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

Optimization of irrigation and nitrogen for sustainable rice cultivation: emissions and yield impact

Rice cultivation is integral to global food security and exports but contributes significantly to greenhouse gas emissions, mainly methane (CH?) and nitrous oxide (N?O), exacerbating climate change. This study evaluates the effects of three irrigation practices-conventional flooding (CF), alternate wetting and drying (AWD) and the modified system of rice intensification (MSRI) on CH? and N?O emissions and rice yields over two seasons (Kar 2022 and Samba 2023). A split-plot design with five nitrogen management strategies was employed, with weekly gas sampling and yield measurements at harvest. Among the treatments, the MSRI method, combined with 75% of the recommended nitrogen dose and a 0.4% foliar nano-urea spray (M3S5), recorded the lowest CH? emissions at 50-60 mg CH?/m²/day, compared to 120-130 mg CH?/m²/day under CF. In contrast, N?O emissions under MSRI peaked at 11-13 µg N?O/m²/day, higher than CF (5-7 µg N?O/m²/day). MSRI also achieved the highest rice yields, averaging 6029 kg/ha in Kar 2022 and 6018 kg/ha in Samba 2023, compared to 5500-5700 kg/ha under AWD. These findings highlight the potential of MSRI with optimized nitrogen management as a sustainable alternative, balancing high productivity with reduced CH? emissions and offering a pathway for climate-resilient rice farming

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

Structural Changes in Irrigation Vis-à-vis Cropping Pattern in Andhra Pradesh, India: An Economic Analysis

The inherent dynamism within a system leads to structural changes of that system. Over the past, the structural changes that are occurring in the world economies are affecting the subsystems one or another way. The development of a nation like India reflects from the development of its states. The current study focuses on the structural shifts within primary sector of Andhra Pradesh (includes Telangana State’s data) more precisely, economic linkages between irrigation and between the dynamic changes in cropping pattern. It also explores the implications of dynamic changes happened in state’s agriculture after green revolution. The shifts from period 1970 – 2020 are studied using secondary data. Growth rate and conventional analyses (percentages and averages) are used to study the effects of shifts in irrigation on cropping pattern of the state. The results exhibit, declining ecofriendly surface water irrigation systems (tanks) by twofold compensated with increased extraction of groundwater resources by fivefold. As a consequence, decline in net cropped area and cropping pattern shifts adopting mono-cropping or double cropping under rice as major crop followed by cash crops or pulse crops under groundwater and canal systems of irrigation aroused. While coarse cereals have lost prominence, cash crops like chilly (93 per cent) have gained importance under irrigation. The prominence of livestock sector in the state can be realized as net irrigated area under fodder crops improved to 50.61 per cent over the decades. Overall improvement in net irrigated area with canal and groundwater systems of irrigation is current scenario of the state. Pressurized irrigation technologies should be promoted to maintain sustainability of tank irrigation structures. Cropping patterns including coarse cereals should be adopted for maintaining nutritional security. Adoption of reclamation measures to attain ecological balance between groundwater and surface water irrigation sources

Latent concepts for area enhancement of mangrove forest: A novel approach through geospatial studies

Despite their vital roles in carbon sequestration, biodiversity conservation and coastal protection, mangrove ecosystems have historically faced degradation from pollution, deforestation and human activity. Mangrove restoration faces several challenges, including deforestation due to unsustainable logging for timber and fuelwood, as well as habitat loss from coastal development projects such as ports and resorts. The expansion of aquaculture, particularly shrimp farming, has led to the large-scale conversion of mangrove areas into degraded or unproductive land. Huge restoration projects have been started all over the world to deal with these issues. Geospatial technologies such as GIS (Geographic Information System), GPS (Global Positioning System), remote sensing and satellite imagery have made it easier to find suitable sites for restoration, which was a challenging task in the past. These technologies also enable the acquisition of large amounts of data. Topography, soil quality, land use and biodiversity are some of the factors that influence the process of identifying possible restoration sites. Although obstacles like ecosystem complexity, lack of data and methodological constraints still exist, developments in machine learning and radar remote sensing provide promising paths to obtaining vital information. Conservation efforts are being bolstered by data integration and predictive modeling-driven evidence-based rehabilitation strategies. This review examines the cutting-edge geospatial technologies and their critical role in surmounting obstacles and promoting the rehabilitation and re-establishment of mangrove habitats

Mapping coconut plantation in Western Agro-Climatic zone using object-based classification and machine learning technique

Coconut (Cocos nucifera), a key crop for over 10 million farming families in India, is vital in the agricultural economies of southern states like Tamil Nadu. However, traditional methods of monitoring coconut plantations are challenging due to the crop's geographical dispersion and seasonal variations. This study focuses on mapping coconut plantations in the Western Agro-Climatic Zone of Tamil Nadu using Object-Based Classification (OBC) and machine learning techniques. A ten-year time series of Landsat 7 optical satellite data (2012-2013 and 2022-2023) was employed, combined with ground truth surveys across the region. The study utilized Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy. The RF classifier achieved an accuracy of 91.7% in 2012-2013 and 90.3% in 2022-2023, outperforming SVM, which hovered around 70%. The research also conducted a change detection analysis, revealing a net increase of 3,270 hectares of coconut plantations over the decade, with the Coimbatore district contributing the most significant growth of 2,560 hectares. This study underscores the effectiveness of integrating OBC and machine learning, mainly RF, for accurate and efficient mapping of coconut plantations using Landsat satellite data

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

Evaluating rice yield and resource efficiency: DSSAT analysis of conventional vs. AWD techniques in Coimbatore

Rice cultivation is a key activity of Indian agriculture, contributing significantly to global rice production and exports. Optimal yield is crucial and influenced by various agronomical and environmental factors. For the experiment, the decision support system for agrotechnology transfer (DSSAT) of the rice crop model is utilized to validate the grain and straw yield in addition to resource productivity metrics and leaf area index. The study was conducted during the Zaid season from January to May in both 2022 and 2023 at the Thensangampalayam village, Coimbatore district, Tamil Nadu. The CO-55 rice variety was used for 2 cultivation methods i.e., conventional and alternate wetting and drying (AWD), along with drone spray of nano urea. The model was calibrated and validated with the input of comprehensive datasets of soil profile, meteorological parameters, crop-specific cultivation methods, agronomic practices and genetic coefficients. AWD consistently outperformed the conventional method in both grain and straw yields. DSSAT simulations achieved a high accuracy of 99.78 % in grain yield and 91.67 % in straw yield between the 2 cultivation methods. The AWD also outperformed in water use efficiency with 2.3 kg/m3 compared to conventional at 1.8 kg/m3. Leaf Area Index was recorded high in the conventional method at heading stage with 6.96 and AWD at 6.46. The study provides valuable information on adaptive farming practices and climate-resilient crop management strategies

Spatiotemporal dynamics of water spread areas and vegetation health in the lower Vaigai sub-basin: A multi-sensor analysis using Sentinel-1A SAR and Sentinel-2A MSI (2018-2023)

A spatiotemporal analysis of water spread areas in tanks within the lower Vaigai sub-basin was performed using Sentinel-1A SAR imagery from 2018 to 2023. The analysis revealed a mean water spread area of 275.29 ha, with the highest being 628.29 ha in summer 2023 and the lowest at 5.55 ha in summer 2018. This was influenced by a total rainfall of 5777.06 mm, with an average of 879.14 mm annually. NDVI data from Sentinel-2 categorized crop health across 74.5 thousand ha, showing high no vegetation (20-45 %) and sparse vegetation (24-33 %) during the Kharif season. The Rabi season exhibited improved conditions, with moderate vegetation peaking at 40 % in 2020, while summer consistently showed crop stress with minimal good vegetation (up to 5 %). Given the arid conditions and dependence on irrigation tanks, the study underscores the importance of water availability for crop growth in lower Vaigai sub basin. In this investigation, the identification of poor crop performance during the Kharif and summer seasons can guide researchers and administrators to increase efforts on introduce drought-resistant crops, adjust planting schedules or implement supplemental irrigation over this region. Additionally, the insights gained from the present investigation on water spread dynamics in tanks recommend the development of climate-smart agricultural practices, including water-saving irrigation techniques and hydrological modelling, to enhance resilience. The results can further support government interventions, such as improving tank rehabilitation programs, which are crucial for ensuring sustainable crop production and food security in the Lower Vaigai sub-basin

A comparative study of crop evapotranspiration estimation in maize using empirical methods, pan evaporation and satellite-based remote sensing technique

A research study was conducted at the Agricultural College and Research Institute, Coimbatore to estimate the evapotranspiration (ET) of maize crop (Zea mays) over 2 consecutive seasons in 2022-2023. Among the different methods used to estimate crop evapotranspiration, the Food and Agricultural Organization Penman-Monteith model (FAO P-M) is widely recognized as the standard approach for ET estimation. This study aimed to compare the effectiveness of three alternative methods - Thornthwaite (TW), NDVI-based and pan methods against the FAO Penman-Monteith (P-M) model in estimating maize evapotranspiration. Meteorological data were collected from the TNAU weather station spanning the period from 2022 to 2023.The performance of the estimation methods was assessed using statistical metrics such as coefficient of determination (R2), root mean squared error (RMSE), percentage error and mean bias error. The findings revealed that the NDVI-based method, relying on satellite data, provided higher accuracy in estimating maize evapotranspiration compared to the FAO PM method. Specifically, the NDVI-based method achieved the highest coefficient of determination (R2) of 0.87 and 0.89, the lowest RMSE of 12.44 mm/month and 15.5 mm/month, the lowest percentage error of 4.8 % and 9.00 % and the lowest mean bias error of 5.5 and 7.85 for the first and second seasons respectively. This study highlights the effectiveness of the NDVI-based ET estimation method for accurately assessing maize evapotranspiration. While the FAO-56 Penman-Monteith method is highly regarded for its accuracy in both theoretical and practical contexts, the comparative evaluation presented in this paper offers valuable insights for selecting alternative methods that require less data, particularly in regions with limited data availability