Utilizing TVDI and NDWI to Classify Severity of Agricultural Drought in Chuping, Malaysia

Abstract

Agricultural drought is crucial in understanding the relationship to crop production functions which can be monitored using satellite remote sensors. The aim of this research is to combine temperature vegetation dryness index (TVDI) and normalized difference water index (NDWI) classifications for identifying drought areas in Chuping, Malaysia which has regularly recorded high temperatures. TVDI and NDWI are assessed using three images of the dry spell period in March for the years 2015, 2016 and 2017. NDWI value representing water content in vegetation decreases numerically to −0.39, −0.37 and −0.36 for the year 2015, 2016 and 2017. Normalized difference vegetation indices (NDVI) values representing vegetation health status in the given area for images of years 2015 to 2017 decreases significantly (p ≤ 0.05) from 0.50 to 0.35 respectively. Overall, TVDI in the Chuping area showed agricultural drought with an average value of 0.46. However, Kilang Gula Chuping area in Chuping showed a significant increase in dryness for all of the three years assessed with an average value of 0.70. When both TVDI and NDWI were assessed, significant clustering of spots in Chuping, Perlis for all the 3 years was identified where geographical local regressions of 0.84, 0.70 and 0.70 for the years 2015, 2016 and 2017 was determined. Furthermore, Moran’s I values revealed that the research area had a high I value of 0.63, 0.30 and 0.23 with respective Z scores of 17.80, 8.63 and 6.77 for the years 2015, 2016 and 2017, indicating that the cluster relationship is significant in the 95–99 percent confidence interval. Using both indices alone was sufficient to understand the drier spots of Chuping over 3 years. The findings of this research will be of interest to local agriculture authorities, like plantation and meteorology departments to understand drier areas in the state to evaluate water deficits severity and cloud seeding points during drought.

Vegetation Effects on Soil Moisture Retrieval from Water Cloud Model Using PALSAR-2 for Oil Palm Trees

“…This gave an average of 0.368 for all three observation dates. It can be noted that the study area faced drought stress in the earlier months of the calendar year where January to March recorded a lower rainfall [64]. Normalized plant water content was similar in all the observations at an average of 0.199%.…”

“…The site was considered flat topography with an elevation of 21.6 m while the slope class was identified as 4-12% [63]. As per the study site, the months of February to March of the calendar year are regarded as the driest season of the year; with a maximum of 28 days of dry spell every year and an average of 1362.38 mm of precipitation per year in the years of 2015-2017 [64]. In the PALSAR-2 image acquisition months, Table 1 shows the monthly meteorological data obtained from Malaysian Meteorological Department.…”

Mapping Sugarcane in Central India with Smartphone Crowdsourcing

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“…In India, crop mapping efforts have mostly focused on rice, an important staple food crop which occupies the largest share of cultivated land in the country and plays a key role in the country's groundwater depletion problem [11,12], with sugarcane receiving less attention. Some phenological characteristics of sugarcane also make mapping it quite challenging, such as a long and variable growing period of 12-18 months, different sowing and harvesting periods for different sugarcane varieties, and an extended harvest period lasting several months [10,13,14].…”

Estimativa do TVDI para avaliação das condições hídricas em áreas de produção de cana-de-açúcar no semiárido brasileiro.

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