Birds of Puerto Rico and the Virgin Islands

Abstract A new distribution map is provided for Pseudocercospora purpurea (Cooke) Deighton. Fungi: Ascomycota: Capnodiales. Hosts: avocado (Persea americana). Information is given on the geographical distribution in Asia (India, Sikkim, Japan, Philippines), Africa (Cameroon, Congo Democratic Republic, Cote d'Ivoire, Guinea, Kenya, South Africa), North America (Mexico, USA, Florida, Georgia, Mississippi), Central America and Caribbean (Bermuda, Dominica, El Salvador, Honduras, Jamaica, Nicaragua, Panama, Puerto Rico, Trinidad and Tobago, United States Virgin Islands), South America (Argentina, Bolivia, Brazil, Sao Paulo, Chile, Guyana, Peru, Venezuela), Oceania (Palau [Belau]).

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Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Remote Sensing ◽

10.3390/rs9020123 ◽

2017 ◽

Vol 9 (2) ◽

pp. 123 ◽

Cited By ~ 7

Author(s):

David Gwenzi ◽

Eileen Helmer ◽

Xiaolin Zhu ◽

Michael Lefsky ◽

Humfredo Marcano-Vega

Keyword(s):

Puerto Rico ◽

Tropical Forest ◽

Forest Biomass ◽

Virgin Islands ◽

Biomass Growth ◽

Canopy Area ◽

Stand Height ◽

The U.S

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Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

Journal of Sensors ◽

10.1155/2021/9926442 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Hanlin Liu ◽

Linqiang Yang ◽

Linchao Li

Keyword(s):

Machine Learning ◽

Puerto Rico ◽

Reference Frame ◽

Learning Algorithm ◽

Virgin Islands ◽

Machine Learning Algorithm ◽

Climate Factors ◽

Helmert Transformation ◽

The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

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