Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery

Özelkan, Emre; Chen, Gang; Ustundag, Burak Berk

doi:10.1016/j.jag.2015.08.003

Cited by 60 publications

(34 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The strongest relationship between SPI and NDVI was found in the areas with low soil water-holding capacity (Ji and Peters, 2003). Also, the relationship between these two indices differs in various agricultural areas: a positive correlation between SPI and NDVI was determined in the rain-fed areas, while a negative correlation was determined in the irrigated areas (Ozelkan et al, 2016). The SPI indicates moisture conditions and the vegetation reacts to the lack of precipitations with some delay.…”

Section: Introductionmentioning

confidence: 99%

“…The SPI indicates moisture conditions and the vegetation reacts to the lack of precipitations with some delay. For this reason, the strongest link was established between the SPI values in spring and NDVI values in summer, which means that spring watering is critically important for the growth of the most plants (Ozelkan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…NDVI is frequently analysed by calculating Vegetation Condition Index (VCI), which compares the current NDVI to the observed values of this index in previous years (Gebrehiwot et al, 2011;Ozelkan et al, 2016) and has a good correlation with the SPI values (Dutta et al, 2015). In different regions of the world, the relationship between the 3- (Gebrehiwot et al, 2011), 6-or 9-month SPI (Quiring and Ganesh, 2010) and VCI values were established.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Drought identification in the eastern Baltic region using NDVI

et al. 2017

View full text Add to dashboard Cite

Abstract. Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53-60 • N and 20-30 • E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1-to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5-6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3-and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Drought identification in the eastern Baltic region using NDVI

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The value range of NDVI is −1 to 1, negative values of NDVI may point to water bodies and snow cover, while values greater than 0.5 indicate dense vegetation cover [34].…”

Section: Ndvi Calculation Using Landsatmentioning

confidence: 99%

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

2019

View full text Add to dashboard Cite

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements.

show abstract

“…The Standardized Precipitation Index (SPI) is considered as an appropriate index due to its simple calculation, availability of its input data across the world, flexibility of time scale, and comparability of droughts by time and space (Hayes et al, 1999;Guttman, 1999;Mishra and Singh, 2010;Moreira, 2015;Mundetia and Sharma, 2015;Ozelkan et al, 2016). The SPI is calculated based on the precipitation probability in different time scales.…”

Section: Introductionmentioning

confidence: 99%

Modeling a Drought Index Using a Nonparametric Approach

2017

Global NEST Journal

View full text Add to dashboard Cite

Alternatively, to other studies that used parametric distributions (e.g. Gamma) in the estimation of the Standardized Precipitation Index (SPI), this study aims to apply a nonparametric method based on Kernel Density Estimator (KDE) for calculating the SPI. Results of the proposed method were compared with the ones from the most widely used parametric distribution, using a long dataset of monthly precipitation of four meteorological stations in Iran (including Bushehr, Mashhad, Tehran and Esfahan) over a period of 107 water years . The capability of KDE-based SPI was compared with the Gamma-based SPI at four-time scales of 3, 6, 9 and 12 months. The frequencies of the drought classes of SPI were calculated and compared with corresponding expected frequencies. The results revealed that the KDE is more consistent with the expected values of the SPI drought/wet classes frequencies (especially in the extreme classes) at all stations as well as at the four-time scales, compared to the Gamma distribution. The greatest deviation from the expected frequencies for KDE and Gamma distribution were about 10% and 150%, respectively. This study proposes a new analytical approach in modeling SPI that provides more accurate results pertaining frequency of occurrences of extreme drought events. The output of the study can be used in many fields (e.g. tourism, agriculture, insurance, etc.) that are influenced by severe droughts.

show abstract

Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery

Cited by 60 publications

References 40 publications

Drought identification in the eastern Baltic region using NDVI

Drought identification in the eastern Baltic region using NDVI

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

Modeling a Drought Index Using a Nonparametric Approach

Contact Info

Product

Resources

About