Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image

Abstract

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers.

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

“…The difference in the calculated average OA at the pixel level was lower than 10%. These results are in accordance with similar studies that report on the successful application of object-based frameworks for crop type [9,24,26,27] or vegetation/tree species [1,3,4] classification. Classifying (even with a relatively small size of foliage segments) canopy objects instead of pixels can provide a more compact representation regarding the distinctive properties between different varieties/classes that a classifier searches for decision making.…”

“…To this end, recent studies have employed high and very high resolution satellite imagery towards vegetation, forest/tree mapping [1][2][3][4], biomass and structural parameters estimation [5][6][7] and crop type mapping and identification [8,9]. Moreover, crop-based and variety-based data analysis can create valuable validated agricultural maps and products for the implementation of effective management decisions [10,11].…”

Analysis and Mapping of the Spectral Characteristics of Fractional Green Cover in Saline Wetlands (NE Spain) Using Field and Remote Sensing Data

“…The implementation of this network requires the monitoring and management of habitats and stresses the urgent need for updated information on habitat conservation status. Remote sensing can provide useful data for mapping and monitoring protected habitats [1][2][3][4][5] and assessing vegetation cover at detailed scales using aerial ortho-images, or high resolution airborne or satellite imagery such as Quickbird, Ikonos [6][7][8][9][10], or the most recent Worldview and Geo-Eye satellites [11][12][13][14], and to establish a relationship between vegetation cover, type or biological crust, and its phenological state using satellite data [15,16].…”

“…Laliberte et al [23] obtained an underestimation of total and senescent vegetation by 5% using digital ground photographs and object-based classification of Quickbird images in arid regions of the southwestern US. Spectral unmixing techniques have been applied in a variety of environments and scales, using fine and coarse resolution imagery such as Landsat to estimate sub pixel proportions of ground components [13,[28][29][30][31][32][33].…”

WorldView-2 sensor for the detection of hematite and goethite in tropical soils

“…The WorldView-2 is a high-resolution sensor that has stood out because it offers 8 bands covering the band of visible and near infrared, besides pixels with spatial resolution of the order of 50 cm, with 11 bits of radiometric resolution. In addition to a new coastal blue band (0.4-0.45 μm), developed for aquatic ecosystems studies, it has a band in yellow (0.585-0.625 μm), and one in the red border (0.705-0.745 μm), both for biophysical vegetation studies (Mutanga et al, 2012;Consoli & Vanella, 2014;Rapinel et al, 2014;Kokaly & Skidmore, 2015;Marshall & Thenkabail, 2015;Hugue et al, 2016).…”