Potential of texture metrics derived from high-resolution PLEIADES satellite data for quantifying aboveground carbon of Kandelia candel mangrove forests in Southeast China

2018 ◽  
Vol 26 (5) ◽  
pp. 789-803 ◽  
Author(s):  
Min Wang ◽  
Wenzhi Cao ◽  
Qingsong Guan ◽  
Gaojie Wu ◽  
Chao Jiang ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Mangrove Forests ◽  
Kandelia Candel ◽  
Southeast China ◽  
Aboveground Carbon ◽  
Texture Metrics

Potential ecosystem service values of mangrove forests in southeastern China using high-resolution satellite data

2018 ◽  
Vol 209 ◽  
pp. 30-40 ◽  
Author(s):  
Min Wang ◽  
Wenzhi Cao ◽  
Chao Jiang ◽  
Yiyun Yan ◽  
Qingsong Guan
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Ecosystem Service ◽  
Mangrove Forests ◽  
Southeastern China

Estimation of aboveground biomass in mangrove forests using high-resolution satellite data

2014 ◽  
Vol 19 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Yasumasa Hirata ◽  
Ryuichi Tabuchi ◽  
Pipat Patanaponpaiboon ◽  
Sasitorn Poungparn ◽  
Reiji Yoneda ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Aboveground Biomass ◽  
Mangrove Forests

Generation of DEM and Ortho-image from High Resolution Stereo Satellite Data for a selected part of Southeast Bangladesh

2011 ◽  
Vol 4 (1) ◽  
pp. 500-502
Author(s):  
Md. Fazlul Haque ◽  
◽  
Md. Mostafizur Rahman Akhand ◽  
Dr. Dewan Abdul Quadir
Keyword(s):  
High Resolution ◽  
Satellite Data

A high-resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

2013 ◽  
Vol 20 (4) ◽  
pp. 1191-1210 ◽  
Author(s):  
Jonas Jägermeyr ◽  
Dieter Gerten ◽  
Wolfgang Lucht ◽  
Patrick Hostert ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Ecosystem Respiration

Very high resolution aboveground carbon mapping in subtropical thicket

2021 ◽  
Vol 15 (03) ◽  
Author(s):  
Dugal Harris ◽  
Cosman Bolus ◽  
James Reeler
Keyword(s):  
High Resolution ◽  
Aboveground Carbon ◽  
Carbon Mapping ◽  
Very High

Machine Learning based Extraction of Electrical Substations from High Resolution Satellite Data: Outcome of the ICETCI 2021 Challenge

2021 ◽  
Author(s):  
Sreenivasan G ◽  
Anju Bajpai ◽  
Prakasa Rao D S ◽  
Girish Kumar T P ◽  
Ashish Shrivastava ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Data

scholarly journals High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US

2018 ◽  
Vol 18 (8) ◽  
pp. 5483-5497 ◽  
Author(s):  
Jennifer Kaiser ◽  
Daniel J. Jacob ◽  
Lei Zhu ◽  
Katherine R. Travis ◽  
Jenny A. Fisher ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Satellite Data ◽  
Data Uncertainty ◽  
Emission Inventories ◽  
Model Errors ◽  
Atmospheric Models ◽  
Isoprene Emission ◽  
Isoprene Oxidation ◽  
Southeast Us

Abstract. Isoprene emissions from vegetation have a large effect on atmospheric chemistry and air quality. “Bottom-up” isoprene emission inventories used in atmospheric models are based on limited vegetation information and uncertain land cover data, leading to potentially large errors. Satellite observations of atmospheric formaldehyde (HCHO), a high-yield isoprene oxidation product, provide “top-down” information to evaluate isoprene emission inventories through inverse analyses. Past inverse analyses have however been hampered by uncertainty in the HCHO satellite data, uncertainty in the time- and NOx-dependent yield of HCHO from isoprene oxidation, and coarse resolution of the atmospheric models used for the inversion. Here we demonstrate the ability to use HCHO satellite data from OMI in a high-resolution inversion to constrain isoprene emissions on ecosystem-relevant scales. The inversion uses the adjoint of the GEOS-Chem chemical transport model at 0.25∘ × 0.3125∘ horizontal resolution to interpret observations over the southeast US in August–September 2013. It takes advantage of concurrent NASA SEAC4RS aircraft observations of isoprene and its oxidation products including HCHO to validate the OMI HCHO data over the region, test the GEOS-Chem isoprene oxidation mechanism and NOx environment, and independently evaluate the inversion. This evaluation shows in particular that local model errors in NOx concentrations propagate to biases in inferring isoprene emissions from HCHO data. It is thus essential to correct model NOx biases, which was done here using SEAC4RS observations but can be done more generally using satellite NO2 data concurrently with HCHO. We find in our inversion that isoprene emissions from the widely used MEGAN v2.1 inventory are biased high over the southeast US by 40 % on average, although the broad-scale distributions are correct including maximum emissions in Arkansas/Louisiana and high base emission factors in the oak-covered Ozarks of southeast Missouri. A particularly large discrepancy is in the Edwards Plateau of central Texas where MEGAN v2.1 is too high by a factor of 3, possibly reflecting errors in land cover. The lower isoprene emissions inferred from our inversion, when implemented into GEOS-Chem, decrease surface ozone over the southeast US by 1–3 ppb and decrease the isoprene contribution to organic aerosol from 40 to 20 %.

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