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1. Rapid Recent Deforestation Incursion in a Vulnerable Indigenous Land in the Brazilian Amazon and Fire-Driven Emissions of Fine Particulate Aerosol Pollutants

   https://doi.org/10.3390/f11080829  

   de Oliveira, Gabriel ; Chen, Jing M. ; Mataveli, Guilherme A. ; Chaves, Michel E. ; Seixas, Hugo T. ; Cardozo, Francielle da ; Shimabukuro, Yosio E. ; He, Liming ; Stark, Scott C. ; dos Santos, Carlos A. ( August 2020 , Forests)

   null (Ed.)

   Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential. 

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2. The effectiveness of global protected areas for climate change mitigation

   https://doi.org/10.1038/s41467-023-38073-9  

   Duncanson, L. ; Liang, M. ; Leitold, V. ; Armston, J. ; Krishna Moorthy, S. M. ; Dubayah, R. ; Costedoat, S. ; Enquist, B. J. ; Fatoyinbo, L. ; Goetz, S. J. ; et al ( June 2023 , Nature Communications)

   Forests play a critical role in stabilizing Earth’s climate. Establishing protected areas (PAs) represents one approach to forest conservation, but PAs were rarely created to mitigate climate change. The global impact of PAs on the carbon cycle has not previously been quantified due to a lack of accurate global-scale carbon stock maps. Here we used ~412 million lidar samples from NASA’s GEDI mission to estimate a total PA aboveground carbon (C) stock of 61.43 Gt (+/− 0.31), 26% of all mapped terrestrial woody C. Of this total, 9.65 + /− 0.88 Gt of additional carbon was attributed to PA status. These higher C stocks are primarily from avoided emissions from deforestation and degradation in PAs compared to unprotected forests. This total is roughly equivalent to one year of annual global fossil fuel emissions. These results underscore the importance of conservation of high biomass forests for avoiding carbon emissions and preserving future sequestration.

    

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3. Impact of a tropical forest blowdown on aboveground carbon balance

   https://doi.org/10.1038/s41598-021-90576-x  

   Cushman, K. C. ; Burley, John T. ; Imbach, Benedikt ; Saatchi, Sassan S. ; Silva, Carlos E. ; Vargas, Orlando ; Zgraggen, Carlo ; Kellner, James R. ( December 2021 , Scientific Reports)

   null (Ed.)

   Abstract Field measurements demonstrate a carbon sink in the Amazon and Congo basins, but the cause of this sink is uncertain. One possibility is that forest landscapes are experiencing transient recovery from previous disturbance. Attributing the carbon sink to transient recovery or other processes is challenging because we do not understand the sensitivity of conventional remote sensing methods to changes in aboveground carbon density (ACD) caused by disturbance events. Here we use ultra-high-density drone lidar to quantify the impact of a blowdown disturbance on ACD in a lowland rain forest in Costa Rica. We show that the blowdown decreased ACD by at least 17.6%, increased the number of canopy gaps, and altered the gap size-frequency distribution. Analyses of a canopy-height transition matrix indicate departure from steady-state conditions. This event will initiate a transient sink requiring an estimated 24–49 years to recover pre-disturbance ACD. Our results suggest that blowdowns of this magnitude and extent can remain undetected by conventional satellite optical imagery but are likely to alter ACD decades after they occur. 

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4. Combining airborne lidar and acoustic remote sensing to characterize the impacts of Amazon forest degradation

   Rappaport, D.I. ; Morton, D.C. ( May 2017 , Anais do Simpósio Brasileiro de Sensoriamento Remoto)

   Frontier forests in the Brazilian Amazon have been heavily altered by nearly a half-century of deforestation for agriculture and degradation from fire and logging. The long-term effects of forest degradation on habitat structure and habitat use remain poorly understood, largely due to the limitations of traditional field methods for characterizing heterogeneity at relevant spatial and temporal scales. This work demonstrates the opportunity to assess degradation impacts on ecosystem structure and biodiversity at landscape scales (200 km2) by combining airborne lidar and acoustic remote sensing across two municipalities in Mato Grosso, Feliz Natal and Nova Ubiratã. Among degradation classes, our results indicate that repeated fire events have the most destructive legacy for both habitat structure and habitat use. Lidar analyses reveal that repeated fire events can result in a total loss of original canopy trees. Similarly, our acoustic analyses suggest that repeated fires may fundamentally transform animal community composition. The combination of remote sensing approaches bridges the scale gap between ground-based and satellite observations to support a regional-scale investigation into the complex consequences of Amazon forest degradation. 

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5. Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

   https://doi.org/10.5281/zenodo.8057232  

   wang, zhuonan ; Kumar, Jitendra ; R., Samantha Weintraub-Leff ; Todd-Brown, Katherine ; Mishra, Umakant ; Sihi, Debjani ( January 2023 , Zenodo)

   Data Description:

   To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.

   Files in collection (32):

   Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts

   gNATSGO TIF files:

   ├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]
   ├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]
   ├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]
   ├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]
   ├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]
   ├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]
   ├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]
   ├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]
   ├── depthWT_30arc_us.tif                                                        [depth to water table]
   ├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]
   ├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]
   ├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]
   ├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]
   ├── pondingFre_30arc_us.tif                                                     [ponding frequency]
   ├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]
   ├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]
   ├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]
   ├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]
   ├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]
   └── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]

   SOC TIF files:

   ├──30cm SOC mean.tif                             [30 cm depth soil SOC]
   ├──100cm SOC mean.tif                           [100 cm depth soil SOC]
   ├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]
   └──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]

   site observations csv files:

   ISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   ISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data

   
   Data format:

   Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.

   Geospatial projection: 

   GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]]

    

    

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