scholarly journals The response of vegetation cover and dune activity to rainfall, drought and fire observed by multitemporal satellite imagery

2019 ◽  
Vol 44 (15) ◽  
pp. 2957-2967 ◽  
Author(s):  
Adrian Fisher ◽  
Paul P. Hesse
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Dune Activity

STUDYING OF TERSKO-SULAKSKAYA LOWLAND VEGETATION UNDER THE RESULTS OF GROUND-BASED RESEARCH AND AUTOMATED INTERPRETATION

1970 ◽  
pp. 29-32
Author(s):  
M. I. Dzhalalova ◽  
A. B. Biarslanov ◽  
D. B. Asgerova
Keyword(s):  
Plant Communities ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Impacts ◽  
Species Abundance ◽  
Remote Sensing Data ◽  
Indigenous Communities ◽  
The State ◽  
Anthropogenic Factors ◽  
Internet Resource

The state of plant communities in areas located in the Tersko-Sulak lowland was studied by assessing phytocenotic indicators: the structure of vegetation cover, projective cover, species diversity, species abundance and elevated production, as well as automated decoding methods. There are almost no virgin soils and natural phytocenoses here; all of them have been transformed into agrocenoses (irrigated arable lands and hayfields, rice-trees and pastures). The long-term impact on pasture ecosystems of natural and anthropogenic factors leads to significant changes in the indigenous communities of this region. Phytocenoses are formed mainly by dry-steppe types of cereals with the participation of feather grass, forbs and ephemera, a semi-desert haloxerophytic shrub - Taurida wormwood. At the base of the grass stand is common coastal wormwood and Taurida wormwood - species resistant to anthropogenic influences. Anthropogenic impacts have led to a decrease in the number of species of feed-rich grain crops and a decrease in the overall productivity of pastures. Plant communities in all areas are littered with ruderal species. The seasonal dynamics of the land cover of the sites was estimated by the methods of automatic decoding of satellite images of the Landsat8 OLI series satellite for 2015, dated by the periods: spring - May 20, summer - July 23, autumn - October 20. Satellite imagery data obtained by Landsat satellite with a resolution in the multispectral image of 30 m per pixel, and in the panchromatic image - 10 m per pixel, which correspond to the requirements for satellite imagery to assess the dynamics of soil and vegetation cover. Lower resolution data, for example, NDVI MODIS, does not provide a reliable reflection of the state of soil and vegetation cover under arid conditions. In this regard, remote sensing data obtained from the Internet resource https://earthexplorer.usgs.gov/ was used.

scholarly journals Monitoring scrub weed change in the Canterbury region using satellite imagery

2007 ◽  
Vol 60 ◽  
pp. 137-140 ◽  
Author(s):  
J.D. Shepherd ◽  
J.R. Dymond ◽  
J.R.I. Cuff
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Satellite Image ◽  
Visual Assessment ◽  
Woody Vegetation ◽  
Spatial Change ◽  
Valid Data ◽  
Woody Cover ◽  
Landsat Satellite ◽  
Thematic Data

The spatial change of woody vegetation in the Canterbury region was automatically mapped between 1990 and 2001 using Landsat satellite image mosaics The intersection of valid data from these mosaics gave coverage of 84 of the Canterbury region Changes in woody cover greater than 5 ha were identified Of the 5 ha areas of woody change only those that were likely to have been a scrub change were selected using ancillary thematic data for current vegetation cover (eg afforestation and deforestation were excluded) This resulted in 2466 polygons of potential scrub change These polygons were rapidly checked by visual assessment of the satellite imagery and assigned to exotic or indigenous scrub change categories Between 1990 and 2001 the total scrub weed area in the Canterbury region increased by 3600 400 ha and indigenous scrub increased by 2300 400 ha

scholarly journals Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020 ◽  
Vol 12 (20) ◽  
pp. 3376 ◽  
Author(s):  
Giovanni Romano ◽  
Giovanni Francesco Ricci ◽  
Francesco Gentile
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Mountain Stream ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

scholarly journals Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

2019 ◽  
Vol 8 (2) ◽  
pp. 86 ◽  
Author(s):  
Ping Liu ◽  
Xi Chen
Keyword(s):  
Support Vector Machine ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Support Vector ◽  
Sufficient Information ◽  
Classification Result ◽  
Rotation Forest ◽  
Svm Algorithm ◽  
Multi Temporal ◽  
Occurrence Matrix

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area.

scholarly journals MANGROVE FOREST CHANGE IN NUSA PENIDA MARINE PROTECTED AREA, BALI - INDONESIA USING LANDSAT SATELLITE IMAGERY

2019 ◽  
Vol 15 (2) ◽  
pp. 141 ◽  
Author(s):  
August Daulat ◽  
Widodo Setiyo Pranowo ◽  
Syahrial Nur Amri
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Protected Area ◽  
Mangrove Forest ◽  
Vegetation Index ◽  
Marine Protected Area ◽  
Mangrove Forests ◽  
Forest Change ◽  
Landsat Satellite ◽  
The Impact

Nusa Penida, Bali was designated as a Marine Protected Area (MPA) by the Klungkung Local Government in 2010 with support from the Ministry of Marine Affairs and Fisheries, Republic of Indonesia. Mangrove forests located in Nusa Lembongan Island inside the Nusa Penida MPA jurisdiction have decreased in biomass quality and vegetation cover. It’s over the last decades due to influences from natural phenomena and human activities, which obstruct mangrove growth. Study the mangrove forest changes related to the marine protected areas implementation are important to explain the impact of the regulation and its influence on future conservation management in the region. Mangrove forest in Nusa Penida MPA can be monitored using remote sensing technology, specifically Normalized Difference Vegetation Index (NDVI) from Landsat satellite imagery combined with visual and statistical analysis. The NDVI helps in identifying the health of vegetation cover in the region across three different time frames 2003, 2010, and 2017. The results showed that the NDVI decreased slightly between 2003 and 2010. It’s also increased significantly by 2017, where a mostly positive change occurred landwards and adverse change happened in the middle of the mangrove forest towards the sea.

The estimation and validation of fractional vegetation cover based on GaoFen-4 satellite imagery

2017 ◽  
Author(s):  
Yuanheng Sun ◽  
Huazhong Ren ◽  
Gongqi Zhou ◽  
Tianyuan Zhang ◽  
Chengye Zhang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Fractional Vegetation Cover

scholarly journals Use of satellite imagery to identify vegetation cover changes following the Waldo Canyon Fire event, Colorado, 2012-2013

2014 ◽  
Author(s):  
Christopher J. Cole ◽  
Beverly A. Friesen ◽  
Earl M. Wilson
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Fire Event
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