Ms. Dildora Aralova Profile

Dildora Aralova

at TU Dresden

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Author

Publications (4)

Proceedings Article | 25 October 2016 Paper

Remote sensing-based vegetation indices for monitoring vegetation change in the semi-arid region of Sudan

Majdaldin R. A., B. Osunmadewa, E. Csaplovics, D. Aralova

Proceedings Volume 9998, 99981X (2016) https://doi.org/10.1117/12.2242002

KEYWORDS: Vegetation, Agriculture, Climatology, Remote sensing, Landsat, Climate change, Earth observing sensors, Image enhancement, Near infrared, Environmental sensing

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Proceedings Article | 25 October 2016 Paper

Regional assessment of trends in vegetation change dynamics using principal component analysis

B. Osunmadewa, E. Csaplovics, Majdaldin R. A., C. Adeofun, D. Aralova

Proceedings Volume 9998, 99981Y (2016) https://doi.org/10.1117/12.2242011

KEYWORDS: Vegetation, Principal component analysis, Remote sensing, Radiometry, Ecosystems, Composites, Agriculture, Climatology, Spatial resolution, Climate change

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Proceedings Article | 18 October 2016 Paper

Environmental resilience of rangeland ecosystems: assessment drought indices and vegetation trends on arid and semi-arid zones of Central Asia

Dildora Aralova, Kristina Toderich, Ben Jarihani, Dilshod Gafurov, Liliya Gismatulina, Babatunde Osunmadewa, Majdaldin Rahamtallah Abualgasim

Proceedings Volume 10005, 100050R (2016) https://doi.org/10.1117/12.2242563

KEYWORDS: Vegetation, Soil science, Ecosystems, Remote sensing, Climatology, Positron emission tomography, Agriculture, Temperature metrology, Climate change, Data modeling

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Proceedings Article | 18 October 2016 Presentation + Paper

Monitoring of vegetation condition using the NDVI/ENSO anomalies in Central Asia and their relationships with ONI (very strong) phases

Dildora Aralova, Kristina Toderich, Ben Jarihani, Dilshod Gafurov, Liliya Gismatulina

Proceedings Volume 10005, 1000512 (2016) https://doi.org/10.1117/12.2242164

KEYWORDS: Vegetation, Electroluminescence, Lanthanum, Climatology, Temperature metrology, Ecosystems, Agriculture, Satellites, Radiometry, Data acquisition

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An investigation of temporal dynamics of El Niño–Southern Oscillation (ENSO) and spatial patterns of dryness/wetness period over arid and semi-arid zones of Central Asia and their relationship with Normalized Difference Vegetation Index (NDVI) values (1982-2011) have explored in this article. For identifying periodical oscillations and their relationship with NDVI values have selected El Nino 3.4 index and thirty years of new generation bi-weekly NDVI 3g acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites time-series data. Based on identification ONI (Oceanic Nino Index) is a very strong El Nino (warm) anomalies observed during 1982-1983, 1997-1998 and very strong La Nino (cool) period events have observed 1988-1989 years. For correlation these two factors and seeking positive and negative trends it has extracted from NDVI time series data as “low productivity period” following years: 1982-1983, 1997 -1998; and as “high productivity period” following years: 1988 -1989. Linear regression observed warm events as moderate phase period selected between moderate El Nino (ME) and NDVI with following periods:1986-1987; 1987-1988; 1991-1992; 2002-2003; 2009-2010; and moderate La Niña (ML) periods and NDVI (1998-1999; 1999-2000; 2007-2008) which has investigated a spatial patterns of wetness conditions. The results indicated that an inverse relationship between very strong El Nino and NDVI, decreased vegetation response with larger positive ONI value; and direct relationship between very strong La Niña and NDVI, increased vegetation response with smaller negative ONI value. Results assumed that significant impact of these anomalies influenced on vegetation productivity. These results will be a beneficial for efficient rangeland/grassland management and to propose drought periods for assessment and reducing quantity of flocks’ due to a lack of fodder biomass for surviving livestock flocks on upcoming years in rangelands. Also results demonstrate that a non-anthropogenic drivers of variability effected to land surface vegetation signals, understanding of which will be beneficial for efficient rangeland and agriculture management and establish ecosystem services in precipitation-driven drylands of Central Asia.

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