scholarly journals Mapping Land Cover Types using Sentinel-2 Imagery: A Case Study

Author(s):  
Laura Annovazzi-Lodi ◽  
Marica Franzini ◽  
Vittorio Casella
Oryx ◽  
2020 ◽  
pp. 1-8
Author(s):  
Nicole Frances Angeli ◽  
Lee Austin Fitzgerald

Abstract Reintroducing species into landscapes with persistent threats is a conservation challenge. Although historic threats may not be eliminated, they should be understood in the context of contemporary landscapes. Regenerating landscapes often contain newly emergent habitat, creating opportunities for reintroductions. The Endangered St Croix ground lizard Pholidoscelis polops was extirpated from the main island of St Croix, U.S. Virgin Islands, as a result of habitat conversion to agriculture and predation by the small Indian mongoose Herpestes auropunctatus. The species survived on two small cays and was later translocated to two islands. Since the 1950s, new land-cover types have emerged on St Croix, creating a matrix of suitable habitat throughout the island. Here we examined whether the new habitat is sufficient for a successful reintroduction of the St Croix ground lizard, utilizing three complementary approaches. Firstly, we compared a map from 1750 to the current landscape of St Croix and found statistical similarity of land-cover types. Secondly, we determined habitat suitability based on a binomial mixture population model developed as part of the programme monitoring the largest extant population of the St Croix ground lizard. We estimated the habitat to be sufficient for > 142,000 lizards to inhabit St Croix. Thirdly, we prioritized potential reintroduction sites and planned for reintroductions to take place during 2020–2023. Our case study demonstrates how changing landscapes alter the spatial configuration of threats to species, which can create opportunities for reintroduction. Presuming that areas of degraded habitat may never again be habitable could fail to consider how regenerating landscapes can support species recovery. When contemporary landscapes are taken into account, opportunities for reintroducing threatened species can emerge.


Author(s):  
A. Sekertekin ◽  
A. M. Marangoz ◽  
H. Akcin

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.


Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>


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