Detecting and mapping the spatial distribution of Chromoleana odorata invasions in communal areas of South Africa using Sentinel-2 multispectral remotely sensed data

2021 ◽  
pp. 103081
Author(s):  
Helen S. Ndlovu ◽  
Mbulisi Sibanda ◽  
John Odindi ◽  
Siphiwokuhle Buthelezi ◽  
Onisimo Mutanga
Keyword(s):  
South Africa ◽  
Spatial Distribution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Communal Areas ◽  
Sentinel 2

Modification of a groundwater prospect zone index using remotely sensed data and the analytic network process in the eastern part of Lesvos island, Greece.

2020 ◽  
Vol 11 (4) ◽  
pp. 126-143
Author(s):  
Terpsichori MITSI ◽  
◽  
Demetre ARGIALAS ◽  
Konstantinos VAMVOUKAKIS ◽  
◽  
...  
Keyword(s):  
Ground Water ◽  
Analytic Network Process ◽  
Remotely Sensed ◽  
Groundwater Potential ◽  
Landsat 8 ◽  
Groundwater Prospect ◽  
Remotely Sensed Data ◽  
Potential Index ◽  
Network Process ◽  
Sentinel 2

Because of climate change and overpopulation, the demand for water is increasing. Groundwater constitutes an alternative renewable source of aquifer, so the spatial distribution of ground water provides important information on its qualitative and quantitative status. This paper develops a methodology for delineating potential ground water zones using remotely sensed data and GIS. The developed methodology was based on the empirical index GPI (MGPI – Modified Groundwater Potential Index) and was applied to the eastern part of Lesvos Island, Greece. To evaluate the criteria used for the result, the Analytic Network Process (ANP) was applied to weight each parameter. The dataset used consists of satellite images derived from Sentinel 2 and Landsat 8, which were combined with vector and raster data, to create the necessary thematic layers. To validate the results, existing ground water zones from the Municipal Water Company of Lesvos were used.

Integrating field data

2010 ◽  
Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector
Keyword(s):  
South Africa ◽  
Habitat Use ◽  
Vegetation Type ◽  
Remotely Sensed ◽  
Battery Life ◽  
Clear Understanding ◽  
Remotely Sensed Data ◽  
Human Settlements ◽  
Ecological Gradient ◽  
Landsat 7

While the savannah elephant (Loxodonta africana) is listed by the International Union for Conservation of Nature (IUCN) as “vulnerable” because of declining abundance in some regions of Africa (Blanc 2008), populations in some protected areas of South Africa are growing rapidly (van Aarde and Jackson 2007). These populations can cause extensive modification of vegetation structure when their density increases (Owen-Smith 1996; Whyte et al. 2003; Guldemond and van Aarde 2007). Management methods such as culling, translocation, and birth control have not reduced density in some cases (van Aarde et al. 1999; Pimm and van Aarde 2001). Providing more space for elephants is one alternative management strategy, yet fundamental to this strategy is a clear understanding of habitat and landscape use by elephants. Harris et al. (2008) combined remotely sensed data with Global Positioning System (GPS) and traditional ethological observations to assess elephant habitat use across three areas that span the ecological gradient of historical elephant distribution. They explored influences on habitat use across arid savannahs (Etosha National Park in Namibia) and woodlands (Tembe Elephant Park in South Africa and Maputo Elephant Reserve in Mozambique). The researchers focused on three main variables—distance to human settlements, distance to water, and vegetation type. The authors used Landsat 7 ETMþ imagery to create vegetation maps for each location, employing supervised classification and maximum likelihood estimation. Across all sites, they recorded the coordinates of patches with different vegetation and of vegetation transitions to develop signatures for the maps. Elephants do not use all vegetation types, and it can be expedient to focus on presence rather than both presence and absence. Accordingly, the researchers used GPS to record the locations of elephants with the aim of identifying important land cover types for vegetation mapping. The authors mapped water locations in the wet and dry seasons using remotely sensed data and mapped human settlements using GPS, aerial surveys, and regional maps. They tracked elephants with radiotelemetry collars that communicated with the ARGOS satellite system, sending location data for most of the elephants over 24 h, and then remaining quiescent for the next 48 h to extend battery life.

scholarly journals Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 279 ◽  
Author(s):  
Ernest William Mauya ◽  
Joni Koskinen ◽  
Katri Tegel ◽  
Jarno Hämäläinen ◽  
Tuomo Kauranne ◽  
...  
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Remotely Sensed ◽  
Small Scale ◽  
Remotely Sensed Data ◽  
Weighted Mean ◽  
Alos Palsar ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

scholarly journals Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Gáspár Albert ◽  
Seif Ammar
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Geological Mapping ◽  
Vegetation Coverage ◽  
Remotely Sensed Data ◽  
Random Forest Classification ◽  
Radar Images ◽  
Forest Classification ◽  
Geological Map ◽  
Sentinel 2

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

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.

Sign in / Sign up

Export Citation Format

Share Document