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Precision agriculture has increasingly incorporated the use of Unmanned Aerial Vehicles (UAVs) equipped with multispectral cameras. This study examined the influence of different UAV flight altitudes on the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge (NDRE), Optimized Soil-Adjusted Vegetation Index (OSAVI), and Leaf Chlorophyll Index (LCI), indices critical to crop monitoring and health assessment. The experiment was conducted on a 2-hectare winter wheat field at the Institute of Field and Vegetable Crops in Novi Sad, Serbia. The field was divided into 400 plots, each containing different wheat varieties subjected to twenty distinct combinations of artificial mineral fertilizer (NPK) treatments. A DJI P4 Multispectral drone was employed to capture images at altitudes of 30, 60, and 90 meters on three separate dates, corresponding to different plant growth stages: May 9, May 20, and June 6, 2022. All other operating parameters were held constant. The data were processed using the DJI Terra and Pix4D software to generate orthomosaic maps, which were subsequently analyzed using ArcGIS (v10.5, ESRI, Redlands, CA, USA) to calculate the multispectral index values for each plot. The results were statistically analyzed using the STATISTICA Tibco software. The analysis revealed significant differences in the index values based on the UAV flight altitude (p < 0.05). This research underscores the centrality of selecting the optimal UAV flight altitude to ensure the accuracy and reliability of data. While higher altitudes enable UAVs to cover larger areas in a single flight, factors such as image resolution, wind conditions, and the precision of crop health indicators must be considered. These findings offer valuable insights for agricultural professionals seeking to improve crop monitoring and ultimately enhance agricultural productivity through more effective UAV deployment. [ABSTRACT FROM AUTHOR]

Highland ecologies are the most susceptible to climate change, often experiencing intensified impacts. Due to climate change and human activities, there were dramatic changes in the alpine domain of the China–Pakistan Economic Corridor (CPEC), which is a vital project of the Belt and Road Initiative (BRI). The CPEC is subjected to rapid infrastructure expansion, which may lead to potential land surface susceptibility. Hence, focusing on sustainable development goals, mainly SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action), to evaluate the conservation and management practices for the sustainable and regenerative development of the mountainous region, this study aims to assess change detection and find climatic conditions using multispectral indices along the mountainous area of Gilgit and Hunza-Nagar, Pakistan. It has yielded practical and highly relevant implications. For sustainable and regenerative ecologies, this study utilized 30 × 30 m Landsat 5 (TM), Landsat 7 (ETM+), and Landsat-8/9 (OLI and TIRS), and meteorological data were employed to calculate the aridity index (AI). The results of the AI showed a non-significant decreasing trend (−0.0021/year, p > 0.05) in Gilgit and a significant decreasing trend (−0.0262/year, p < 0.05) in Hunza-Nagar. NDVI distribution shows a decreasing trend (−0.00469/year, p > 0.05), while NDWI has depicted a dynamic trend in water bodies. Similarly, NDBI demonstrated an increasing trend, with rates of 79.89%, 87.69%, and 83.85% from 2008 to 2023. The decreasing values of AI mean a drying trend and increasing drought risk, as the study area already has an arid and semi-arid climate. The combination of multispectral indices and the AI provides a comprehensive insight into how various factors affect the mountainous landscape and climatic conditions in the study area. This study has practical and highly relevant implications for policymakers and researchers interested in research related to land use and land cover change, environmental and infrastructure development in alpine regions. [ABSTRACT FROM AUTHOR]

The red seaweed Kappaphycus alvarezii is an economically important gelling agent κappa carrageenan source. Phytochemical analysis has pointed to the presence of various other inorganic and organic compounds, which are expanding the application of biomass as a biostimulant in the agroindustry and as a source of new bioactive molecules in the food, chemical, and pharmaceutical industries. Native to Southeast Asia, K. alvarezii has been introduced as an exotic species in Brazil for commercial large-scale farming. Nowadays, legal farming areas are located in the South and on the South-East coast, but with initiatives to be authorized in the country’s Northeast. The biomass yield in a large-scale farming system can be affected by cultivation techniques and environmental stressors, such as temperature, salinity, water quality, disease, and predators. The use of high-resolution images obtained with unmanned aerial vehicles (UAV or drones) is becoming a popular technology in agriculture, and it has the potential to be employed in seaweed farming to extract a variety of variables and features to predict biomass yield throughout the cultivation period. The present study was conducted to analyze and select multispectral indices obtained from images collected by drone for the detection and quantification of K. alvarezii in a commercial cultivation environment in Brazil. Frequency analysis of pixel values, statistical analyses, and visual interpretations for 24 pre-selected indices was applied according to scores attributed to the efficiency of image segmentation. This analysis resulted in the selection of four indices (ABDI1, ABDI2, CIG, and GNDVI) as the best ones for the segmentation of images in the K. alvarezii commercial farms analyzed. The data obtained are the first step in improving the analysis process of images generated by drones, which will facilitate decision-making and better management, and help scale-up K. alvarezii farming in Brazil.

The red seaweed Kappaphycus alvarezii is an economically important gelling agent κappa carrageenan source. Phytochemical analysis has pointed to the presence of various other inorganic and organic compounds, which are expanding the application of biomass as a biostimulant in the agroindustry and as a source of new bioactive molecules in the food, chemical, and pharmaceutical industries. Native to Southeast Asia, K. alvarezii has been introduced as an exotic species in Brazil for commercial large-scale farming. Nowadays, legal farming areas are located in the South and on the South-East coast, but with initiatives to be authorized in the country's Northeast. The biomass yield in a large-scale farming system can be affected by cultivation techniques and environmental stressors, such as temperature, salinity, water quality, disease, and predators. The use of high-resolution images obtained with unmanned aerial vehicles (UAV or drones) is becoming a popular technology in agriculture, and it has the potential to be employed in seaweed farming to extract a variety of variables and features to predict biomass yield throughout the cultivation period. The present study was conducted to analyze and select multispectral indices obtained from images collected by drone for the detection and quantification of K. alvarezii in a commercial cultivation environment in Brazil. Frequency analysis of pixel values, statistical analyses, and visual interpretations for 24 pre-selected indices was applied according to scores attributed to the efficiency of image segmentation. This analysis resulted in the selection of four indices (ABDI1, ABDI2, CIG, and GNDVI) as the best ones for the segmentation of images in the K. alvarezii commercial farms analyzed. The data obtained are the first step in improving the analysis process of images generated by drones, which will facilitate decision-making and better management, and help scale-up K. alvarezii farming in Brazil. [ABSTRACT FROM AUTHOR]

Deforestation and forest degradation are one of the most complex ecological challenges worldwide. Multispectral imaging and remote sensing help researchers and forest managers to quantify forest loss and degradation. By combining multiple bands of satellite data, multispectral indices can highlight changes in forest structure and reduce the cost and time required for field research. In this research multispectral indices and remote sensing are used in different areas of forestry (such as deforestation, wildfires, phenology) and five examples of remote sensing in forest monitoring are presented. The Normalized Difference Vegetation Index (NDVI) is used to quantify deforestation in ski center in Kopaonik National park. The Normalized Burn Ration (NBR) is used to assess forest fire damage in Šar planina National Park. The Soil Adjusted Vegetation Index (SAVI) is used to monitor some phenological events in Fruška gora National Park. The moisture content of vegetation in Djerdap National Park is analyzed with the Normalized Difference Moisture Index (NDMI) and pest damage is monitored with the NDVI and the Normalized Difference Rededge Vegetation Index (NDRE) in Tara National Park. The results of these five case study analyses show that multispectral imaging provides the most evident results in monitoring deforestation, while pest and disease damage is difficult to detect.

The use of manure in agricultural fields during the wet season can lead to environmental pollution by releasing nitrates into nearby water sources. To address this issue, authorities may impose closed periods during which manure application is prohibited. However, ensuring compliance with these regulations can be challenging, as it is difficult to monitor all fields in a country. To tackle this problem, a solution has been proposed that involves employing machine learning techniques in conjunction with satellite imagery to automatically identify freshly manured fields. This paper investigates the relationship and effectiveness of the Sentinel-2 satellite bands and 51 frequently utilized multispectral indices in the context of precision agriculture, by exploring different feature selection methods. The proposed method achieves nearly 90% F1-Score and detects all test plots of the northern Spanish region, showing its potential for large-scale use in precision agriculture and environmental monitoring. This method incorporates temporal data, resulting in an 8% improvement in the detection F1-Score. Despite their lower spatial resolution, infrared bands have proven to be more effective than visible bands, enhancing the F1-Score by 4%. Furthermore, the use of over 80 features contributes to a 12% increase in the F1-Score compared to using fewer than 10 features. For further research and future studies, a dataset of recently manured plots, verified on-site, has been developed and made publicly available.

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model's predictive performance was optimized. The study's findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas. [ABSTRACT FROM AUTHOR]

Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types. [ABSTRACT FROM AUTHOR]

The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites. [ABSTRACT FROM AUTHOR]

Abstract Background Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. Results Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. Conclusions Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling.

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R 2) = 0.527–0.667] compared to using a single multispectral sensor (R 2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R 2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R 2 varying from 0.677 to 0.729. The best prediction result (R 2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield. [ABSTRACT FROM AUTHOR]

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R2) = 0.527–0.667] compared to using a single multispectral sensor (R2 = 0.130–0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R2 = 0.667, root mean square error (RMSE) = 0.881 t ha–1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha–1. The EWF model outperformed at all the individual growth stages with R2 varying from 0.677 to 0.729. The best prediction result (R2 = 0.729, RMSE = 0.831 t ha–1, RRMSE = 14.3%, and MAE = 0.684 t ha–1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

Background: Soil organic carbon (SOC) affects essential biological, biochemical, and physical soil functions such as nutrient cycling, water retention, water distribution, and soil structure stability. The Andean páramo known as such a high carbon and water storage capacity ecosystem is a complex, heterogeneous and remote ecosystem complicating field studies to collect SOC data. Here, we propose a multi-predictor remote quantification of SOC using Random Forest Regression to map SOC stock in the herbaceous páramo of the Chimborazo province, Ecuador. Results: Spectral indices derived from the Landsat-8 (L8) sensors, OLI and TIRS, topographic, geological, soil taxonomy and climate variables were used in combination with 500 in situ SOC sampling data for training and calibrating a suitable predictive SOC model. The final predictive model selected uses nine predictors with a RMSE of 1.72% and a R2 of 0.82 for SOC expressed in weight %, a RMSE of 25.8 Mg/ha and a R2 of 0.77 for the model in units of Mg/ha. Satellite-derived indices such as VARIG, SLP, NDVI, NDWI, SAVI, EVI2, WDRVI, NDSI, NDMI, NBR and NBR2 were not found to be strong SOC predictors. Relevant predictors instead were in order of importance: geological unit, soil taxonomy, precipitation, elevation, orientation, slope length and steepness (LS Factor), Bare Soil Index (BI), average annual temperature and TOA Brightness Temperature. Conclusions: Variables such as the BI index derived from satellite images and the LS factor from the DEM increase the SOC mapping accuracy. The mapping results show that over 57% of the study area contains high concentrations of SOC, between 150 and 205 Mg/ha, positioning the herbaceous páramo as an ecosystem of global importance. The results obtained with this study can be used to extent the SOC mapping in the whole herbaceous ecosystem of Ecuador offering an efficient and accurate methodology without the need for intensive in situ sampling. [ABSTRACT FROM AUTHOR]

• Tailings and waste rock are the main heat source of the surrounding temperature field. • Topographic indices have the greatest impact on model accuracy improvement. • Planting highly active plants can reduce the thermal environment differences in the mining area. • The MTVET model shows rich and clear LST spatial texture features in typical mine sites. The stripping of soil and vegetation by mining activities in mountainous and hilly areas of southern China has caused drastic surface disturbance and serious ecological and environmental problems. The spatial distribution of Land Surface Temperature (LST) in the mine site can better reflect the ecological disturbances caused by mining and is an important parameter for assessing the ecological environment of the mining sites. The classic Distrad thermal pixel decomposition model was improved by using different regression factors and linear and nonlinear fitting methods in this study, and three multi-factor downscaling models were proposed. Finally, four downscaling models were used to estimate the high spatial resolution LST for three three typical open-pit mining areas and explore the contribution of different surface parameters in the downscaling process from qualitative and quantitative perspectives. The results show that: (1) The MTVET (Extremely Randomized Trees regression model with multitype predictor variables) model exhibits the most accurate prediction accuracy in all three mining areas. The spatial distribution of errors shows that the area share of absolute errors between 0 and 1 K exist in 86.48 %, 81.38 %, and 84.51 % of the areas in Ganzhou rare earth mine, Dexing copper mine and Pingxiang coal mine, respectively. Based on the Root Mean Square Error (RMSE) assessment, the three mines are 0.699 K, 0.790 K, and 0.751 K, respectively, which are all controlled within 1 K; (2) The downscaled results of the MTVET model show rich and clear LST spatial texture features when typical mine sites are selected in each mining areas for detail comparison; (3) It was found that the topographic index has the greatest impact on accuracy enhancement, suggesting that topographic factors have an important influence in the study of LST downscaling in mountainous and hilly areas of southern China. [ABSTRACT FROM AUTHOR]

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) “Index-then-Blend” (IB); and (ii) “Blend-then-Index” (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm.

The Biosphere Reserve of La Mancha Húmeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modeling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2%. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the EuropeanWater Framework Directive and the Habitats Directive. [ABSTRACT FROM AUTHOR]

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) "Index-then-Blend" (IB); and (ii) "Blend-then-Index" (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R2; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm. [ABSTRACT FROM AUTHOR]

Reflectance data from multiseasonal SPOT-5 imagery was combined with monthly measures of water levels collected in the Rhône river delta (Camargue) in 2005 and 2006. Classification tree and regression models using monthly values of 17 multispectral indices and 4 bands, as well as their seasonal variations, were used for predicting the presence and levels of water, independently of vegetation type and density in shallow marshes. Accuracy of the classification model was estimated by cross-validation and by calculating the percentage of correctly classified pixels on the resulting maps using an independent sampling. Goodness-of-fit of the regression model was assessed by calculating the coefficient of correlation between predicted and observed values. Predictive accuracy of both models was estimated by calculating NRMSE for the independent validation sample. Regression model robustness was also tested using Scheffé post-hoc analyses on the residuals. Biophysical parameters of Camargue marsh vegetation were used to interpret misclassifications and model deficiency. Both models were composed of a single variable consisting of a multispectral index using the mid-infrared band. The resulting classification tree provided a cross-validation accuracy of 76% and a map validation accuracy of 83%. With an R=0.5, the regression model predicted water level with a 6-cm precision up to 20cm of water depth. For both approaches, the predictive power of model was most affected by close canopy. This study highlights the usefulness of data mining for long-term monitoring of wetland hydrology based on multispectral indices using the mid-infrared band. [ABSTRACT FROM AUTHOR]

Abstract: Three levels of scale for determining leaf area index (LAI) were explored within an almond orchard of alternating rows of Nonpareil and Monterey varieties using hemispherical photography and mule lightbar (MLB) at ground level up to airborne and satellite imagery. We compared LAI estimates of 56 fisheye photos strategically placed in the orchard to validate 500,000 MLB point scans of a small portion of the aisles between tree rows to water and vegetation indexes of MASTER (MODIS/ASTER simulator) and Landsat 5 imagery. The high correlation of fisheye photo LAI to MLB LAI estimates establishes this new method against the measurement standard within the plant community while significantly increasing sample size. MLB LAI and MASTER vegetation indexes, such as NDWI (normalized difference water index), GMI (Gitelson–Merzlyak index) and NDVI (normalized difference vegetation index), were highly correlated (r 2 =0.90). In addition, a high correlation (r 2 =0.80) between the MLB measured LAI and selected Landsat derived vegetation indexes (VI) was found. This scaling and validation of LAI estimate expands the spatial area and frequency of determination for time series analysis of crop phenology studies. [Copyright &y& Elsevier]

Abstract: Ecologists and conservationists need accurate and replicable tools for monitoring wetland conditions in order to develop and implement adaptive management strategies efficiently. The Rhone Delta (Camargue) in southern France encloses 9200ha of fragmented reed marshes actively managed for reed harvesting, waterfowl hunting or cattle grazing, and holding significant numbers of vulnerable European birds. We used multi-season SPOT-5 data in conjunction with ground survey to assess the predictive power of satellite imagery in modelling indicators of reed structure (height, diameter, density and cover of green/dry stems) relevant to ecosystem management and bird ecology. All indicators could be predicted accurately with a combination of bands (SWIR, NIR) and indices (SAVI, OSAVI, NDWI, DVI, DVW, MSI) issued from scenes of March, June, July, September or December and subtraction between these. All models were robust when validated with an independent set of satellite and field data. The high spatial resolution of SPOT-5 scenes (pixel of 10×10m) permits the monitoring of detailed attributes characterizing the reed ecosystem across a large spatial extent, providing a scientifically-based, replicable tool for managers, stakeholders and decision-makers to follow wetland conditions in the short and long-term. Combined with models on the ecological requirements of vulnerable bird species, these tools can provide maps of potential species ranges at spatial extents that are relevant to ecosystem functioning and bird populations. [Copyright &y& Elsevier]

Abstract: Multiseason reflectance data from radiometrically and geometrically corrected multispectral SPOT-5 images of 10-m resolution were combined with thorough field campaigns and land cover digitizing using a binary classification tree algorithm to estimate the area of marshes covered with common reeds (Phragmites australis) and submerged macrophytes (Potamogeton pectinatus, P. pusillus, Myriophyllum spicatum, Ruppia maritima, Chara sp.) over an area of 145,000ha. Accuracy of these models was estimated by cross-validation and by calculating the percentage of correctly classified pixels on the resulting maps. Robustness of this approach was assessed by applying these models to an independent set of images using independent field data for validation. Biophysical parameters of both habitat types were used to interpret the misclassifications. The resulting trees provided a cross-validation accuracy of 98.7% for common reed and 97.4% for submerged macrophytes. Variables discriminating reed marshes from other land covers were the difference in the near-infrared band between March and June, the Optimized Soil Adjusted Vegetation Index of December, and the Normalized Difference Water Index (NDWI) of September. Submerged macrophyte beds were discriminated with the shortwave-infrared band of December, the NDWI of September, the red band of September and the Simple Ratio index of March. Mapping validations provided accuracies of 98.6% (2005) and 98.1% (2006) for common reed, and 86.7% (2005) and 85.9% (2006) for submerged macrophytes. The combination of multispectral and multiseasonal satellite data thus discriminated these wetland vegetation types efficiently. Misclassifications were partly explained by digitizing inaccuracies, and were not related to biophysical parameters for reedbeds. The classification accuracy of submerged macrophytes was influenced by the proportion of plants showing on the water surface, percent cover of submerged species, water turbidity, and salinity. Classification trees applied to time series of SPOT-5 images appear as a powerful and reliable tool for monitoring wetland vegetation experiencing different hydrological regimes even with a small training sample (N =25) when initially combined with thorough field measurements. [Copyright &y& Elsevier]

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires. [ABSTRACT FROM AUTHOR]