scholarly journals Sentinel-2 Data for Land Cover/Use Mapping: A Review

2020 ◽  
Vol 12 (14) ◽  
pp. 2291 ◽  
Author(s):  
Darius Phiri ◽  
Matamyo Simwanda ◽  
Serajis Salekin ◽  
Vincent R. Nyirenda ◽  
Yuji Murayama ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Positive Impact ◽  
European Space Agency ◽  
Remotely Sensed ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Free Access ◽  
Remotely Sensed Data ◽  
Sentinel 2

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring.

scholarly journals Examining the Roles of Spectral, Spatial, and Topographic Features in Improving Land-Cover and Forest Classifications in a Subtropical Region

2020 ◽  
Vol 12 (18) ◽  
pp. 2907
Author(s):  
Xiaozhi Yu ◽  
Dengsheng Lu ◽  
Xiandie Jiang ◽  
Guiying Li ◽  
Yaoliang Chen ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Coniferous Forest ◽  
Remotely Sensed ◽  
Sensor Data ◽  
Remotely Sensed Data ◽  
Topographic Factors ◽  
Spectral Bands ◽  
Spatial Features ◽  
Sentinel 2

Many studies have investigated the effects of spectral and spatial features of remotely sensed data and topographic characteristics on land-cover and forest classification results, but they are mainly based on individual sensor data. How these features from different kinds of remotely sensed data with various spatial resolutions influence classification results is unclear. We conducted a comprehensively comparative analysis of spectral and spatial features from ZiYuan-3 (ZY-3), Sentinel-2, and Landsat and their fused datasets with spatial resolution ranges from 2 m, 6 m, 10 m, 15 m, and to 30 m, and topographic factors in influencing land-cover classification results in a subtropical forest ecosystem using random forest approach. The results indicated that the combined spectral (fused data based on ZY-3 and Sentinel-2), spatial, and topographical data with 2-m spatial resolution provided the highest overall classification accuracy of 83.5% for 11 land-cover classes, as well as the highest accuracies for almost all individual classes. The improvement of spectral bands from 4 to 10 through fusion of ZY-3 and Sentinel-2 data increased overall accuracy by 14.2% at 2-m spatial resolution, and by 11.1% at 6-m spatial resolution. Textures from high spatial resolution imagery play more important roles than textures from medium spatial resolution images. The incorporation of textural images into spectral data in the 2-m spatial resolution imagery improved overall accuracy by 6.0–7.7% compared to 1.1–1.7% in the 10-m to 30-m spatial resolution images. Incorporation of topographic factors into spectral and textural imagery further improved overall accuracy by 1.2–5.5%. The classification accuracies for coniferous forest, eucalyptus, other broadleaf forests, and bamboo forest can be 85.3–91.1%. This research provides new insights for using proper combinations of spectral bands and textures corresponding to specifically spatial resolution images in improving land-cover and forest classifications in subtropical regions.

scholarly journals Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam

2020 ◽  
Vol 12 (9) ◽  
pp. 1367 ◽  
Author(s):  
Huong Thi Thanh Nguyen ◽  
Trung Minh Doan ◽  
Erkki Tomppo ◽  
Ronald E. McRoberts
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Confusion Matrix ◽  
Support Vector ◽  
Remotely Sensed Data ◽  
K Nearest Neighbors ◽  
Tropical Regions ◽  
Multi Temporal ◽  
Sentinel 2

Information on land use and land cover (LULC) including forest cover is important for the development of strategies for land planning and management. Satellite remotely sensed data of varying resolutions have been an unmatched source of such information that can be used to produce estimates with a greater degree of confidence than traditional inventory estimates. However, use of these data has always been a challenge in tropical regions owing to the complexity of the biophysical environment, clouds, and haze, and atmospheric moisture content, all of which impede accurate LULC classification. We tested a parametric classifier (logistic regression) and three non-parametric machine learning classifiers (improved k-nearest neighbors, random forests, and support vector machine) for classification of multi-temporal Sentinel 2 satellite imagery into LULC categories in Dak Nong province, Vietnam. A total of 446 images, 235 from the year 2017 and 211 from the year 2018, were pre-processed to gain high quality images for mapping LULC in the 6516 km2 study area. The Sentinel 2 images were tested and classified separately for four temporal periods: (i) dry season, (ii) rainy season, (iii) the entirety of the year 2017, and (iv) the combination of dry and rainy seasons. Eleven different LULC classes were discriminated of which five were forest classes. For each combination of temporal image set and classifier, a confusion matrix was constructed using independent reference data and pixel classifications, and the area on the ground of each class was estimated. For overall temporal periods and classifiers, overall accuracy ranged from 63.9% to 80.3%, and the Kappa coefficient ranged from 0.611 to 0.813. Area estimates for individual classes ranged from 70 km2 (1% of the study area) to 2200 km2 (34% of the study area) with greater uncertainties for smaller classes.

Long-term change monitoring of natural grasslands ecosystem in support of SDG 15.3.1

2020 ◽  
Author(s):  
Cristina Tarantino ◽  
Maria Adamo ◽  
Palma Blonda
Keyword(s):  
Land Cover ◽  
Spatial Resolution ◽  
Protected Area ◽  
National Park ◽  
Land Abandonment ◽  
Support Vector ◽  
Natural Grasslands ◽  
Natural Grassland ◽  
Sentinel 2

<p>Assessing and maintaining the conservation of natural and semi-natural grassland ecosystems is one of the most important actions of the Biodiversity Strategy by the European Commission.</p><p>The present study focuses on the detection of long-term changes, from 1990 to 2018, of natural grasslands ecosystem, at local scale, in the “Murgia Alta”, a National Park as well as a Natura 2000 protected area, Southern Italy. The study site represents one of the largest areas for the conservation of such ecosystem in Italy. It is under pressure and in danger of destruction due to soil graining for agricultural intensification, illegal expansion of extraction sites, fires and land abandonment with consequent biodiversity loss.</p><p>Land Cover (LC) changes and class trends are one of the measures (sub-indicator) required for the implementation of the Sustainable Development Goals (SDG) 15.3.1 Indicator (“Proportion of land that is degraded over total land area”) of the Agenda 2030 by United Nations.</p><p>Multisource/multiresolution free available satellite data (visible, near infrared and short wave infrared spectral bands) were considered. Historical images from Landsat (4 images per year, one per season) were analyzed to produce different LC multiclass maps for 1990, 2001, 2004, 2011 and 2018 at 30 m spatial resolution, with an automatic data-driven classifier (Support Vector Machine). For 2018 Sentinel-2 data, 10 m spatial resolution, were also considered.</p><p>The mean value of the Overall Accuracies obtained for the LC maps from Landsat was 95%. Similar value was obtained in the last year from Sentinel-2.</p><p>Then natural grassland layer was extracted from those maps to analyze the trend of the grasslands ecosystem over time. The findings obtained indicate a total loss in the extension of the ecosystem of about 18% from 1990 to 2018. The major decrease (26%) occurred in 1990-2001. Then a modest decrease followed up to 2004 (year of institution of the National Park). Finally a slight increase probably due to land abandonment followed to fire events was quantified after 2004.</p><p>From the comparison of the different LC maps obtained, the decrease of natural grasslands resulted mainly due to transformation into agricultural areas.</p><p>In addition, these results are consistent with those obtained using Corine Land Cover maps available for the same period although at a coarser scale.</p><p>The SDG sub-indicator was evaluated inside the protected area and in a buffer, 10 km, area around. This sub-measure, which can be evaluated from time-series of satellite free data, can support long-term monitoring of protected area and can be used not only for the resilience evaluation of the study site to climate changes but also for the evaluation of conservation policies and as input to scenario modelling.</p>

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

Land Use and Land Cover Changes in Northern Kordofan State of Sudan: A Remotely Sensed Data Analysis

2014 ◽  
pp. 269-283 ◽  
Author(s):  
Mohamed S. Dafalla ◽  
Elfatih M. Abdel-Rahman ◽  
Khalid H. A. Siddig ◽  
Ibrahim S. Ibrahim ◽  
Elmar Csaplovics
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Land Cover ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Remotely Sensed Data
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