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Geographies, Volume 1, Issue 3 (December 2021) – 3 articles

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Article
The Silent Forest: Impact of Bird Hunting by Prehistoric Polynesians on the Decline and Disappearance of Native Avifauna in Hawai’i
Geographies 2021, 1(3), 192-216; https://0-doi-org.brum.beds.ac.uk/10.3390/geographies1030012 - 18 Oct 2021
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Abstract
This research focuses on the historical demise of Hawaiian avifauna due to hunting by ancient Polynesians. Numerous documents, published since the early 1800s, were scrutinized and evaluated; these provided information on bird hunting and traditional Hawaiian practices. Hawaiians used birds as sources of [...] Read more.
This research focuses on the historical demise of Hawaiian avifauna due to hunting by ancient Polynesians. Numerous documents, published since the early 1800s, were scrutinized and evaluated; these provided information on bird hunting and traditional Hawaiian practices. Hawaiians used birds as sources of feathers and food. Feathers were important symbols of power for Polynesians; in Hawai’i, feathers were more highly prized than other types of property. Feathers used for crafts were obtained from at least 24 bird species, however, the golden feathers of ōō and mamo birds made them primary targets for birdhunters; both birds became extinct by the late 1800s. Feathers were utilized for many items, including ahuula [cloaks], mahiole [war helmets], and kāhili [standards]. Most garments utilized a considerable number of feathers; a cloak for Kamehameha consumed the golden feathers of 80,000 mamo birds. Bird meat was an important food item for native Hawaiians. It is believed that most birds were killed after being plucked; historical sources mention ~30 bird species were consumed. The uau (Pterodroma sandwichensis), a currently endangered seabird, was ruthlessly hunted and avidly eaten. Its current geographical range is just a minute fraction of its former one; now, uaus are largely restricted to inaccessible cliffs at Haleakalā Crater (Maui). Full article
(This article belongs to the Special Issue Feature Papers of Geographies in 2021)
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Article
The Utility of Sentinel-2 MSI Data to Estimate Wetland Vegetation Leaf Area Index in Natural and Rehabilitated Wetlands
Geographies 2021, 1(3), 178-191; https://0-doi-org.brum.beds.ac.uk/10.3390/geographies1030011 - 16 Oct 2021
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Abstract
Wetland ecosystems are being modified and threatened due to anthropogenic activities and climate change, hence the urgent need for wetland restoration. Wetland rehabilitation is important in the reversal of these dire conditions, and this can be pursued through restoring damaged wetland ecosystems and [...] Read more.
Wetland ecosystems are being modified and threatened due to anthropogenic activities and climate change, hence the urgent need for wetland restoration. Wetland rehabilitation is important in the reversal of these dire conditions, and this can be pursued through restoring damaged wetland ecosystems and recovering wetland vegetation. Wetland biophysical properties such as leaf area index (LAI) are important indicators of vegetation productivity and stress. Therefore, the study sought to test the potential of Sentinel-2 multispectral instrument (MSI) derived standard bands, traditional vegetation indices and red-edge derived vegetation indices in estimating wetland vegetation LAI across natural and rehabilitated wetlands. Traditional field surveys were carried out for LAI measurement of wetland vegetation using the LAI-2200 Plant Canopy Analyser. Partial Least Squares Regression (PLSR) algorithms were used to compare the estimation strength of models derived from all Sentinel-2 MSI bands, conventional vegetation indices and red-edge derived vegetation indices. Leave-one-out cross-validation (LOOCV) was completed on a selected measured dataset to evaluate the performance and accuracy of the estimation models. The optimal models for estimating wetland vegetation LAI were produced based on red-edge bands centred between the 705–783 nm as well as the 865 nm (Band 8a) of the electromagnetic spectrum. The results showed that vegetation indices derived from red-edge bands performed better at estimating LAI for both wetlands with a root mean square error of prediction (RMSE) of 0.32 m2/m2 and R2 of 0.61 for the natural wetland, and RMSE of 0.51 m2/m2 and R2 of 0.75 for the rehabilitated wetland. The optimal model for predicting LAI across natural and rehabilitated wetlands was attained based on red-edge bands centred at 705 nm (Band 5), 740 nm (Band 6), 783 nm (Band 7) as well as 865 nm (Band 8a) yielding a RMSE of 0.51 m2/m2 and R2 of 0.54. Overall, the results underscore the importance of remotely sensed derived data and vegetation indices in the optimal characterisation of wetland vegetation productivity which can be utilized in the monitoring and management of wetland ecosystems. Full article
(This article belongs to the Special Issue Early Career Scientists’ (ECS) Contributions to Geographies)
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Technical Note
Spatiotemporal Land-Use Changes of Batticaloa Municipal Council in Sri Lanka from 1990 to 2030 Using Land Change Modeler
Geographies 2021, 1(3), 166-177; https://0-doi-org.brum.beds.ac.uk/10.3390/geographies1030010 - 28 Sep 2021
Viewed by 434
Abstract
Land-use change is a predictable and principal driving force of potential environmental changes on all spatial and temporal scales. A land-use change model is a tool that supports the analysis of the sources and consequences of land-use dynamics. This study aims to assess [...] Read more.
Land-use change is a predictable and principal driving force of potential environmental changes on all spatial and temporal scales. A land-use change model is a tool that supports the analysis of the sources and consequences of land-use dynamics. This study aims to assess the spatiotemporal land-use changes that occurred during 1990–2020 in the municipal council limits of Batticaloa. A land change modeler has been used as an innovative land planning and decision support system in this study. The main satellite data were retrieved from Landsat in 1990, 2000, 2010, and 2020. For classification, the supervised classification method was employed, particularly with the medium resolution satellite images. Land-use classes were analyzed by the machine learning algorithm in theland change modeler. The Markov chain method was also used to predict future land-use changes. The results of the study reveal that only one land-use type, homestead, has gradually increased, from 12.1% to 34.1%, during the above-mentioned period. Agriculture land use substantially declined from 26.9% to 21.9%. Bare lands decreased from 11.5% to 5.0%, and wetlands declined from 13.9% to 9.6%. Full article
(This article belongs to the Special Issue Early Career Scientists’ (ECS) Contributions to Geographies)
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