Application of LISS III and MODIS-derived vegetation indices for assessment of micro-level agricultural drought

Palchaudhuri, Moumita; Biswas, Sujata

doi:10.1016/j.ejrs.2019.12.004

Cited by 19 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vegetation cover has a critical role in climate change by sequestering, or storing, large quantities of carbon. Several climate research projects focused on the quantification of vegetation cover to study the drought severity [1][2][3]. Moreover, it has been used to investigate a relationship with soil [4], temperature and urban form [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Mustafa¹

2020

JASTT

View full text Add to dashboard Cite

The rapidly and wide use of remote sensing and accurately obtain information on the spatiotemporal distribution of large-scale vegetation is of great significance for improving and managing the Environment. To assess and analyze the spatiotemporal variation of vegetation status in Kurdistan Region of Iraq (KRGI), we used time series NDVI-based vegetation that are extracted from MOD13Q1 MODIS product over 20 years (2000 - 2019). The results showed that vegetation was mainly distributed in the north-east to south-east of the KRGI, while west region has less distributed and almost no vegetation. This is clearly remarkable in the south-west part of the region (Garmian administration). While, the most dominated vegetation province was Duhok province in KRGI during study period. There is a noticeable temporal variation in vegetation over a period of 20-year in the KRGI. The lower vegetated cover area is observed in the years 2000, 2008, and 2009. The increase/decrease of vegetated cover area is not only effected by climate conditions. The anthropogenic resource is also one of the main resources that has a major influence on the increase/decrease of vegetation.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Mustafa¹

2020

JASTT

View full text Add to dashboard Cite

show abstract

“…These data are produced in a resolution of 250 m by choosing the most reliable pixel value among daily values within 16 days. The low percentage of cloud coverage, low view angle, and the highest NDVI value are among the applied selection criteria [32,33]. In this study, we used the period between 28 July and 12 August within the fire season of 2018.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…These products rely on an interpolation method using historical data collected by 60,000 weather stations around the globe. Furthermore, remotely sensed data like maximum and minimum land surface temperature and cloud cover, as obtained via the MODIS satellite platform, were used as satellite-derived covariates for accuracy improvement [33]. Fick and Hijmans provide further details about the production process of remotely sensed data via Google Earth Engine in their previous work [34].…”

Section: Data Acquisitionmentioning

confidence: 99%

Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

et al. 2021

View full text Add to dashboard Cite

We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk.

show abstract

“…These data are produced in a resolution of 250m by choosing the most reliable pixel value among daily values within 16 days. The low percentage of cloud coverage, low view angle, and the highest NDVI value are among the applied selection criteria 31,32 . In this study, we used the period between 28 July and 12 August within the fire season of 2018.…”

Section: Normalized Difference Vegetation Index (Ndvi) Data Is Extracmentioning

confidence: 99%

A Practical Approach to Assess the Wildfire Ignition and Spreading Capacities of Vegetated Areas at Landscape-scale

Hysa¹,

Spalević²,

Dudić³

et al. 2021

Preprint

View full text Add to dashboard Cite

We bring a practical and comprehensive GIS-based framework to utilize freely available remote sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by the consequences of climate change. We utilize the Wildfire Ignition Probability/ Wildfire Spreading Capacity Index (WIPI/ WSCI). WIPI/ WSCI models rely on a multi-criteria data mining procedure assessing the social, environmental, geophysical, and fuel properties of the study area based on open access remote sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion's impact factor and assess the model's overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland-urban interface of Bucharest's metropolitan area, there is a remarkable building stock like healthcare, residential and educational that are significantly exposed to wildfire spreading the risk.

show abstract

Application of LISS III and MODIS-derived vegetation indices for assessment of micro-level agricultural drought

Cited by 19 publications

References 14 publications

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

A Practical Approach to Assess the Wildfire Ignition and Spreading Capacities of Vegetated Areas at Landscape-scale

Contact Info

Product

Resources

About