Displaying 1 - 5 of 5
Operations
Duarte-Guardia, Sandra; Peri, Pablo; Amelung, Wulf; Thomas, Evert; Borchard, Nils; Baldi, German; Cowie, Annette; Ladd, Brenton. 2020. Biophysical and socioeconomic factors influencing soil carbon stocks: a global assessment. [Mitigation and Adaptation Strategies for Global Change] . 25: 1129-1148 DOI: https://doi.org/10.1007/s11027-020-09926-1
SEDAC Data Collection(s):
(Journal Article)
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Macdonald, David W.; Chiaverini, Luca; Bothwell, Helen M.; Kaszta, Żaneta; Ash, Eric; Bolongon, Gilmoore; Can, Özgün Emre; Campos-Arceiz, Ahimsa; Channa, Phan; Clements, Gopalasamy Reuben; Hearn, Andrew J.; Hedges, Laurie; Htun, Saw; Kamler, Jan F.; Macdonald, Ewan A.; Moore, Jonathan; Naing, Hla; Onuma, Manabu; Rasphone, Akchousanh; Rayan, D. Mark; Ross, Joanna; Singh, Priya; Tan, Cedric Kai Wei; Wadey, Jamie; Yadav, Bhupendra P.; Cushman, Samuel A. 2020. Predicting biodiversity richness in rapidly changing landscapes: climate, low human pressure or protection as salvation?. [Biodiversity and Conservation] . 29: 4035-4057 DOI: https://doi.org/10.1007/s10531-020-02062-x
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(Journal Article)
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Zhao, Sen; Pereira, Paulo; Wu, Xiuqin; Zhou, Jinxing; Cao, Jianhua; Zhang, Weixin. 2020. Global karst vegetation regime and its response to climate change and human activities. [Ecological Indicators] . 113: 106208 DOI: https://doi.org/10.1016/j.ecolind.2020.106208
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
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Chammartin, Frédérique; Scholte, Ronaldo G. C.; Malone, John B.; Bavia, Maria E.; Nieto, Prixia; Utzinger, Jürg; Vounatsou, Penelope. 2013. Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia. [Parasites & Vectors] . 6(1): 152 DOI: https://doi.org/10.1186/1756-3305-6-152
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
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Scholte, Ronaldo G. C.; Schur, Nadine; Bavia, Maria E.; Carvalho, Edgar M.; Chammartin, Frédérique; Utzinger, Jürg; Vounatsou, Penelope. 2013. Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models. [Geospatial Health] . 8(1): 97-110 DOI: https://doi.org/10.4081/gh.2013.58
Uses Remote Sensing: yes
SEDAC Data Collection(s):
(Journal Article)
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