Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests

The role of tropical forests in the global carbon budget remains controversial,as carbon emissions from deforestation are highly uncertain.This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation.New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar.We found that lost forests are special cases,and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography.Thus,using biomass mapping,we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions.Here,using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests,we found that deforestation in the 2000s in Latin America,one of the severely deforested regions,mainly occurred in forests with a significantly lower carbon stock density than typical mature forests.

A case study for intercomparison of land surface temperature retrieved from GOES and MODIS

In recent years,algorithms have been developed to derive land surface temperature(LST)from geostationary and polar satellite systems.However,few works have addressed the intercomparison between Geostationary Operational Environmental Satellites(GOES)and the available suite of polar sensors.In this study,differences in LSTs between GOES and MODerate resolution Imaging Spectroradiometer(MODIS)have been compared and also evaluated against ground observations.Due to the lack of split-window(SW)channels in the GOES M(12)-Q era,a dual-window algorithm using a mid-infrared 3.9µm channel is compared with traditional SW algorithm.It is found that the differences in LST between different platforms are bigger during daytime than those during nighttime.During daytime,LSTs from GOES with the dualwindow algorithm are warmer than MODIS LSTs,while LSTs from the SW algorithm are close to MODIS LSTs.The difference during daytime is found to be related to anisotropy in satellite viewing geometry,and land surface properties,such as vegetation cover and especially surface emissivity at middle infrared(MIR)channel.When evaluated against ground observations,the standard deviation(precision)error(2.35 K)from the dual window algorithm is worse than that(1.83 K)from the SW algorithm,indicating the lack of split-window channel in the GOES M(12)-Q era may degrade the performance of LST retrievals.

Fine scale optical remote sensing experiment of mixed stand over complex terrain(FOREST)in the Genhe Reserve Area:objective,observation and a case study

Optical remote sensing allows to efficiently monitor forest ecosystems at regional and global scales.However,most of the widely used optical forward models and backward estimation methods are only suitable for forest canopies in flat areas.To evaluate the recent progress in forest remote sensing over complex terrain,a satellite-airborne-ground synchronous Fine scale Optical Remote sensing Experiment of mixed Stand over complex Terrain(FOREST)was conducted over a 1 km×1 km key experiment area(KEA)located in the Genhe Reserve Areain 2016.Twenty 30 m×30 m elementary sampling units(ESUs)were established to represent the spatiotemporal variations of the KEA.Structural and spectral parameters were simultaneously measured for each ESU.As a case study,we first built two 3D scenes of the KEA with individual-tree and voxel-based approaches,and then simulated the canopy reflectance using the LargE-Scale remote sensing data and image Simulation framework over heterogeneous 3D scenes(LESS).The correlation coefficient between the LESS-simulated reflectance and the airborne-measured reflectance reaches 0.68-0.73 in the red band and 0.56-0.59 in the near-infrared band,indicating a good quality of the experiment dataset.More validation studies of the related forward models and retrieval methods will be done.

Clear-sky land surface upward longwave radiation dataset derived from the ABI onboard the GOES-16 satellite

Surface upward longwave radiation(SULR)is one of the four components of the surface radiation budget,which is defined as the total surface upward radiative flux in the spectral domain of 4-100μm.The SULR is an indicator of surface thermal conditions and greatly impacts weather,climate,and phenology.Big Earth data derived from satellite remote sensing have been an important tool for studying earth science.The Advanced Baseline Imager(ABI)onboard the Geostationary Operational Environmental Satellite(GOES-16)has greatly improved temporal and spectral resolution compared to the imager sensor of the previous GOES series and is a good data source for the generation of high spatiotemporal resolution SULR.In this study,based on the hybrid SULR estimation method and an upper hemisphere correction method for the SULR dataset,we developed a regional clear-sky land SULR dataset for GOES-16 with a half-hourly resolution for the period from 1st January 2018 to 30th June 2020.The dataset was validated against surface measurements collected at 65 Ameriflux radiation network sites.Compared with the SULR dataset of the Global LAnd Surface Satellite(GLASS)longwave radiation product that is generated from the Moderate Resolution Imaging Spectroradiometer(MODIS)onboard the polar-orbiting Terra and Aqua satellites,the ABI/GOES-16 SULR dataset has commensurate accuracy(an RMSE of 15.9 W/m2 vs 19.02 W/m2 and an MBE of−4.4 W/m2 vs−2.57 W/m2),coarser spatial resolution(2 km at nadir vs 1 km resolution),less spatial coverage(most of the Americas vs global),fewer weather conditions(clear-sky vs all-weather conditions)and a greatly improved temporal resolution(48 vs 4 observations a day).The published data are available at http://www.dx.doi.org/10.11922/sciencedb.j00076.00062.

Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Canopy cover is an important parameter affecting forest succession,carbon fluxes,and wildlife habitats.Several global maps with different spatial resolutions have been produced based on satellite images,but facing the deficiency of reliable references for accuracy assessments.The rapid development of unmanned aerial vehicle(UAV)equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost,which provides the research community a promising tool to collect reference data.However,it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images(RGB)due to the limited spectral information.In addition,the canopy height model(CHM)derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations.This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images,which was referred to as BAMOS for convenience.The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map(DOM)and structural information from digital surface model(DSM).Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km^(2) across Daxing’anling forested area in northeast of China.Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient(r)of 0.96 and root mean square error(RMSE)of 5.7%.Then,the UAV-based canopy covers served as references for assessment of satellite-based maps,including MOD44B Version 6 Vegetation Continuous Fields(MODIS VCF),maps developed by the Global Land Cover Facility(GLCF)and by the Global Land Analysis and Discovery laboratory(GLAD).Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns,but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas,and GLCF failed to capture non-tree areas.Most important of all,obvious underestimations with RMSE about 20%were easily observed in all satellite-based maps,although the temporal inconsistency with references might have some contributions.