ASSESSMENT OF AGRICULTURAL DROUGHT USING REMOTE SENSING BASED ON MODIS NDVI FOR DIFFERENT AGRO-CLIMATIC ZONES OF TAMIL NADU, INDIA

2020 ◽  
Author(s):  
Venkadesh Samykannu ◽  
◽  
S. Pazhanivelan ◽  
P.J. Prajesh ◽  
K.P. Ragunath ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tamil Nadu ◽  
Agricultural Drought ◽  
Climatic Zones ◽  
Modis Ndvi

scholarly journals Assessment of Agricultural Drought using MODIS NDVI based Vegetation Status for Different Agro Climatic Zones of Tamil Nadu

2019 ◽  
Vol 8 (05) ◽  
pp. 2204-2212
Author(s):  
S. Venkadesh ◽  
S. Pazhanivelan ◽  
K.P. Ragunath ◽  
R. Kumaraperumal ◽  
S. Panneerselvam ◽  
...  
Keyword(s):  
Tamil Nadu ◽  
Agricultural Drought ◽  
Climatic Zones ◽  
Modis Ndvi

Agricultural drought monitoring in semi-arid and arid areas using MODIS data

2011 ◽  
Vol 149 (4) ◽  
pp. 403-414 ◽  
Author(s):  
A. SHAHABFAR ◽  
J. EITZINGER
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Winter Wheat ◽  
Drought Index ◽  
Climatic Conditions ◽  
Agricultural Drought ◽  
Crop Evapotranspiration ◽  
Climatic Zones ◽  
Reference Crop ◽  
Semi Arid

SUMMARYThe performances of two remote sensing drought indices were evaluated at selected agricultural sites in different agro-climatic zones in Iran to detect the severity of drought phenomena related to temporal variation and different climatic conditions. The indices used were the perpendicular drought index (PDI) and the modified perpendicular drought index (MPDI), which are derived from moderate resolution imaging spectroradiometer (MODIS) satellite images (MOD13A3 V005). The correlations between these perpendicular indices and two other remote sensing indices in ten different agro-climatic zones of Iran from February 2000 to December 2005 were analysed. The additional indices evaluated were the enhanced vegetation index (EVI) and the vegetation condition index (VCI) along with five water balance parameters, including climatic water balance (CL), crop water balance (CR), monthly reference crop evapotranspiration (ET0), crop evapotranspiration (ETc) and required irrigation water (I). Winter wheat was selected as the reference crop because it is grown in the majority of climatic conditions in Iran.The results show that in several climatic regions, there is a statistically significant correlation between PDI and MPDI and the water balance parameters, indicating an acceptable performance in detecting crop drought stress conditions. In all zones except at the sites located in northwest and northeast of Iran, VCI and EVI are less correlated with the applied water balance indicators compared to PDI and MPDI. In a temporal analysis, PDI and MPDI showed a greater ability to detect CR conditions than VCI and EVI in the most drought-sensitive winter wheat-growing stages. Since Iran is characterized by arid or semi-arid climatic conditions and winter wheat is a major agricultural crop, a combination of both PDI and MPDI could be used as simple remote sensing-based tool to map drought conditions for crops in Iran and in other developing countries with similar climatic conditions.

Evaluation of Gloriosa superba for Yield Attributing Characters and Quantification of Colchicine Originated from Different Agro Climatic Zones of Tamil Nadu and Andhra Pradesh

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Arun Kumar P. ◽  
Elangaimannan R.
Keyword(s):  
Seed Yield ◽  
Tamil Nadu ◽  
High Performance ◽  
Andhra Pradesh ◽  
Crop Improvement ◽  
Mean Value ◽  
Hplc Method ◽  
Gloriosa Superba ◽  
Climatic Zones ◽  
Colchicine Alkaloid

The study was conducted to evolve Gloriosa superba for yield characters and alkalodi content for selecting elite genotypes for comercial exploitatio n. The genotypes were sowm in Variyankaval village, Udayarpalayam taluk of Ariyalur district, Tamil Nadu. The highest mean value for fresh and dry seed yield was observed in Chittor local. The genotype Mulanur local has recorded the highest mean value for number of pods per plant and number of seeds per pod and Arupukotai local excelled the general mean for the traits seeds per pod, fresh and dry seed yield and also for tuber characters. An investigation was carried out to quantify the colchicine (alkaloid) present in tubers by High Performance Liquid Chromatography (HPLC) method. The genotypes collected from Arupukotai recorded the highest colchicine content (0.760 mg/g) followed by Chittoor (0.578 mg/g) and Mulanur (0.496 mg/g) and there by these three genotypes were utilized for further crop improvement.

scholarly journals Estimating Forest Canopy Cover by Multiscale Remote Sensing in Northeast Jiangxi, China

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 433
Author(s):  
Xiaolan Huang ◽  
Weicheng Wu ◽  
Tingting Shen ◽  
Lifeng Xie ◽  
Yaozu Qin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Google Earth ◽  
Tree Canopy ◽  
Landsat Data ◽  
High Resolution Data ◽  
Modis Ndvi ◽  
Tree Canopy Cover

This research was focused on estimation of tree canopy cover (CC) by multiscale remote sensing in south China. The key aim is to establish the relationship between CC and woody NDVI (NDVIW) or to build a CC-NDVIW model taking northeast Jiangxi as an example. Based on field CC measurements, this research used Google Earth as a complementary source to measure CC. In total, 63 sample plots of CC were created, among which 45 were applied for modeling and the remaining 18 were employed for verification. In order to ascertain the ratio R of NDVIW to the satellite observed NDVI, a 20-year time-series MODIS NDVI dataset was utilized for decomposition to obtain the NDVIW component, and then the ratio R was calculated with the equation R = (NDVIW/NDVI) *100%, respectively, for forest (CC >60%), medium woodland (CC = 25–60%) and sparse woodland (CC 1–25%). Landsat TM and OLI images that had been orthorectified by the provider USGS were atmospherically corrected using the COST model and used to derive NDVIL. R was multiplied for the NDVIL image to extract the woody NDVI (NDVIWL) from Landsat data for each of these plots. The 45 plots of CC data were linearly fitted to the NDVIWL, and a model with CC = 103.843 NDVIW + 6.157 (R2 = 0.881) was obtained. This equation was applied to predict CC at the 18 verification plots and a good agreement was found (R2 = 0.897). This validated CC-NDVIW model was further applied to the woody NDVI of forest, medium woodland and sparse woodland derived from Landsat data for regional CC estimation. An independent group of 24 measured plots was utilized for validation of the results, and an accuracy of 83.0% was obtained. Thence, the developed model has high predictivity and is suitable for large-scale estimation of CC using high-resolution data.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals STUDY ON REMOTE SENSING MONITORING MODEL OF AGRICULTURAL DROUGHT BASED ON RANDOM FOREST DEVIATION CORRECTION

2021 ◽  
pp. 413-422
Author(s):  
Shao Li ◽  
Xia Xu
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Monitoring ◽  
Monitoring Methods ◽  
Large Area ◽  
Sensing Data ◽  
Monitoring Model

Using remote sensing data to monitor large area drought is one of the important methods of drought monitoring at present. However, the traditional remote sensing drought monitoring methods mainly focus on monitoring single drought response factors such as soil moisture or vegetation status, and the research on comprehensive multi-factor drought monitoring is limited. In order to improve the ability to resist drought events, this paper takes Henan Province of China as an example, takes multi-source remote sensing data as data sources, considers various disaster-causing factors, adopts random forest method to model, and explores the method of regional remote sensing comprehensive drought monitoring using various remote sensing data sources. Compared with neural network, classification regression tree and linear regression, the performance of random forest is more stable and tolerant to noise and outliers. In order to provide a new method for comprehensive assessment of regional drought, a comprehensive drought monitoring model was established based on multi-source remote sensing data, which comprehensively considered the drought factors such as soil water stress, vegetation growth status and meteorological precipitation profit and loss in the process of drought occurrence and development.

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