Estimation of changes of vegetation cover in Sundarban using multi-temporal satellite data

Vegetation measures are crucial for assessing changes in the ecological environment. Fractional vegetation cover (FVC) provides information on the growth status, distribution characteristics, and structural changes of vegetation. An in-depth understanding of the dynamic changes in urban FVC contributes to the sustainable development of ecological civilization in the urbanization process. However, dynamic change detection of urban FVC using multi-temporal remote sensing images is a complex process and challenge. This paper proposed an improved FVC estimation model by fusing the optimized dynamic range vegetation index (ODRVI) model. The ODRVI model improved sensitivity to the water content, roughness degree, and soil type by minimizing the influence of bare soil in areas of sparse vegetation cover. The ODRVI model enhanced the stability of FVC estimation in the near-infrared (NIR) band in areas of dense and sparse vegetation cover through introducing the vegetation canopy vertical porosity (VCVP) model. The verification results confirmed that the proposed model had better performance than typical vegetation index (VI) models for multi-temporal Landsat images. The coefficient of determination (R2) between the ODRVI model and the FVC was 0.9572, which was 7.4% higher than the average R2 of other typical VI models. Moreover, the annual urban FVC dynamics were mapped using the proposed improved FVC estimation model in Hefei, China (1999–2018). The total area of all grades FVC decreased by 33.08% during the past 20 years in Hefei, China. The areas of the extremely low, low, and medium grades FVC exhibited apparent inter-annual fluctuations. The maximum standard deviation of the area change of the medium grade FVC was 13.35%. For other grades of FVC, the order of standard deviation of the change ratio was extremely low FVC > low FVC > medium-high FVC > high FVC. The dynamic mapping of FVC revealed the influence intensity and direction of the urban sprawl on vegetation coverage, which contributes to the strategic development of sustainable urban management plans.

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Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000

Estuarine Coastal and Shelf Science ◽

10.1016/j.ecss.2006.12.019 ◽

2007 ◽

Vol 73 (1-2) ◽

pp. 91-100 ◽

Cited By ~ 222

Author(s):

Chandra Giri ◽

Bruce Pengra ◽

Zhiliang Zhu ◽

Ashbindu Singh ◽

Larry L. Tieszen

Keyword(s):

Satellite Data ◽

Forest Dynamics ◽

Mangrove Forest ◽

Multi Temporal ◽

The Sundarbans

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A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

Reports on Geodesy and Geoinformatics ◽

10.1515/rgg-2016-0004 ◽

2016 ◽

Vol 100 (1) ◽

pp. 27-38 ◽

Cited By ~ 1

Author(s):

Grazia Caradonna ◽

Antonio Novelli ◽

Eufemia Tarantino ◽

Raffaela Cefalo ◽

Umberto Fratino

Keyword(s):

Land Cover ◽

Satellite Data ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Geospatial Data ◽

Usage Patterns ◽

Mediterranean Regions ◽

Multi Temporal ◽

Using Data ◽

Set Up

Abstract Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

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Early stage variable rate nitrogen fertilization of silage maize driven by multi-temporal clustering of archive satellite data

Advances in Animal Biosciences ◽

10.1017/s2040470017000103 ◽

2017 ◽

Vol 8 (2) ◽

pp. 288-292 ◽

Cited By ~ 5

Author(s):

R. Casa ◽

F. Pelosi ◽

S. Pascucci ◽

F. Fontana ◽

F. Castaldi ◽

...

Keyword(s):

Satellite Data ◽

Nitrogen Fertilization ◽

Early Stage ◽

Central Italy ◽

N Fertilization ◽

Yield Potential ◽

Variable Rate ◽

Landsat Images ◽

Silage Maize ◽

Multi Temporal

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

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