Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat.

Wheat is the important food grain and is cultivated in many Indian states: Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, which contributes to major crop production in India. In this study, popular statistical approach multiple linear regression (MLR) and time series approaches Time Delay Neural Network (TDNN) and ARIMAX models were envisaged for wheat yield forecast using weather parameters for a case study area, i.e., Junagarh district, western Gujarat region situated at the foot of Mount Girnar. Weather data corresponds to 19 weeks (42nd to 8th Standard Meteorological Week, SMW) during crop growing season was used for prediction of wheat yield using these statistical techniques and were evaluated for their predictive capability. Furthermore, trend analysis among weather parameters and crop yield was also carried out in this study using non-parametric Mann-Kendall test and Sen's slope method. Significant negative correlation was observed between wheat yield and some of the weekly weather variables, viz., maximum temperature (48, 49, 50, 51, 52, and 4th SMW), and total rainfall (50, 51, and 1st SMW) while positive correlation was observed with morning relative humidity (49 and 3rd SMW). Study indicated that forecast error varied from 1.80 to 10.28 in MLR, 0.79 to 7.79 in ARIMAX (2,2,2), - 3.09 to 10.18 in TDNN (4,5) during model training period (1985-2014). The MAPE value shows that the time series data predicted less than 5% of variation, whereas the conventional MLR technique indicated more than 7% variation. Both ARIMAX and TDNN approaches indicated better performance during model training periods, i.e., 1985-2014 and 1985-2015, while former performed well during the forecast periods 1985-2016 and 1985-2017. Overall, the study indicated that the ARIMAX approach can be used consistently for 4 years using the same model.

Integrated drought monitoring and assessment using multi-sensor and multi-temporal earth observation datasets: a case study of two agriculture-dominated states of India.

In the current scenario of climate change, there has been a substantial increase in the frequency and severity of drought events. Therefore, it is necessary to investigate spatio-temporal characteristics of different drought events to plan for water resource utilization. The present study aims to assess and quantify the impact of meteorological, hydrological, and agricultural drought events from 2001 to 2017 over two large states of India (i.e., Maharashtra and Madhya Pradesh) using multi-temporal earth observation data at a finer resolution of 1 km. Drought indices including Standardized Precipitation Index (SPI), Standardized Water level Index (SWI), and Vegetation Health Index (VHI) were derived from precipitation, groundwater level, vegetation indices, and land surface temperature data respectively to map the spatial extent and severity of meteorological, hydrological, and agricultural drought. Assessment of individual drought indices was carried out to understand the effect of these drought events separately on the study area. Area vulnerable with multiple droughts in the region was identified by integrating multiple drought indices to derive a composite drought map. This included the locations that are hotspots in terms of the occurrence of drought events of different types. The spatial pattern captured in the composite drought map indicates that most of the study areas are prone to drought events varying from mild to extreme severity. Madhya Pradesh is more prone to meteorological and agricultural drought events compared to hydrological drought. Maharashtra state is prone to three types of drought with agricultural drought being the dominant one. This study provides an opportunity to investigate and understand the drought phenomenon in a comprehensive manner at comparatively finer spatial resolution.

Vegetation health conditions assessment and mapping using AVIRIS-NG hyperspectral and field spectroscopy data for -environmental impact assessment in coal mining sites.

This paper focuses on vegetation health conditions (VHC) assessment and mapping using high resolution airborne hyperspectral AVIRIS-NG imagery and validated with field spectroscopy-based vegetation spectral data. It also quantified the effect of mining on vegetation health for geo-environmental impact assessment at a fine level scale. In this study, we have developed and modified vegetation indices (VIs) based model for VHC assessment and mapping in coal mining sites. We have used thirty narrow banded VIs based on the statistical measurement for suitable VIs identification. The highest Pearson's r, R2, lowest RMSE, and P values indices have been used for VIs combined pixels analysis. The highest different (Healthy vs. unhealthy) vegetation combination index (VCI) has been selected for VHC assessment and mapping. We have also compared VIs model-based VHC results to ENVI (software) forest health tool and Spectral-based SAM classification results. The 1st VCI result showed the highest difference (72.07%) from other VCI. The AUC values of the ROC curve have shown a better fit for the VIs model (0.79) than Spectral classification (0.74), and ENVI FHT (0.68) based on VHC results. The VHC results showed that unhealthy vegetation classes are located at low distances from mine sites, and healthy vegetation classes are situated at high distances. It is also seen that there is a highly significant positive relationship (R2 =0.70) between VHC classes and distance from mines. These results will provide a guideline for geo-environmental impact assessment in coal mining sites.

Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region.

This work mainly focused on deforestation susceptibility (DS) assessment and its prediction based on statistical models (FR, LR & AHP) in the Saranda forest, India. Also, efforts had been made to quantify the effect of mining on deforestation. We had considered twenty-five (twenty present and five predicted) causative variables of deforestation, including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The predicted variables have been generated from different simulation models. Also, very high-resolution, Google Earth imagery have been used in time series analysis for deforestation from 1987 to 2020 data and generated dependent variable. On deforestation analysis, it was observed that a total of 4197.84 ha forest areas were lost in the study region due to illegal mining, agricultural and tribal people allied activities. The DS results have shown that of total existing forest area, 11.22% area were under very high, 16.08% under high, 16.18% under moderate, 24.25% under low, and 32.27% falls very low categories. According to the DS assessment and predicted results, the very high susceptibility classes were found at and close to mines, agricultural, roads and settlement's surrounding sites. The sensitivity analysis results also shown that some causative variables (maximum temperature (2.95%), minimum temperature (0.51%), rainfall (2.69%), LST (4.56%), hot spot (7.36%), aspect (1.14%), NDVI (2.64%), forest density (3.78%), lithology (3.26%), geomorphology (3.00%), distance from agricultural (19.40%), soil type (2.05%), solar radiation (5.97%), LULC (3.26%), drought (3.16%), altitude (2.85%), slope (5.97%), distance from mines (18.05%), roads (2.17%), and settlements (5.18%)) were more sensitive to deforestation. Most of the sensitive parameters showed a positive correlation with DS. The AUC values of the ROC curve had shown a better fit for AHP (0.72) than (0.69) FR and LR (0.68) models for present DS results. The correlation results had shown a good inverse relationship between DS and distance from mines and foliar dust concentration. This work will espouse the future work in the effective planning and management of the mining-affected forest region and predicted deforestation susceptibility would be helpful for forest ecosystem study and policymaking.

Planning rainwater conservation measures using geospatial and multi-criteria decision making tools.

The assessment of rainwater-harvesting demand (RWHD) map and the identification of appropriate priority-based locations for rainwater-harvesting (RWH) and groundwater recharge structures are very crucial for the water managers, particularly in irrigation commands. This study addresses this challenge by using multi-criteria decision-making (MCDM) and geospatial techniques to present a novel and robust approach for generating RWHD map and identifying sites/zones for distinct RWH and groundwater recharge on a priority basis. Primary thematic layers such as existing irrigation water supply, irrigation demand, and groundwater potential were considered in this study for delineating RWHD zones. Further, sites suitable for RWH and groundwater recharge were identified using soil, slope, drainage network, and lineament thematic layers of the study area and they were prioritized. Four zones of rainwater demand were identified for the prioritization of RWH and groundwater structures: (a) "low" rainwater-harvesting demand zone (covering 3% of the total study area), (b) "moderate" rainwater-harvesting demand zone (40%), (c) "high" rainwater-harvesting demand zone (42%), and (d) "very high" rainwater-harvesting demand zone (15%). Moreover, 46 sites for check dams and 145 suitable sites for percolation tanks were identified, together with 253 ha area for groundwater recharge based on the priority of rainwater-harvesting demand. Integration of geospatial and MCDM techniques in conjunction with suitable thematic layers provides a helpful and realistic tool for large-scale planning and management of rainwater conservation measures.

Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin, India.

Population growth rate indicates the proportional rate of settlement expansion and landscape modification in any river basin. The Mahanadi River basin (MRB), which is a densely populated, cropland and forest-dominated landscape, is selected as a case study area for studying the nature of built-up expansion and the corresponding land cover modifications. Satellite data-derived land use/land cover (LU/LC) maps for the years 1995, 2005, and 2015 were used for identification of landscape changes during the past three decades. One of the major LU/LC changes are observed in terms of increase in the water, which may be attributed to construction of new dams at the cost of the croplands and forest areas. Conversion of forest to cropland and expansion and densification of built-up areas in and around the existing built-up areas are also identified as a major LU/LC change. The geostatistical analysis was performed to identify the relationship between LU/LC classes with drivers, which showed that built-up areas were more in topographically flat terrain with higher soil depth, and expanded more around the existing built-up areas; cropland areas were more at lower elevation and less sloppy terrain, and forest areas were more at higher elevation. The LU/LC scenario of 2025 was projected using a spatially explicit dynamic conversion of land use and its effects (Dyna-CLUE) modeling platform with the LU/LC change trends of past 10 years (2005-2015) and 20 years (1995-2015). The major LU/LC changes observed during 2005-2015 were built-up expansion by 36.53% and deciduous forest and cropland reduction by 0.35% and 0.45%, respectively. Thus, the corresponding predicted change during 2015-2025 estimated built-up expansion by 25.70% and deciduous forest and croplands loss by 0.43% and 0.35%, respectively. On the other hand, during 1995 to 2015, the total built-up expansion and deciduous forest and cropland reduction were observed 50.79%, 0.45%, and 0.73%, respectively. Thus, the predicted changes during 2015-2025 were estimated as 18.48% built-up expansion and 0.22% and 0.21% deciduous forest and cropland loss. However, with the conditions of restricted deforestation and less landscape modification, the LU/LC projections show less built-up area expansion, reducing the cropland, fallow land, plantation, and waste land. The reduced numbers of land cover conversions types during 2005-2015 compared with 1995-2005 indicate more stabilized landscape. The input LU/LC maps and statistical analysis demonstrated the landscape modifications and causes observed in the basin. The model projected LU/LC maps are giving insights to possible changes under multiple pathways, which will help the agriculture, forest, urban, and water resource planners and managers in improved policy-making processes.

Developing quantifiable approaches for delineating suitable options for irrigating fallow areas during dry season-a case study from Eastern India.

Harvesting surface runoff during monsoon season for further utilization in crop production during the post-monsoon season is now becoming an effective solution to mitigate water scarcity problems. In this study, multi-criteria analysis-analytic hierarchy process (MCA-AHP)-based approach was envisaged for rainwater harvesting (RWH) zoning for a case study area, i.e., two districts of Odisha state situated in Eastern India. In spite of having a large irrigation network in the study area, major portion of these two densely populated and agriculture dominated districts remains fallow during dry seasons. Suitable locations for RWH structures such as farm pond, check dam, and percolation tanks were identified through Boolean conditions. RWH potential map was generated using different thematic layers namely land use/land cover (LU/LC), geomorphology, slope, stream density, soil type, and surface runoff. AHP-based MCA technique was used to integrate these thematic layers by assigning weights to the thematic layers and ranks to the individual theme features on 1-9 AHP Saaty's scale, considering their relative importance on RWH potential of the study area. The Natural Resources Conservation Service-Curve Number method was used to derive surface runoff using Climate Hazards Group Infra-Red Precipitation with Station rainfall data, satellite-derived LU/LC and FAO soil maps. In comparison to single cropped areas in 48% of the total study area, only 4% area was under double and triple cropped areas during 2016-2017. Moderate runoff was observed in > 50% of the study area dominated by agricultural landscape. Nearly 40%, 25.11%, and 32.45% of the study area indicated very high, high, and moderate RWH potentials, respectively. Particularly, very high RWH potential is observed in the eastern and central portion of the study area. The use of appropriate RWH structures in less irrigated areas will facilitate multiple cropping and will substitute the use of sub-surface water harvesting practices. In these two districts, 73 check dams and 153 percolation tanks are prescribed along the 2nd- and 3rd-order streams. In coarser textured soil, nearly 306 km2 and 608 km2 areas are identified as moderate and highly suitable zones for percolation tank construction on ground, while in fine soil, around 786 km2 area is identified as suitable for farm pond construction. Majority of the suitable zones for percolation tanks is found in Jajpur district, while suitability for adoption of farm pond and check dam is more in Bhadrak district. It is expected that implementation of the prescribed RWH structures can mitigate the threats of flood, drought, soil erosion, and enhance the soil moisture and cropping intensity significantly. The use of GIS platform with the spatial layers and the methodology adopted can be updated and replicated in larger regions in a shorter time. The spatially explicit maps are offering insights to different themes, providing useful information to the water resource managers, and may improve the decision-making process.