Actual value and historical data chart for Brazil Forest Area Percent Of Land Area

Brazil: Forest area, percent of total land area: The latest value from 2023 is 59 percent, a decline from 59.1 percent in 2022. In comparison, the world average is 31.6 percent, based on data from 194 countries. Historically, the average for Brazil from 1990 to 2023 is 63.6 percent. The minimum value, 59 percent, was reached in 2023 while the maximum of 70.5 percent was recorded in 1990.

In 2022, the public forest area in Brazil amounted to 327 million hectares. This represented an increase of some 5.8 percent when compared to 2020 and the highest figure recorded during the period in consideration. The "Cadastro Nacional de Florestas Públicas" (CNFP) was created in 2006 as a forest management and planning instrument, to gather geo-referenced data on federal, state and municipal public forests. These forests are later categorized into different types according to the use, such as conservation, indigenous land, public rural settlements, military areas, and others.

Brazil's total forest area has been significantly decreasing since the turn of the century. In 2000, the country had around *** million square kilometers of land occupied by forests, whereas in 2016, the last year informed by the source, *** million square kilometers of forest land were registered. This represents a decrease of more than five percent when compared to 2000.

The state of Amazonas had by far the most public forest area in Brazil in 2022. The state's public forest area accounted for more than 40 percent of Brazil's public forest area that year. The state of Pará followed with nearly 79 million hectares, which represents some 24 percent of Brazil's public forest area. Both states are in the Amazon biome. By comparison, Sergipe was the state with the least public forest area in 2022, with around 47,200 hectares. The "Cadastro Nacional de Florestas Públicas" (CNFP) was created in 2006 as a forest management and planning instrument, to gather georeferenced data on federal, state and municipal public forests. These forests are later categorized into different types according to the use, such as conservation, indigenous land, public rural settlements, military areas, and others.

Russia had the largest forest area in the world in 2023, amounting to around 815 million hectares, more than twice that of Canada, whose forest area amounted to 346 million hectares. The forestry industry in Canada With the third largest forest area in the world, Canada’s forestry industry is a significant contributor to the country’s gross domestic product. In 2023, the nominal GDP of Canada’s forest industry reached more than 27 billion Canadian dollars, with the wood product manufacturing sector alone contributing around 13.3 billion Canadian dollars in nominal GDP. A comparison of Canadian provinces shows that British Colombia has the largest forestry and logging industry in the country, followed by Quebec and Ontario. The Amazon rainforest in Brazil Brazil has the second largest forest area in the world after Russia, with total forest areas in the South American country amounting to approximately 494 million hectares in 2022. This is largely because around 62 percent of the Amazon rainforest is located in Brazil. The Amazon rainforest is the world’s largest rainforest, what some call “the lungs of the planet”. However, in recent years, deforestation has been a salient issue in the Amazon, with illegal logging and wildfires raging across the rainforest have contributed to very high deforestation rates. Indeed, around 8,000 square kilometers were destroyed in the Brazilian Amazon in 2023. Deforestation and its impact on climate change has spurred opposition to the logging industry, which was the sector responsible for the most killings of environmental activists in 2021.

The Amazon biome has by far the largest public forest area in Brazil. In 2022, the state accounted for about 87 percent of Brazil's public forest area. By comparison, the Pampa biome, in south Brazil, had the smallest forest area, with some 406,000 hectares that year. The "Cadastro Nacional de Florestas Públicas" (CNFP) was created in 2006 as a forest management and planning instrument, to gather georeferenced data on federal, state, and municipal public forests. These forests are categorized into different types according to their use, such as conservation, indigenous land, public rural settlements, military areas, and others.

This data set reports the results of soil and vegetation surveys at four distinct areas within the Tapajos National Forest (TNF), 50 to 100 km south of Santarem, Para, Brazil, in November 1999. At 13 individual sites across the four areas, all located in primary forest, core soil samples at 10, 30 and 50 cm depths were collected and analyzed for dry mass, bulk density, texture, percentage carbon (C), percentage organic matter, and percentage nitrogen (N). At these 13 sites, vegetation was characterized for 250 m long by 10 m wide transects. Biomass was estimated for all stems over 10 cm DBH from allometric relationships for species, measured height, canopy dimension, and diameter. LAI was measured along the transect at 26 points with a LICOR LAI-2000. Canopy foliage samples were collected with a shotgun at dawn and leaf water potential was determined with a pressure chamber. Samples of foliage, wood, bark, fine roots, and litter were analyzed for %N, % C, delta 13C, and delta 15N. There are five comma-delimited ASCII data files with this data set.

Land use and land cover change models and scenarios are essential to understand the interconnections between global and regional factors influencing land use and demand changes, especially if we consider population growth and food demand projections in 2050.

Understanding the future of changes in land use and land cover in Brazil is fundamental for the future of global climate and biodiversity, given the richness of its five biomes. Thus, the new spatially explicit regional scenarios were developed for Brazil by 2050. Those scenarios are aligned with the Shared Socio-Economic Pathways (SSPs) and Representative Concentration Pathway (RCPs). Aim to detail global models regionally and can be used both regionally to support decision-making and enrich the overall analysis.

For the development of these new scenarios, the LuccME spatially explicit land change allocation modeling framework and the INLAND surface model were combined to incorporate climatic variables in water deficit and biophysical, socioeconomic, and institutional factors for Brazil. The scenarios were developed for land use and land cover classes: forest vegetation, grassland vegetation, planted pasture, agriculture, mosaic of occupations, and forestry.

The dataset comes in NetCDF format and includes the following products:

LUCCMEBR_land_cover_type_100km2_2000.nc: Percentage of land use and land cover for the year 2000 (Observed data).

LUCCMEBR_land_cover_type_100km2_2010.nc: Percentage of land use and land cover for the year 2010 (Observed data).

LUCCMEBR_land_cover_type_100km2_2012.nc: Percentage of land use and land cover for the year 2012 (Observed data).

LUCCMEBR_land_cover_type_100km2_2014.nc: Percentage of land use and land cover for the year 2014 (Observed data).

LUCCMEBR_SSP1_RCP19_land_cover_type_100km2_2015_2050.nc: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP1 and RCP1.9.

LUCCMEBR_SSP2_RCP45_land_cover_type_100km2_2015_2050.nc: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP2 and RCP4.5.

LUCCMEBR_SSP3_RCP70_land_cover_type_100km2_2015_2050.nc: Percentage of land use and land cover for the period 2015-2050 (Simulated data). This scenario considers the combination of SSP3 and RCP7.0.

Data

Percentage of land use and land cover classes: Forest vegetation (veg), Grassland vegetation (gveg), Planted pasture (pastp), Agriculture (agric), Mosaic of occupation (mosc), Forestry (fores) and Others (others).

Spatial resolution

The scenarios are available in a spatial resolution of 0.083º x 0.083º (~100 km²) and cover the entire Brazilian territory.

Temporal resolution

Period of observed data: 2000, 2010, 2012 e 2014

Scenario Period: 2015 – 2050 (each five-year)

Coordinate reference system

Geographic Coordinate System with Datum WGS84 (EPSG4326)

Data format

Data is provided as NetCDF.

Dataset usage

It is free to use, but please make sure to cite the repository and our paper properly if you use this dataset.

Publication & further information

For additional scenario information, please contact Francisco Gilney Silva Bezerra (franciscogilney@gmail.com).

Acknowledgments

The authors thank the project “MSA / BNDES (Environmental Monitoring by Satellite in the Amazon biome)” for financing the development of LuccMEBR.

Brésil: Forest area, percent of total land area: Pour cet indicateur, FAO fournit des données pour la Brésil de 1990 à 2023. La valeur moyenne pour Brésil pendant cette période était de 63.6 pour cent avec un minimum de 59 pour cent en 2023 et un maximum de 70.5 pour cent en 1990.

a Refers to the difference in mean annual rate of forest loss before (t ≤ yr–1) and after (t ≥ yr+1) the onset of agrarian settlements (see text). Age of settlement was therefore omitted from these models.b Most parsimonious model selected based on multiple comparisons of AIC values.Significance levels:† < 0.10,* < 0.05,** < 0.01,*** < 0.001.c Omitted from Δ Deforestation models because our before-and-after deforestation rate already considers the settlement time trajectory;d Omitted from vegetation conversion rate models due to data unavailability for all but the 300 settlement polygons for which Δ Deforestation models were performed.GLM model results (slope coefficients and associated ± SE) of predictors of cumulative vegetation (forest and cerrado) conversion rate as of 2011 within agrarian settlement areas across the Brazilian Legal Amazon region (N = 1,911); and mean difference in annual deforestation rate before (until year–1) and after (since year+1) the creation of settlements (N = 300).

Explanatory variables used in deforestation and land-use conversion models in this study.

Tropical forests are diminishing in extent due primarily to the rapid expansion of agriculture, but the future magnitude and geographical distribution of future tropical deforestation is uncertain. Here, we introduce a dynamic and spatially-explicit model of deforestation that predicts the potential magnitude and spatial pattern of Amazon deforestation. Our model differs from previous models in three ways: (1) it is probabilistic and quantifies uncertainty around predictions and parameters; (2) the overall deforestation rate emerges “bottom up”, as the sum of local-scale deforestation driven by local processes; and (3) deforestation is contagious, such that local deforestation rate increases through time if adjacent locations are deforested. For the scenarios evaluated–pre- and post-PPCDAM (“Plano de Ação para Proteção e Controle do Desmatamento na Amazônia”)–the parameter estimates confirmed that forests near roads and already deforested areas are significantly more likely to be deforested in the near future and less likely in protected areas. Validation tests showed that our model correctly predicted the magnitude and spatial pattern of deforestation that accumulates over time, but that there is very high uncertainty surrounding the exact sequence in which pixels are deforested. The model predicts that under pre-PPCDAM (assuming no change in parameter values due to, for example, changes in government policy), annual deforestation rates would halve between 2050 compared to 2002, although this partly reflects reliance on a static map of the road network. Consistent with other models, under the pre-PPCDAM scenario, states in the south and east of the Brazilian Amazon have a high predicted probability of losing nearly all forest outside of protected areas by 2050. This pattern is less strong in the post-PPCDAM scenario. Contagious spread along roads and through areas lacking formal protection could allow deforestation to reach the core, which is currently experiencing low deforestation rates due to its isolation.

Forest Area, Number of Dogs, Reserve size and Presence of both Top Predators were also used to model the process variance in abundance estimates of ocelot populations in six Atlantic Forest reserves in southeastern Brazil.

In 2023, the deforested area in the Legal Amazon in Brazil amounted to approximately 802,300 hectares. Just a year earlier, the Amazon deforested area surpassed 1.2 million hectares. What is behind the growing Amazon deforestation in Brazil? Illegal logging, expansion of agricultural areas for soybean cultivation, and an increase in wildfire outbreaks are all among the leading causes of deforestation in the Brazilian Amazon. Politics, however, has also played an important role. For example, the authorized budget for Brazil’s Ministry of the Environment has been on a mostly downward trend since 2013, when it reached a decade-long peak of nearly seven billion Brazilian reals. How big is the Brazilian deforestation issue? In 2023, Brazil registered by far the largest area of primary forest loss in the world, amounting to more than one million hectares. This was roughly the same area as the remaining top nine countries combined. As the country with the second-largest forest area worldwide, these developments are cause for concern amidst the conversation on climate change mitigation. With the global tree cover loss annually increasing, and the emission of greenhouse gases from forest areas along with it, reaching net-zero emissions targets by 2050 grows ever more challenging.

Abstract: Among amphibian species from high elevation areas in the Brazilian Atlantic Forest there is a high percentage of threatened and endemic species, but there is still a relative scarcity of local inventories for these organisms. Here, we present data on anuran composition, relative abundance and estimated densities for leaf-litter frogs from an Atlantic Forest area within the APA Serra da Mantiqueira, in Rio de Janeiro state, Brazil, based on results of a short-term survey carried out at altitudes of 1,350-1,750 m, in November 2005 (with additional records from surveys made in 2010 and 2011). Three sampling methods were used during the 2005 survey: plot sampling, visual encounter surveys (VES; performed during the day, at the dusk, and at night), and pitfall traps with drift fences; only non-standardized visual searches were employed during the 2010 and 2011 surveys. We recorded 24 species, with the direct-developer Ischnocnema sp. (gr. lactea) being the most abundant. Most anurans (90% of all individuals) sampled by VES were captured during the crepuscular and nocturnal periods. The estimated density of the local leaf-litter frog assemblage based on plot sampling was 18.4 ind/100 m2, which is one of the highest values currently reported for Atlantic Rainforest areas. This is the first study analyzing the anuran fauna composition of an Atlantic Forest area within the APA Serra da Mantiqueira and adds to the body of knowledge on the fauna of the southern region of Rio de Janeiro state.

Summary of land cover, fire incidence and rural population size for the entire Legal Amazon administrative region, distinguishing areas within and outside INCRA agrarian reform settlements.

ABSTRACT The uncontrolled expansion of human activities may lead to a reduction in vegetation cover, an increase in erosion processes and soil sealing. The aim of this study is to examine forest cover using the Weights of evidence method based on land use maps between the years of 1996 and 2011 in the micro-region of the Campanha Ocidental located in the state of Rio Grande do Sul (Brazil). The spatial database was constructed in SPRING software (version 5.2.1) based on LANDSAT 5 images, which were georeferenced and classified. Geophysical and socioeconomic variables were included in the database for further analysis in Dinamica EGO software (version 2.2.8). In order to parameterize the probabilistic model for the analysis of the dynamics of forest cover change, we calculated the percentage of class change through transition matrices; calculated the intervals for discretization of continuous variables; calculated the weights of evidence (W+); analyzed the correlation between the weights of the input variables for all transitions; simulated future scenarios and; validated the simulated final map based on the historical map. This model was adequate for understanding the variables that most contribute to forest cover change in the region. The results showed that the emergence of new forest areas was influenced by hypsometry, distance to sandy lands and per capita income, while deforestation by rural population and distance to the sandy lands.

Data and code from the paper:

Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264

Link: https://doi.org/10.1002/rse2.264

This repository contains:

1) model_train.R: This is the code to run the U-Net model in R language.

2) input.rar: Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.

3) unet_weights_best.h5: These are the best weights for the U-Net architecture achieved in the paper.

4) palm_stats.RData: Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.

If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).

If you use these data, please cite the paper:

Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264

Biodiversity losses have increased in tropical forests due to fire-related disturbances. As landscape fragmentation and climate change increase, fires will become more frequent and widespread across tropical rain forests worldwide, with important implications for forest dynamics by altering plant-animal interactions. Here we tested the hypothesis that recurrent fires in tropical rain forests change bottom-up and top-down forces controlling the abundance of insect herbivores, which in turn increases herbivory. To quantify herbivory, we collected 50 leaves per tree of five species in burned and unburned experimental plots (N = 75) in southeastern Amazonian forests. We measured leaf nitrogen content and leaf thickness of tree leaves as bottom-up factors that could explain differences in herbivory; we measured predation pressure on model caterpillars and estimated the abundance of predatory ants as top-down factors. We found higher herbivory in burned than in unburned forests, as well as lower predator attacks in caterpillar models and lower abundance of predatory ants. Leaf nitrogen content did not vary across treatments. Birds attacked model caterpillars more frequently in burned than in unburned forests, and leaf thickness was higher in burned forests, but these factors together were not enough to offset the higher herbivory in burned plots. Fire degrades tropical forests not only by killing trees and altering their structure and community dynamics, but also by reducing predatory arthropods and disrupting predator-prey interactions, which triggers increased herbivory. These indirect impacts of recurrent fires probably contribute to further alter forest structure, functioning, and to decrease forest regeneration in Amazonian forests. Methods Information from our manuscript: Study site The study was established at “Fazenda Tanguro”, in the state of Mato Grosso, Brazil, in the southern portion of the Amazon basin (13º 04’ S, 52º 23’ W). The climate is tropical humid, and the transitional forest between the Cerrado (Brazilian savannas) and the southeast Amazon forest composes the vegetation of our experimental plots. Expanding agriculture threatens the region (Brando et al. 2013; Marques et al. 2020), ultimately altering regional climate and increasing fire occurrence (Brando et al. 2013, 2020). Experimental fires were set in three large and adjacent 50-ha (500 × 1000 m) forest plots edging a soybean field. One plot was burned annually from 2004 to 2010, except in 2008 (Burn 1yr); a second one was burned triennially, in 2004, 2007 and 2010 (Burn 3yr); and a third plot was not burned (Control). Burnings were conducted at the end of the dry season between August and September, with kerosene drip torches. Fires were more intense and severe in the triennially burned plot because of higher litter accumulation (Balch et al. 2015), especially after the extreme drought events of 2007 and 2010 (Brando et al. 2014). Even eight years after fires ceased, these burned forests continued to degrade, with an increased mortality of large trees, and decreased above-ground biomass and canopy cover (Brando et al. 2019b). Our plots were not replicated due to logistical, ethical and legal limitations. However, the plots did not differ regarding several vegetation and microclimatic variables before fire (Balch et al., 2008), suggesting that observed differences can be attributed to the treatment rather than to site effects. For more details of the fire experiments, including pictures of burned and control sites, see Balch et al. (2015). Sampling design We selected five individuals from five tree species in each plot treatment (N = 75). These individuals were located across two linear transects, distant 500 and 750 m from forest plot edges, and were spaced at least 10 m from each other. The selection of tree species was based upon the following criteria: absences of extrafloral nectaries and commonness across the study area, according to previous inventories (e.g., Brando et al. 2019b). The selected species were Myrcia multiflora (Lam.) DC. (Myrtaceae), Micropholis egensis (A.DC.) Pierre (Sapotaceae), Sacoglottis guianensis Benth. (Humiriaceae), Sloanea sinemariensis Aubl. (Elaeocarpaceae) and Tapirira guianensis Aubl. (Anacardiaceae). Herbivory assessment We quantified leaf area loss by chewing insects (hereafter herbivory) between March and April 2018 by sampling 50 leaves from each tree – and a total of 1,250 leaves per plot. Sampled trees were at least 4m high, so sampled branches were chosen from a distance to avoid selection bias. We sampled branches from the outer part of the crown of all species; for that, we used a slingshot-like apparatus to throw a rope in the selected branch and then bring it down for sampling. We collected the first 50 leaves from the base to the apex of each branch (according to the standardized protocol of Mendes et al. 2021). Leaves were taken to the laboratory, scanned and had their margins or limb completed to account for leaf area loss to herbivores using the software GIMP. Measurements of leaf area and leaf area lost to herbivory were performed with the R package ‘EBImage’ (Pau et al. 2010). We calculated herbivory in each tree by averaging the percentage of leaf area loss across all 50 leaves sampled. Plant palatability Leaf nitrogen content indicates the plant nutritional value to insects (Mattson 1980) and it was estimated through chlorophyll content using a SPAD-502 (Spectrum Technologies, Inc., Plainfield, IL, USA), as leaf chlorophyll concentration and leaf nitrogen content are linearly correlated (Loh et al. 2002). One intact leaf was randomly sampled from each tree (different from those which we measured herbivory), and measurements were taken at the lamina midpoint close to the midrib of the leaves. We measured leaf thickness as a proxy for sclerophylly. We sampled 10 intact leaves of each individual and measured leaf thickness with a Digital Micrometer (0-25 mm/0.001 mm MDC-Lite 293-821-30). Measurements were taken around the midpoint in both sides of the midrib of the leaves. We calculated mean leaf thickness for each leaf and then for each individual plant, using leaves as replicates per species. Predation pressure To estimate predation pressure, we first established a field experiment using artificial model prey – which represent herbivores as caterpillars and other herbivores that do not have anti-predation behavioral forms and strategies (Dáttilo et al. 2016; Roslin et al. 2017). Artificial caterpillars were made with oil-based, odorless, non-toxic soft modeling clay, and were ~ 3 cm long and 5 mm in diameter. All models were green and had a wire passing through their center to attach them to the branch (Figure S1). We established the experiment in May of 2018, at 500 m from the forest plots edges, in different plants than those used for the herbivory experiment, irrespectively of plant species. For each treatment plot we set 15 sampling points across a previously established trail (2 m wide), which were located 10 m apart from each other. Each sampling point consisted of two plants, located at least two meters apart and on opposite sides of the trail. Plants were between 1.5 and 2 m in height, and were chosen arbitrarily. One model prey was fixed distally in an outer branch at ~ 1.5 m height of each plant, totaling 30 artificial prey per plot - each sampling point had a pair of plants with an artificial prey in each. From the total artificial prey, four were colonized by termites and were discarded from our analyses. All model prey remained in the field for four days, and afterwards each model was examined for predation marks, and categorized to major groups of predators (birds, arthropods, mammals) according to Low et al. (2014). As an additional proxy to our estimation of predation pressure by arthropods we compared the abundances of predatory ants, which are the main arthropod predators in the tropics (Zvereva et al. 2019), across treatment plots. For that, we sampled ant communities in the same study area approximately one year (February 2019) after vegetation and predation experiment samplings. We established six sampling sites in each treatment plot (N = 18); all sampling sites were located at least 250 m from the forest edge, and at least 170 m apart from each other. Each sampling site consisted of a 3 × 3 grid of nine sampling stations located 10 m apart. Each sampling station consisted of unbaited pitfall traps – one in the epigaeic and the other in the arboreal stratum. All pitfall traps were 5 cm in diameter, were partly filled with a salt solution and detergent, and were left open for 48 h. The epigaeic traps were buried with their rims flush to the soil surface, and arboreal traps were tied at a height of 2 m to the trunk of the nearest tree with dbh ≥10 cm. We sorted ants to species, and assigned morphospecies when species identification was not possible. Voucher specimens are held at the Laboratório de Ecologia de Comunidades e Ecossistemas Tropicais (EcoTrop), Universidade Federal de Viçosa (UFV), Brazil. Data analysis We adjusted a mixed beta regression model with logit link to test for the effects of fire on herbivory, with tree species as random variable. Results of beta regression modeling are shown in the text and figure as percentage rather than proportion for ease of interpretation. To test whether leaf nitrogen content and leaf thickness varied across treatment plots, we built two generalized linear mixed models, one with leaf nitrogen content and the other with leaf thickness as response variable, and fire treatments as explanatory variable. Both models had Gaussian error distribution, and tree species as a random effect. We compared predation pressure across treatment plots by considering arthropod and bird marks separately, as these two groups of predators were the most abundant in our study area. We built two generalized linear