21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions

Tropical carbon emissions are largely derived from direct forest clearing processes. Yet, emissions from drought-induced forest fires are, usually, not included in national-level carbon emission inventories. Here we examine Brazilian Amazon drought impacts on fire incidence and associated forest fire carbon emissions over the period 2003–2015. We show that despite a 76% decline in deforestation rates over the past 13 years, fire incidence increased by 36% during the 2015 drought compared to the preceding 12 years. The 2015 drought had the largest ever ratio of active fire counts to deforestation, with active fires occurring over an area of 799,293 km2. Gross emissions from forest fires (989 ± 504 Tg CO2 year−1) alone are more than half as great as those from old-growth forest deforestation during drought years. We conclude that carbon emission inventories intended for accounting and developing policies need to take account of substantial forest fire emissions not associated to the deforestation process

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013-2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land-sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change

Remote sensing of phytoplankton functional types in the coastal ocean from the HyspIRI Preparatory Flight Campaign

The 2013-2015 Hyperspectral Infrared Imager (HyspIRI) Preparatory Flight Campaign, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS/ASTER Airborne Simulator (MASTER), seeks to demonstrate appropriate sensor signal, spatial and spectral resolution, and orbital pass geometry for a global mission to reveal ecological and climatic gradients expressed in the selected California, USA study area. One of the awarded projects focused on the flight transects covering the coastal ocean to demonstrate that the AVIRIS data can be used to infer phytoplankton functional types at the land-sea interface. Specifically, this project directly assesses whether HyspIRI can provide adequate signal in the complex aquatic environment of the coastal zone to address questions of algal bloom dynamics, water quality, transient responses to human disturbance, river runoff, and red tides. Phytoplankton functional type (PFT), or biodiversity, can be determined from ocean color using the Phytoplankton Detection with Optics (PHYDOTax) algorithm and this information can be used to detect and monitor for harmful algal blooms. PHYDOTax is sensitive to spectral shape and accurate retrievals of ocean color across the visible spectral range is needed. The specific goal of this paper is to address the challenges of sensor capabilities and atmospheric correction in coastal environments by assessing two atmospheric correction methods using AVIRIS data for the retrieval of ocean color for use in derived products of chlorophyll-a and phytoplankton functional type. The atmospheric correction algorithms Atmospheric Removal (ATREM) and Tafkaa were applied to AVIRIS imagery of Monterey Bay, CA collected on 10 April 2013 and 31 October 2013. Data products from the imagery were compared with shipboard measurements including chlorophyll-a from whole-water samples and phytoplankton community structure estimated from diagnostic pigment markers using CHEMical TAXonomy (CHEMTAX). Using ATREM and Tafkaa and a selected set of input parameters for the scenes, we were unable to produce accurate retrievals of ocean color for the determination of chlorophyll-a and phytoplankton diversity. A modified ATREM correction produced science-quality data in which chlorophyll-a was accurately estimated using the Ocean Color 3 (OC3) chlorophyll-a algorithm, but biodiversity using PHYDOTax was not accurately estimated. Improvements in sensor calibration, sensitivity, and atmospheric correction of the HyspIRI imagery data set is needed in order to adequately estimate biogeochemically meaningful data products for the ocean such as chlorophyll-a, inherent optical properties, or PFTs. The HyspIRI Science Team is seeking improvements so the HyspIRI Airborne Campaign data set can be used for algorithm development to understand biodiversity and ecosystem function of coastal habitats that are facing increasing threats of human impact and climate change