scholarly journals Coral bleaching variability during the 2017 global bleaching event on a remote, uninhabited island in the western Pacific: Farallon de Medinilla, Commonwealth of the Northern Mariana Islands

2020 ◽  
Vol 96 (4) ◽  
pp. 785-802
Author(s):  
Jessica E Carilli ◽  
Leslie Bolick ◽  
Donald E Marx ◽  
Stephen H Smith ◽  
Douglas Fenner
Keyword(s):  
Time Window ◽  
Mariana Islands ◽  
Depth Stratum ◽  
Unsuccessful Attempt ◽  
Northern Mariana Islands ◽  
Object Based Image Analysis ◽  
Bleached Coral ◽  
Object Based ◽  
Survey Protocol ◽  
The Western Pacific

A survey conducted in Fall 2017 by US Navy scientists around the small, uninhabited island of Farallon de Medinilla (FDM) documented severe bleaching related to extended regional heat stress. Three of the dominant scleractinian genera at FDM, Pocillopora, Leptastrea, and Astreopora, were severely impacted, with more than 90% of colonies from many species exhibiting bleaching. In contrast, several species of Porites corals, another dominant genus at FDM, fared better, with less bleaching (7%–68% by species) than the island average (78%). Bleaching was somewhat higher at shallower depths (<10 m depth stratum, compared to 10–20 m depth stratum) and on the leeward of the island. Surveying FDM presented logistical challenges including a compressed time window for survey execution, periods of strong currents >1 knot that precluded diving, rare but potentially hazardous ordnance items, survey requirements for georeferenced imagery, and quantitative data collection. The survey protocol designed to accommodate these challenges is presented here, as are lessons from an unsuccessful attempt to delineate bleached coral colonies in photographs using automated object-based image analysis.

Tales of the Pacific

Worldview ◽  
1983 ◽  
Vol 26 (9) ◽  
pp. 7-9
Author(s):  
Patricia Luce Chapman
Keyword(s):  
World War Ii ◽  
Mariana Islands ◽  
World War ◽  
Northern Mariana Islands ◽  
Political Status ◽  
The Pacific ◽  
Thousand Islands ◽  
The Republic ◽  
Republic Of Palau ◽  
The Western Pacific

The American “strategic trusteeship” of Micronesia, awarded by the United Nations after the Japanese defeat in World War II, is today giving way to a new political status for the islands. Tucked under the wing of the Federal Government since 1947—first as a ward of the Navy and* then of the Interior Department—the two thousand islands and atolls extending over three million-square miles of the Western Pacific are familiar only to those Americans old enough to have followed news of the war in the Pacific and of early A-bomb tests.The Micronesian chain begins with the Marshalls, 2,200 miles southwest of Hawaii—Bikini, Enewetak, and Kwajalein are here—and ends with the U.S. Territory of Guam, 1,600 mile east of Manila. To the west of the Marshalls are the Federated States of Micronesia (FSM)—Truk and Yap among them. Above Guam stretch the islands of the Commonwealth of the Northern Mariana Islands (CNMI), and below, the Republic of Palau.

Diabetes in the Indigenous Population of the Commonwealth of the Northern Mariana Islands

1997 ◽  
Vol 9 (1) ◽  
pp. 28-32 ◽  
Author(s):  
A Mark Durand ◽  
Joyce Bourne ◽  
David Thohey-Mote ◽  
K David Khorram ◽  
Isamu J Abraham
Keyword(s):  
Indigenous Population ◽  
Mariana Islands ◽  
Northern Mariana Islands ◽  
Relative Risks ◽  
Stage Renal Disease ◽  
End Stage ◽  
Diagnosed Diabetes ◽  
Prevention Of Diabetes ◽  
The Western Pacific ◽  
Related Mortality

Diabetes prevalence and complications rates were examined for the indigenous population of the Commonwealth of the Northern Mariana Islands in the western Pacific. Diabetes is common, with 4% of the population and 11% of adults having diagnosed diabetes. Diabetes is more common among females and islanders of Carolinian descent End stage renal disease, hospitalizations for pneumonia, non-traumatic lower extremity amputations and retinopathy are more common than expected (with relative risks of 3.9, 3.0, 1.5 and 1.3, respectively, compared with diabetics in US), while diabetes-related mortality and hospitalizations for ischemic heart disease/cerebrovas-cular disease are less (with relative risks of 0.9 and 0.8). Implications for primary and secondary prevention of diabetes and its complications are discussed.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

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