Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

He, Yinan; Falco, Nicola; Bond-Lamberty, Ben; N. Myers-Pigg, Allison; E. Newcomer, Michelle; Ladau, Joshua; R. Holmquist, James; B. Brown, James

doi:10.15485/2326012

Title: Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

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Abstract

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

Authors:

He, Yinan;

; Bond-Lamberty, Ben; N. Myers-Pigg, Allison; E. Newcomer, Michelle; Ladau, Joshua; R. Holmquist, James; B. Brown, James

Lawrence Berkeley National Laboratory; Lawrence Berkeley National Laboratory
Lawrence Berkeley National Laboratory
Pacific Northwest National Laboratory (PNNL)

Publication Date:: Sun Dec 31 23:00:00 EST 2023

DOE Contract Number:: AC02-05CH11231

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; COMPASS-FME

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES; Chesapeake Bay; Costal ecosystem; Distance from shoreline; EARTH SCIENCE > AGRICULTURE > SOILS > SOIL RESPIRATION; EARTH SCIENCE > AGRICULTURE > SOILS > SOIL TEXTURE; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > AIR TEMPERATURE; EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > COMMUNITY DYNAMICS > BIODIVERSITY FUNCTIONS; EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND USE CLASSES; EARTH SCIENCE > LAND SURFACE > SOILS; EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY; EARTH SCIENCE > OCEANS > COASTAL PROCESSES > TIDAL HEIGHT; Functional zonation; Harmonized Landsat 8 and Sentinel-2; Kernel NDVI; Terrestrial-aquatic interface; biodiversity; kernel NDVI; soil respiration; topographical metrics

OSTI Identifier:: 2326012

DOI:: https://doi.org/10.15485/2326012

Citation Formats


                    He, Yinan, Falco, Nicola, Bond-Lamberty, Ben, N. Myers-Pigg, Allison, E. Newcomer, Michelle, Ladau, Joshua, R. Holmquist, James, and B. Brown, James. Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”.  United States: N. p., 2023. 
        Web.  doi:10.15485/2326012.

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                    He, Yinan, Falco, Nicola, Bond-Lamberty, Ben, N. Myers-Pigg, Allison, E. Newcomer, Michelle, Ladau, Joshua, R. Holmquist, James, & B. Brown, James. Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”.  United States.  doi:https://doi.org/10.15485/2326012

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                    He, Yinan, Falco, Nicola, Bond-Lamberty, Ben, N. Myers-Pigg, Allison, E. Newcomer, Michelle, Ladau, Joshua, R. Holmquist, James, and B. Brown, James. 2023.  
"Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”".  United States.  doi:https://doi.org/10.15485/2326012.  https://www.osti.gov/servlets/purl/2326012. Pub date:Sun Dec 31 23:00:00 EST 2023

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@article{osti_2326012,

  title        = {Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”},

  author       = {He, Yinan and Falco, Nicola and Bond-Lamberty, Ben and N. Myers-Pigg, Allison and E. Newcomer, Michelle and Ladau, Joshua and R. Holmquist, James and B. Brown, James},

  abstractNote = {The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.},

  doi          = {10.15485/2326012},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sun Dec 31 23:00:00 EST 2023},

  month        = {Sun Dec 31 23:00:00 EST 2023}

}

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DOI: https://doi.org/10.15485/2326012

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