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Soil salinity plays an important role in agriculture production and land degradation, especially in semi-arid and arid regions. Accurate prediction of soil salinity requires evaluating crop yield, native vegetation situations, and irrigation command area management. In this study, MLR (multiple linear regression), SVMs (support vector machines) and ANNs (artificial neural networks) models were employed by using Landsat-8 OLI and GIS (Geographical Information Systems) techniques for predicting soil salinity in northwest Iran. Soil salinity was measured at 92 points (in a depth of 0–20 cm). The vegetation and soil salinity spectral indices, extracted from Landsat-8 OLI, were employed as input data. The results of this study indicated that the best-developed model for predicting soil salinity was the SVM-based model with R2 (0.874) and RPD (2.32) and the lowest RMSE (11.20 dS m−1). Moreover, the performance of developed models under different vegetation coverage showed that the SVM-based model yielded the best result. It was concluded that the SVM-based model is reliable for quantifying soil salinization. [ABSTRACT FROM AUTHOR]

In Mediterranean regions, soil erosion is one of the most serious challenges with land degradation. The current work aims to study the water erosion in the watershed of Wadi Rmel located in the North-East of Tunisia, using the SWAT model, RUSLE method and, PAP/RAC approach for the year 2020. These methods are concentrated on the integration of factors influencing soil erosion and then validated by chosen spectral indices (NDVI, IB and, MSAVI), that are the most used to characterize the state of soil degradation. SWAT model demonstrates that the most eroded zones are located in South-West zones, in sub-basins number 10, 17 and 26. These areas presented a degraded vegetation cover with an NDVI value near zero. Moreover, the resulting map of the RUSLE method shows that the most erosive zone is located in the regions of the southwest. This part presents a high percentage of BI, more than 20%. On the other hand, the analysis of the erosive map conditions, presented by the PAP/RAC guidelines, shows also that the very high erosive state is located in southwestern areas with various uncovered zones. The same spatial distribution of soil erosion is found by the three used methods and confirmed by the spectral indices. Finally, this study results are beneficial for decision-makers to identify priority areas for intervention and to evaluate the existing management plans. Moreover, the application of spectral indices provides a useful tool to predict soil erosion hazard and to validate modeling results in case of the absence of field missions and observations. [ABSTRACT FROM AUTHOR]

Forest canopy density (FCD) is one of the important parameters for forest mapping and monitoring. Terrestrial Laser Scanning (TLS) is one of the most accurate tools used in field data collection. Hence, the present study aimed to predict forest aboveground biomass (AGB) by integrating TLS data, satellite data-derived FCD and spectral indices. FCD Mapper was used to classify Landsat-8 OLI data into FCD classes, which were validated using field-measured FCD. In the field, point cloud data were collected from each FCD class using TLS. The diameter at breast height (dbh) and height of individual trees were retrieved from the point cloud data and validated with field-measured dbh and height. AGB was estimated from the TLS-derived dbh and height and modelled as a function of Landsat-8 OLI-derived FCD classes and spectral indices. A total of 11 FCD classes were generated, which showed a strong positive correlation (r = 0.96) with the field-measured FCD. From the TLS point cloud data, 96% of individual trees were extracted. Positive correlations were found between TLS-measured dbh and field-measured dbh (r = 0.99), and TLS-measured height and field-measured height (r = 0.96). A linear function best fitted between TLS-estimated AGB and FCD classes was established. Because of the low variability in AGB due to absolute FCD classes, the model was further extended using a few spectral indices. Using a multiple linear model, the average AGB (374 Mg ha−1) and total AGB (3,024,550 Mg) of the study area were predicted. The study highlighted that the combined application of TLS and satellite data-derived FCD and spectral indices can be one of the fastest and accurate methods in forest AGB prediction. [ABSTRACT FROM AUTHOR]

This study develops a workflow to quantify and map long-term (1990–2020) changing land cover, especially of the fluvial systems, in parts of the foothills of north-western Himalayas, mainly between the Satluj and Ghagghar River flood plains in north-west India. The workflow begins with principal component analysis of seven Landsat annual median images, followed by change vector analysis of spectral indices and hybrid image classification. The analysis identified a significant amount of disappeared dry fluvial channels (59.6 km2 area; 1215 km length). Other changes included appearance of new water bodies (6.3 km2 of reservoir/lake area), including 1.9 km2 of five major lakes due to dam construction in the upstream, conversion of 226.4 km2 area from non-built-up to built-up, and an increase in vegetation intensity. Vanishing fluvial channels being replaced by other land use/land cover (LULC) classes (vegetation (42.4%) and built-up (12%)) may eventually cause urban floods, requiring policy measures. The integrated semi-automatic workflow proposed in this study, implemented primarily in Google Earth Engine, was well suited for this analysis. [ABSTRACT FROM AUTHOR]

Mapping the salt-affected soils (SAS) through spectral indices derived from satellite imagery is essential for its monitoring at a frequent interval and for the management. Several salinity indices have been developed based on the combination of spectral bands for predicting soil salinity in the arid environment. Present study attempted to identify and map the salt-affected soils (SAS) in inland plains of semi-arid agro-ecological region of Tamil Nadu, India using satellite image (Resourcesat-1(IRS-P6) LISS IV). Vertisols and Inceptisols are the major soil orders cultivated under paddy, sugarcane and pulses. An integrated approach of spectral bands and indices along with analytical data of samples collected from the field was used to develop multiple regression equations for prediction. The best linear regression model was selected based on adjusted R2 and MSE values for the prediction of pH, EC, and ESP. The variation observed between salt-affected soils predicted by multiple regression equations and measured data were 15, 10, and 5 percent for pH, EC, and ESP, respectively, due to weak variation in soil properties, sample density, better vegetation cover by management practices. [ABSTRACT FROM AUTHOR]

The emerging threat for eco-sustainability has led to a breakthrough in satellite image analyses and such instantaneous monitoring of hazards could replenish the rejuvenation of natural ecosystem. Generally, the satellite images are huge dimensional data with numerous bands of specific details about the observed region. To apply immediate precautionary measures for environmental hazards and natural devastations, deploying a cloud-based intelligent web service for handling real time satellite image processing is inevitable. Therefore, the cloud implementation could afford integrated huge storage and parallel data processing tasks, the outcome of instantaneous satellite image processing relies with the effective data processing methods of less complexity. In this regard to address a major hazard of today which is drought monitoring, this paper focuses on developing an effective water segmentation method for such geospatial cloud web services. The Landsat 8 images of Sambhar lake region has been chosen for exploiting the water segmentation results. Most prevalent approaches from Spectral indices and unsupervised clustering such as normalized difference water index (NDWI), modified normalized difference water index, fuzzy C means, K-Means, Adaptive Regularized kernel fuzzy C means (ARKFCM) and simple linear iterative clustering-based superpixel segmentation (SLIC-SUPER) are compared, respectively. On comparative assessment using standard image quality assessment metrics, NDWI and ARKFCM outstands the rest with more accurate water body delineation. However, based on reduced computational complexity and instant localization, NDWI of spectral indexing approach clearly portray the significance of spectral influence in water body segmentation from satellite images. And it can be adapted as a persistent choice for instantaneous water body segmentation in a cloud-centered geospatial module. [ABSTRACT FROM AUTHOR]

A field experiment was conducted for three cultivars of wheat i.e. PBW 343, PBW 550 and DBW 17 with five nitrogen levels i.e. 0, 75, 125, 175 and 225 kg N/ha in split plot design. Growth parameters like LAI, chlorophyll content, nitrogen, chlorophyll concentration index (CCI) and plant height were recorded. Spectral indices such as Normalized difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Moisture Stress Index (MSI), Green Index (GI), Leaf Chlorophyll Index (LCI), Plant Senescence Reflectance Index (PSRI), were computed using the multiband spectral data. Among the different phenological phases of crop growth, best correlations were observed between different growth parameters and vegetation indices at booting stage of wheat. The spectral indices selected for the study recorded at booting stage were correlated better with all the crop parameters having 'R' values varied between 0.68 and 0.83. All the spectral indices were having good correlation (R) ranging between 0.58 and 0.82 for grain yield and 0.63-0.80 for 1000 grain weight, But best correlation were noticed between NDVI and grain yield having R2 value 0.78, NDVI and 1000 grain wt having R value 0.80, LCI and grain yield having R value 0.82, LCI and 1000 grain weight having R2 value 0.79. The LCI and NDVI were having best correlation with grain yield and 1000 grain wt. It was concluded that amongst the various indices selected, Leaf color index (LCI) was the only index which was correlated better, with all the plant growth parameters and yield at booting stage of the wheat crop. [ABSTRACT FROM AUTHOR]

Present study was designed to determine the effect of various growing environments on sucking pest population dynamics in cotton and to work out their relation with spectral indices. Crop spectral reflectance in four IRS bands was measured with ground truth radiometer during 1000-1200 h in all the treatment combinations. Incidence of sucking pest in cotton was found out to be highly influenced by growing environments. The leafhopper and whitefly population was highest in 15 May sown cotton crop and was lowest in 15 April sown crop. Cultivar HS-6 was highly affected by both the sucking pest than the other cultivar H-1226. The spectral indices (SR, NDVI and TVI) were highest in 15 April sown crop at all the phenophases and were lowest in 15 May sown crop. The cultivar H-1226 showed higher values of spectral indices as compared to HS-6. The relationship of pests' population with various spectral indices was established. Multiple regression models based on spectral indices can be used for prediction of sucking pest population more than 69 and 74 % accuracy in leafhopper and whitefly, respectively in cotton crop. [ABSTRACT FROM AUTHOR]

Crop residue has become an increasingly important factor in agriculture management. It assists in the reduction of soil erosion and is an important source of soil organic carbon (soil carbon sequestration). In recent past, remote sensing, especially narrowband, data have been explored for crop residue assessment. In this context, a study was carried out to identify different narrow-bands and evaluate the performance of SWIR region based spectral indices for crop residue discrimination. Ground based hyperspectral data collected for wheat crop residue was analyzed using Stepwise Discriminant Analysis (SDA) technique to select significant bands for discrimination. Out of the seven best bands selected to discriminate between matured crop, straw heap, combine-harvested field with stubbles and soil, four bands were from SWIR (1980, 2030, 2200, 2440 nm) region. Six spectral indices were computed, namely CAI, LCA, SINDRI, NDSVI, NDI5 and hSINDRI for crop residue discrimination. LCA and CAI showed to be best ( F > 115) in discriminating above classes, while LCA and SINDRI were best ( F > 100) among all indices in discriminating crop residue under different harvesting methods. Comparison of different spectral resolution (from 1 nm to 150 nm) showed that for crop residue discrimination a resolution of 100 nm at 2100-2300 m region would be sufficient to discriminate crop residue from other co-existing classes. [ABSTRACT FROM AUTHOR]

Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated. [ABSTRACT FROM AUTHOR]