Displaying 1 - 10 of 12
Operations
Gao, Jing; O'Neill, Brian. 2019. Data-driven spatial modeling of long-term urban land development potential for global environmental change impact assessment: The SELECT model. [Environmental Modelling & Software] . 119: 458-471 DOI: https://doi.org/10.1016/j.envsoft.2019.06.015
SEDAC Data Collection(s):
(Journal Article)
export
Han, Xian Xian; Li, Gao Yang; Lu, Wen Fang; Jiang, Yu Wu. 2019. Comparing statistical and semi-distributed rainfall–runoff models for a large subtropical watershed: A case study of Jiulong River catchment, China. [Atmosphere] . 10(2): 62 DOI: https://doi.org/10.3390/atmos10020062
Uses Remote Sensing: yes
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export
Jin, Kai; Wang, Fei; Chen, Deliang; Liu, Huanhuan; Ding, Wenbin; Shi, Shangyu. 2019. A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series. [Scientific Data] . 6(1): 139 DOI: https://doi.org/10.1038/s41597-019-0143-1
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
export
Liu, Xue; de Sherbinin, Alex; Zhan, Yanni. 2019. Mapping urban extent at large spatial scales using machine learning methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data. [Remote Sensing] . 11(10): 1247 DOI: https://doi.org/10.3390/rs11101247
Uses Remote Sensing: yes
SEDAC Data Collection(s):
grump-v1
ulandsat
(Journal Article)
export
Maier, Stephanie D.; Lindner, Jan Paul; Francisco, Javier. 2019. Conceptual framework for biodiversity assessments in global value chains. [Sustainability] . 11(7): 1841 DOI: https://doi.org/10.3390/su11071841
SEDAC Data Collection(s):
ferman-v1
lulc
ulandsat
(Journal Article)
export
Marivoet, Wim; Ulimwengu, John; Sedano, Fernando. 2019. Spatial typology for targeted food and nutrition security interventions. [World Development] . 120: 62-75 DOI: https://doi.org/10.1016/j.worlddev.2019.04.003
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export
Meehan, Timothy D.; Michel, Nicole L.; Rue, Håvard. 2019. Spatial modeling of Audubon Christmas Bird Counts reveals fine-scale patterns and drivers of relative abundance trends. [Ecosphere] . 10(4): e02707 DOI: https://doi.org/10.1002/ecs2.2707
SEDAC Data Collection(s):
(Journal Article)
export
Meerow, Sara. 2019. A green infrastructure spatial planning model for evaluating ecosystem service tradeoffs and synergies across three coastal megacities. [Environmental Research Letters] . 14(12): 125011 DOI: https://doi.org/10.1088/1748-9326/ab502c
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export
Meng, Jun; Li, Chi; Martin, Randall V.; van Donkelaar, Aaron; Hystad, Perry; Brauer, Michael. 2019. Estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America from chemical transport modeling, satellite remote sensing and ground-based measurements. [Environmental Science & Technology] . 53(9): 5071-5079 DOI: https://doi.org/10.1021/acs.est.8b06875
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
export
Morgan, Brett; Guénard, Benoit. 2019. New 30 m resolution Hong Kong climate, vegetation, and topography rasters indicate greater spatial variation than global grids within an urban mosaic. [Earth System Science Data] . 11(3): 1083-1098 DOI: https://doi.org/10.5194/essd-11-1083-2019
Uses Remote Sensing: yes
SEDAC Data Collection(s):
ulandsat
(Journal Article)
export

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