestation is a major global and national environmental concern, and the ability to model and project both its course and the effect of different policy options depends on understanding how this process occurs at present and how it might change in the future. The present paper addresses one key factor in Amazon deforestation: land-tenure concentration in settlements. Brazil's policies for establishing and regulating settlement projects represent critical government decisions shaping the landscape in the 5 × 106 km2 Legal Amazonia region. We used remote-sensing data and information provided by the National Institute for Colonization and Agrarian Reform (INCRA) to evaluate the effect of land-tenure concentration in a settlement project (Projeto de Assentamento) located in a frontier area where cattle-ranching is expanding. We identified the actors and their deforestation patterns in the Matupi settlement in the southern part of Brazil's state of Amazonas. We spatially identified actors who concentrated "lots" (the parcels of land distributed to individual settlers) in 2011 and assessed whether the concentration was done by individual landholders or by "families" (where members merged their lots and the clearing was done together). Deforestation rates (1995-2011) were estimated for each type of actor and the trajectory of deforestation in the settlement (cumulative deforestation to 1994 and annual deforestation 1995-2016) was also analyzed. Concentrators occupied 28% (9653 ha) of the settlement and 29% of the lots (152 lots) analyzed; the numbers of lots concentrated ranged from two to ten. Concentrators of two lots and non-concentrators were the predominant actor types in the settlement. The mean annual clearing per landholding for concentrators of two lots (families: 4.1 ± 2.8 ha (mean ± SD); individuals: 5.1 ± 4.6 ha) was greater than for non-concentrators (1.7 ± 1.2 ha), despite their having similar patterns of small clearings. Concentrators of three or more lots had mean annual clearing per landholding between 6.2 ± 12.2 ha and 23.9 ± 38.7 ha and, the pattern of patches cleared per year >34 ha in area was predominant. The deforestation rate per lot was higher among concentrators as compared to non-concentrators, showing that lot concentration speeds deforestation. Analysis of deforestation patterns helps to better understand the process of lot concentration by spatially identifying the predominant patterns of each type of actor. The approach used in our study could assist authorities in identifying and monitoring land-tenure concentration in settlements. Agrarian-reform policymakers need to monitor this process, since it speeds deforestation in Amazonian settlement projects, as well as undermining the social objectives of the agrarian-reform program.

Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon.

Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty.

Multi-temporal analysis of radiometric changes in satellite images of forest edges to infer selective-logging areas in the Amazon forest / Análise multitemporal das mudanças radiométricas em imagens de satélite de bordas de floresta para inferir áreas de corte seletivo na floresta Amazônica

Radiometric changes observed in multi-temporal optical satellite images have an important role in efforts to characterize selective-logging areas. The aim of this study was to analyze the multi-temporal behavior of spectral-mixture responses in satellite images in simulated selective-logging areas in the Amazon forest, considering red/near-infrared spectral relationships. Forest edges were used to infer the selective-logging infrastructure using differently oriented edges in the transition between forest and deforested areas in satellite images. TM/Landsat-5 images acquired at three dates with different solar-illumination geometries were used in this analysis. The method assumed that the radiometric responses between forest with selective-logging effects and forest edges in contact with recent clear-cuts are related. The spatial frequency attributes of red/near infrared bands for edge areas were analyzed. Analysis of dispersion diagrams showed two groups of pixels that represent selective-logging areas. The attributes for size and radiometric distance representing these two groups were related to solar-elevation angle. The results suggest that detection of timber exploitation areas is limited because of the complexity of the selective-logging radiometric response. Thus, the accuracy of detecting selective logging can be influenced by the solar-elevation angle at the time of image acquisition. We conclude that images with lower solar-elevation angles are less reliable for delineation of selecting logging.

Mudanças radiométricas observadas em imagens multitemporais de satélites ópticos têm importante papel na caracterização de áreas afetadas pelo corte seletivo. O objetivo desse estudo foi analisar o comportamento multitemporal da resposta da mistura espectral em imagens de satélite de áreas simuladas por corte seletivo de madeira na floresta amazônica, considerando as relações espectrais do vermelho/infravermelho próximo. Para a análise dos dados foram utilizadas as margens da floresta para inferir a infraestrutura de exploração florestal em diferentes orientações a partir da transição entre áreas de floresta e desmatamento em imagens de satélite. Imagens do sensor TM/Landsat-5 adquiridas em três datas distintas com diferentes geometrias de iluminação solar foram utilizadas na análise. O método utilizado presume que as respostas radiométricas entre a floresta com efeitos do corte seletivo e as margens da floresta em contato com áreas recém-desmatadas estão relacionadas. Os atributos da frequência espacial das bandas espectrais do vermelho/infravermelho próximo foram analisados. A análise dos diagramas de dispersão mostraram dois grupos de pixels que representam áreas de corte seletivo. Os atributos de tamanho e distância radiométrica representando esses dois grupos foram relacionados com o ângulo de elevação solar. Os resultados sugerem que a detecção de áreas exploradas é limitada devido à complexidade da resposta radiométrica do corte seletivo. Assim, a acurácia em detectar o corte seletivo pode ser influenciada pelo ângulo de elevação solar durante a aquisição das imagens. Nós concluímos que imagens com ângulos de elevação solar baixos são menos confiáveis para a delimitação do corte seletivo.

Simulating deforestation and carbon loss in Amazonia: impacts in Brazil's Roraima state from reconstructing Highway BR-319 (Manaus-Porto Velho).

Reconstruction of Highway BR-319 (Manaus-Porto Velho) would allow for access from the "arc of deforestation" in the southern part of Brazil's Amazon region to vast blocks of forests in central and northern Amazonia. Building roads is known to be a major driver of deforestation, allowing entry of squatters, and other actors. Rather than deforestation along the highway route, here we consider the road's potential for stimulating deforestation in a separate location, approximately 550 km north of BR-319's endpoint in Manaus. Reconstructing BR-319 has great potential impact to start a new wave of migration to this remote region. The southern portion of the state of Roraima, the focus of our study, is already connected to Manaus by Highway BR-174. We modeled deforestation in southern Roraima and simulated carbon emissions between 2007 and 2030 under four scenarios. Simulations used the AGROECO model in DINAMICA-EGO © software. Two scenarios were considered with reconstruction of BR-319 and two without this road connection. For each of the two possibilities regarding BR-319, simulations were developed for (1) a "conservation" (CONSERV) scenario that assumes the creation of a series of protected areas, and (2) a "business-as-usual" (BAU) scenario that assumes no additional protected areas. Results show that by 2030, with BR-319 rebuilt, deforestation carbon emissions would increase between 19% (CONSERV) and 42% (BAU) over and above those corresponding to no-road scenarios.

Caracterização de uso e cobertura da terra na Amazônia utilizando imagens duais multitemporais do COSMO-SkyMed / Land use/land cover characterization in Amazonia using COSMO-SkyMed multitemporal images

The use of radar imagery is an alternative source of information to support the monitoring of the Amazon region, since the optical images have imaging limitations in tropical areas due to the occurrence of clouds. Therefore, the goal of this study is to analyze the radar images in X-band multi-temporal polarized obtained by COSMO-SkyMed satellite (COnstellation of small Satellites for Mediterranean basin Observation), in the intensity mode, isolated and/or combined with textural information, to thematic characterization of land use/land cover in the Humaitل, Amazonas State region. The methodology used includes: analysis of the dual images obtained during two subsequent acquisitions, in order to explore the potential of the dataset as a quad-pol intensity; extraction of textural attributes from the co-occurrence matrix (Gray Level Co-occurrence Matrix) and subsequent contextual classification; statistical assessment of the thematic performance of the intensity and textural images, isolated and in polarized groups. Within the results achieved, the group formed only by the intensity images presented a better performance if compared to those containing the textural attributes. In this discrimination, the classes involved were forest, alluvial forest, reforestation, savannah, pasture and burned areas, yielding 66% overall accuracy and a Kappa value of 0.55. The results showed that X band images, from COSMO-SkyMed, StripMap mode (Ping-Pong), multi-polarized, presents a moderate potential to characterize and monitor the dynamics of land use/land land cover in the Brazilian Amazon.

A utilização de imagens de radar é fonte alternativa de informações para subsidiar o monitoramento da região amazônica, visto que as imagens ópticas têm limitações de imageamento em zonas tropicais face a ocorrência de nuvens. Por conseguinte este trabalho teve como objetivo analisar a capacidade das imagens-radar de banda X multitemporais e polarizadas obtidas pelo satélite COSMO-SkyMed (COnstellation of small Satellites for Mediterranean basin Observation), no modo intensidade, isoladamente e agregados às informações texturais, na caracterização temática de uso e cobertura da terra no município de Humaitá/AM. A metodologia empregada consistiu da: análise das imagens duais obtidas em duas aquisições subsequentes, de forma a explorar a potencialidade do conjunto de dados na forma quad-pol intensidade; extração dos atributos texturais a partir da matriz de coocorrência (Gray Level Co-occurrence Matrix) e posterior classificação contextual; avaliação estatística de desempenho temático das imagens intensidade e texturais, isoladas e em grupos polarizados. Dentre os vários resultados alcançados, foi verificado que o grupo formado somente pelas imagens intensidade apresentou o melhor desempenho, comparado àqueles contendo os atributos texturais. Nesta separabilidade, estavam envolvidas as classes de floresta, floresta aluvial, reflorestamento, savana, pasto e queimada, obtendo-se 66% de acurácia total e valor Kappa de 0,55. Os resultados mostraram que as imagens de banda X do COSMO-SkyMed, modo StripMap (Ping-Pong), multipolarizadas, têm potencial moderado para a caracterização e monitoramento da dinâmica de uso e cobertura da terra na Amazônia brasileira.