Analysis of Agricultural Drought in East Java Using Vegetation Health Index

Amalo, Luisa Febrina; Hidayat, Rahmat; Sulma, Sayidah

doi:10.17503/agrivita.v40i1.1080

Cited by 12 publications

(14 citation statements)

References 11 publications

(19 reference statements)

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“…The index originally used long-term data from the NOAA-AVHRR spacecraft and has recently been used with the Visible Infrared Imaging Radiometer Suite (VIIRS) spectral bands [55] as well. VHI has been applied on a regional scale in many areas around the world [56][57][58], but has also been proposed as a global drought index [55,[59][60][61]. Global coverage of the VHI is updated weekly on an official NOAA webpage (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/vhbrowse.php).…”

Section: Relation To Lst-ndvi Modelsmentioning

confidence: 99%

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

et al. 2019

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Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST-NDVI relations over Europe, using long-term (2000Europe, using long-term ( -2017 MODIS images. Overlaying the LST-NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March-August). During the spring season (March-May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June-August), most of the biomes-except the northern ones-turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST-NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. IntroductionIn terrestrial ecosystems, vegetation growth-limiting factors (VGLFs) are species-specific values attributed to a cause that constrains the biological response to appropriate environmental conditions [1]. The primary VGLFs are either climatic or terrestrial. The climatic constraints include water, energy, light, relative humidity, wind, and atmospheric elements (e.g., gases). Climatic variables directly and indirectly affect all levels of biodiversity, from the individual species through to population, community, ecosystem, and up to biome scales [2]. Terrestrial VGLFs include soil characteristics (e.g., texture, depth, and nutrients) and terrain properties (e.g., elevation, slope, and aspect). Different VGLFs should be in balance in order to allow optimal growth and development. When one or more of these factors exceed a low o...

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Section: Relation To Lst-ndvi Modelsmentioning

confidence: 99%

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

et al. 2019

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“…The review can be used to analyze the strength and weaknesses of the method that could be improved in the next study. The monitoring of drought in Indonesia from remote sensing data has been carried out based on the VHI index with MODIS and TRMM images on an island-scale by Roswintiarti et al [63] and on provincial-scale by Amalo et al [64]. Dirgahayu [19] also use MODIS and TRMM images at the regency-scale based on a combination of drought types.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing and GIS application for monitoring drought vulnerability in Indonesia: a review

Rahmi

Dimyati

2021

Bulletin EEI

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Agricultural drought is one of the hydrometeorological disasters that cause significant losses because it affects food stocks. In addition, agricultural droughts, impact the physical and socio-economic development of the community. Remote sensing technology is used to monitor agricultural droughts spatially and temporally for minimizing losses. This study reviewed the literatures related to remote sensing and GIS for monitoring drought vulnerability in Indonesia. The study was conducted on an island-scale on Java Island, a provincial-scale in East Java and Bali, and a district-scale in Indramayu and Kebumen. The dominant method was the drought index, which involves variable land surface temperature (LST), vegetation index, land cover, wetness index, and rainfall. Each study has a strong point and a weak point. Low-resolution satellite imagery has been used to assess drought vulnerability. At the island scale, it provides an overview of drought conditions, while at the provincial scale, it focuses on paddy fields and has little detailed information. In-situ measurements at the district scale detect meteorological drought accurately, but there were limitations in the mapping unit's detailed information. Drought mapping using GIS and remote sensing at the district scale has detailed spatial information on climate and physiographic aspects, but it needs temporal data monitoring.

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“…Climate related studies in Indonesia showed that extreme weather events are closely related to various natural disasters, such as drought (King et al, 2016;Surmaini et al, 2018;Taufik et al, 2020Taufik et al, , 2017, floods (Lestari and Dasanto, 2019;Narulita and Ningrum, 2018), and frost (Pradana et al, 2018a(Pradana et al, , 2018b. The disasters influence many sectors in Indonesia specially the agricultural sector (Amalo et al, 2017;Kim et al, 2019;Kuswanto et al, 2019;Lesk et al, 2016;Mulyaqin, 2020), which indirectly affect national economic conditions (Boer and Surmaini, 2020;Chatzopoulos et al, 2020). Extreme weather events mostly influence by regional climate variability, which periodically occur (Newman et al, 2018;Veitch et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Frost Predictions in Dieng using the Outputs of Subseasonal to Seasonal (S2S) Model

Aini

Faqih

2021

J.Agromet

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Dieng volcanic highland, where located in Wonosobo and Banjarnegara regencies, has a unique frost phenomenon that usually occurs in the dry season (July, August, and September). This phenomenon may attract tourism, but it has caused losses to farmers due to crop damage. Information regarding frost prediction is needed in order to minimize the negative impact of this extreme event. This study evaluates the potential use of the Subseasonal to Seasonal (S2S) forecast dataset for frost prediction, with a focus on two areas where frost usually occurs, i.e. the Arjuna Temple and Sikunir Hill. Daily minimum air temperature data used to predict frost events was from the outputs of the ECMWF model, which is one of the models contributed in the Subseasonal to Seasonal prediction project (S2S). The minimum air temperature observation data from the Banjarnegara station was used in conjunction with the Digital Elevation Model Nasional (DEMNAS) data to generate spatial data based on the lapse rate function. This spatial data was used as a reference to downscale the ECMWF S2S data using the bias correction approach. The results of this study indicated that the bias-corrected data of the ECMWF S2S forecast was able to show the spatial pattern of minimum air temperature from observations, especially during frost events. The S2S prediction represented by the bias-corrected ECMWF model has the potential for providing early warning of frost events in Dieng, with a lead time of more than one month before the event.

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Analysis of Agricultural Drought in East Java Using Vegetation Health Index

Cited by 12 publications

References 11 publications

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Spatial and Seasonal Patterns in Vegetation Growth-Limiting Factors over Europe

Remote sensing and GIS application for monitoring drought vulnerability in Indonesia: a review

Frost Predictions in Dieng using the Outputs of Subseasonal to Seasonal (S2S) Model

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