Land use land cover change detection in the lower Bhavani basin, Tamil Nadu, using geospatial techniques

Land use land cover (LULC) change detection is essential for sustainable development, planning and management. This study was an attempt to evaluate the LULC change in the lower bhavani basin from 2014 to 2019, using Landsat 8 data integrating Google Earth Engine (GEE) as a web-based platform and Geographic Information System. The CART and Random Forest classifiers in GEE were used for performing supervised classification. The classified map accuracy was assessed using high resolution imagery and evaluated using a confusion matrix implemented in GEE. Five major LULC classes, viz., agriculture, built up, current fallow, forest and waterbody, were identified, and the dominant land use in the study area was agriculture and current fallow, followed by dominant land use of forest. During the study period (2014–2019) the change inbuilt-up area 7.37% in 2019 and 5.45% in 2014, was noted due to urban sprawl. GEE showed significant versatility and proved to be an effective platform for LULC detection

Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using Surface Energy Balance Algorithm for Land Model

Estimating evapotranspiration's spatiotemporal variance is critical for regional water resource management and allocation, including irrigation scheduling, drought monitoring, and forecasting. The Surface Energy Balance Algorithm for Land (SEBAL) method can be used to estimate spatio-temporal variations in evapotranspiration (ET) using remote sensing-based variables like Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), surface albedo, transmittance, and surface emissivity. The main aim of the study was to evaluate the actual evapotranspiration for the lower Bhavani basin, Tamil Nadu based on remote sensing methods using Landsat 8 data for the years 2018 to 2020. The actual evapotranspiration was estimated using SEBAL model and its spatial variation was compared over different land covers. The estimated values of daily actual evapotranspiration in the lower Bhavani basin ranged from 0 to 4.72 mm day-1. Thus it is evident that SEBAL model can be used to predict ET with limited ground base hydrological data. The spatially estimated ET values will help in managing the crop water requirement at each stage of crop and irrigation scheduling, which will ensure the efficient use of available water resources

Monitoring and mapping of seasonal vegetation trend in Tamil Nadu using NDVI and NDWI imagery

In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics

Agricultural drought monitoring in Tamil Nadu in India using Satellite-based multi vegetation indices

Drought being an insidious hazard, is considered to have one of the most complex phenomenons. The proposed study identifies remote sensing-based indices that could act as a proxy indicator in monitoring agricultural drought over Tamil Nadu's region India. The satellite data products were downloaded from 2000 to 2013 from MODIS, GLDAS – NOAH, and TRMM. The intensity of agricultural drought was studied using indices viz., NDVI, NDWI, NMDI, and NDDI. The satellite-derived spectral indices include raw, scaled, and combined indices. Comparing satellite-derived indices with in-situ rainfall data and 1-month SPI data was performed to identify exceptional drought to no drought conditions for September month. The additive combination of NDDI showed a positive correlation of 0.25 with rainfall and 0.23 with SPI, while the scaled NDDI and raw NDDI were negatively correlated with rainfall and SPI. Similar cases were noticed with raw LST and raw NMDI. Indices viz., LST, NDVI, and NDWI performed well; however, it was clear that NDWI performed better than NDVI while LST was crucial in deciding NDVI coverage over the study area. These results showed that no single index could be put forward to detect agricultural drought accurately; however, an additive combination of indices could be a successful proxy to vegetation stress identification.

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Effect of different herbicide spray volumes on weed control efficiency of a battery-operated Unmanned aerial vehicle sprayer in transplanted rice (Oryza sativa L.)

The effect of spray volume on weed control in transplanted rice ecosystems using the Unmanned aerial vehicle (UAV) needs to be better understood for management in the advancements of UAV-based spraying technology. The present study aimed to find out the influence of varied spray volumes of 15 L/ha, 20 L/ha and 25 L/ha using the UAV and 500 L/ha using a Knapsack sprayer (KS) to compare the weed density, weed dry matter and weed control efficiency and yield in transplanted rice (Oryza sativa L.). Pre-emergence (PE) application of Pyrazosulfuron-ethyl at 25 g a.i./ha at three days after transplanting (DAT) and post-emergence (PoE) application of Bis-pyribac sodium at 25 g a.i./ha at 25 DAT were used as herbicide treatments. The results revealed that varied spray volumes significantly influenced the weed density, dry matter, and weed control efficiency of the UAV and KS. Application of herbicides using KS (500 L/ha) and UAV (25 L/ha) had better control on the weeds by reducing weed density and dry matter at 20, 40, and 60 DAT, with no significant difference. Higher grain yield and straw yield were recorded in KS (500 L/ha) and UAV (25 L/ha), with no significant difference. However, applying 25 L/ha had better weed control efficiency and higher yield, possibly due to optimum deposition. Considering the low volume application of UAV (25 L/ha) as compared with KS (500 L/ha), it is better to go for the optimal application of 25 L/ha, which is an energy-efficient and cost-effective, labour-saving approach compared to KS

Monitoring vegetation dynamics using multi-temporal Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images of Tamil Nadu

Vegetation indices serve as an essential tool in monitoring variations in vegetation. The vegetation indices used often, viz., normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were computed from MODIS vegetation index products. The present study aimed to monitor vegetation's seasonal dynamics by using time series NDVI and EVI indices in Tamil Nadu from 2011 to 2021. Two products characterize the global range of vegetation states and processes more effectively. The data sources were processed and the values of NDVI and EVI were extracted using ArcGIS software. There was a significant difference in vegetation intensity and status of vegetation over time, with NDVI having a larger value than EVI, indicating that biomass intensity varies over time in Tamil Nadu. Among the land cover classes, the deciduous forest showed the highest mean values for NDVI (0.83) and EVI (0.38), followed by cropland mean values of NDVI (0.71) and EVI (0.31) and the lowest NDVI (0.68) and EVI (0.29) was recorded in the scrubland. The study demonstrated that vegetation indices extracted from MODIS offered valuable information on vegetation status and condition at a short temporal time period

Determination of nitrogen and water stress with hyper spectral reflectance on maize using classification tree (CT) analysis

Field experiments were conducted at the Tamil Nadu Agricultural University, Coimbatore, Tamilnadu, India during rabi (winter) season of 2013-14 with maize crop (TNAU maize hybrid Co 6). To ensure the stressed environment, the crop was subjected to two irrigation levels (IW/CPE: 0.80 and 0.50) and five staggered nitrogen levels (0, 50, 75, 100 and 125 % of recommended dose of nitrogen (RDN). The experiment was laid out in factorial randomized blocks design RBD (Factorial) with three replications. Hyper spectral observations were made with spectroradiometer GER 1500 at 60 and 90 days after sowing (DAS). Measured spectral reflectance curve of maize exhibited a broad low intensity peak centered in the green region at 550 nm and a sharp rise starting at about 675 nm to a plateau in the vicinity of 762 nm under unstressed environment created with irrigation at 0.80 IW/CPE ratio and fertilizer application at 100 % RDN. Significant differences in reflectance were established for nitrogen and water stress at green and NIR region. The results of Classification tree analysis revealed that nitrogen and water stress can be assessed and differentiated using reflectance data when transformed into spectral vegetation indices viz., NDVI, GNDVI, RVI, LCI, IR-RED and SR. Further the Classification tree algorithm could determine that NDVI was the most effective index to assess the combined effect of nitrogen and water stress in maize crop