Assessment of different methods for shadow detection in high-resolution optical imagery and evaluation of shadow impact on calculation of NDVI, and evapotranspiration

Vegetation as an ecological indicator in assessing environmental restoration in mining areas

As global demand for natural resources escalates, the environmental impact stemming from resource extraction has risen to the forefront of contemporary discussions. This paper probed the potential of using vegetation cover as an ecological barometer to gauge the level of environmental damage and restoration in mining areas: a decline in vegetation cover may signify detrimental impacts from intense mining activities, while an increase may indicate effective local environmental stewardship. Therefore, this paper undertook an assessment and discussion of mining damage and environmental management at China's Ta'ershan Mining Area since 2007, calculating and visualizing FVC (Fractional Vegetation Cover) of the Ta'ershan Mining Area to track changes in vegetation cover between 2007 and 2021. Changes in vegetation cover in the Ta'ershan Mining Area could act as a reflection of both mining-induced damage and subsequent successful environmental management by local authorities, providing a practical way to evaluate ecological effects in resource development.

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

BACKGROUND

The advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop's condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements.

RESULTS

The results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The [Formula: see text] of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The [Formula: see text] of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition.

CONCLUSIONS

Our work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning

Disease incidence (DI) and metrics of disease severity are relevant parameters for decision making in plant protection and plant breeding. To develop automated and sensor-based routines, a sugar beet variety trial was inoculated with Cercospora beticola and monitored with a multispectral camera system mounted to an unmanned aerial vehicle (UAV) over the vegetation period. A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters. Features based on the digital surface model, vegetation indices, shadow condition, and image resolution improved classification performance in comparison with using single multispectral channels in 12 and 6% of diseased and soil regions, respectively. With a postprocessing step, area-related parameters were computed after classification. Results of this pipeline also included extraction of DI and disease severity (DS) from UAV data. The calculated area under disease progress curve of DS was 2,810.4 to 7,058.8%.days for human visual scoring and 1,400.5 to 4,343.2%.days for UAV-based scoring. Moreover, a sharper differentiation of varieties compared with visual scoring was observed in area-related parameters such as area of complete foliage (A_F), area of healthy foliage (A_H), and mean area of lesion by unit of foliage ([Formula: see text]). These advantages provide the option to replace the laborious work of visual disease assessments in the field with a more precise, nondestructive assessment via multispectral data acquired by UAV flights.[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Disentangling Soil, Shade, and Tree Canopy Contributions to Mixed Satellite Vegetation Indices in a Sparse Dry Forest

Remote sensing (RS) for vegetation monitoring can involve mixed pixels with contributions from vegetation and background surfaces, causing biases in signals and their interpretations, especially in low-density forests. In a case study in the semi-arid Yatir forest in Israel, we observed a mismatch between satellite (Landsat 8 surface product) and tower-based (Skye sensor) multispectral data and contrasting seasonal cycles in near-infrared (NIR) reflectance. We tested the hypothesis that this mismatch was due to the different fractional contributions of the various surface components and their unique reflectance. Employing an unmanned aerial vehicle (UAV), we obtained high-resolution multispectral images over selected forest plots and estimated the fraction, reflectance, and seasonal cycle of the three main surface components (canopy, shade, and sunlit soil). We determined that the Landsat 8 data were dominated by soil signals (70%), while the tower-based data were dominated by canopy signals (95%). We then developed a procedure to resolve the canopy (i.e., tree foliage) normalized difference vegetation index (NDVI) from the mixed satellite data. The retrieved and corrected canopy-only data resolved the original mismatch and indicated that the spatial variations in Landsat 8 NDVI were due to differences in stand density, while the canopy-only NDVI was spatially uniform, providing confidence in the local flux tower measurements.

Effect of the Shadow Pixels on Evapotranspiration Inversion of Vineyard: A High-Resolution UAV-Based and Ground-Based Remote Sensing Measurements

Due to the proliferation of precision agriculture, the obstacle of estimating evapotranspiration (ET) and its components from shadow pixels acquired from remote sensing technology should not be neglected. To accurately detect shaded soil and leaf pixels and quantify the implications of shadow pixels on ET inversion, a two-year field-scale observation was carried out in the growing season for a pinot noir vineyard. Based on high-resolution remote sensing sensors covering visible light, thermal infrared, and multispectral light, the supervised classification was applied to detect shadow pixels. Then, we innovatively combined the normalized difference vegetation index with the three-temperature model to quantify the proportion of plant transpiration (T) and soil evaporation (E) in the vineyard ecosystem. Finally, evaluated with the eddy covariance system, we clarified the implications of the shadow pixels on the ET estimation and the spatiotemporal patterns of ET in a vineyard system by considering where shadow pixels were presented. Results indicated that the shadow detection process significantly improved reliable assessment of ET and its components. (1) The shaded soil pixels misled the land cover classification, with the mean canopy cover ignoring shadows 1.68–1.70 times more often than that of shaded area removal; the estimation accuracy of ET can be improved by 4.59–6.82% after considering the effect of shaded soil pixels; and the accuracy can be improved by 0.28–0.89% after multispectral correction. (2) There was a 2 °C canopy temperature discrepancy between sunlit leaves and shaded leaves, meaning that the estimation accuracy of T can be improved by 1.38–7.16% after considering the effect of shaded canopy pixels. (3) Simultaneously, the characteristics showed that there was heterogeneity of ET in the vineyard spatially and that E and T fluxes accounted for 238.05 and 208.79 W·m−2, respectively; the diurnal variation represented a single-peak curve, with a mean of 0.26 mm/h. Our findings provide a better understanding of the influences of shadow pixels on ET estimation using remote sensing techniques.

Retrieval of Nitrogen Content in Apple Canopy Based on Unmanned Aerial Vehicle Hyperspectral Images Using a Modified Correlation Coefficient Method

The accurate retrieval of nitrogen content based on Unmanned Aerial Vehicle (UAV) hyperspectral images is limited due to uncertainties in determining the locations of nitrogen-sensitive wavelengths. This study developed a Modified Correlation Coefficient Method (MCCM) to select wavelengths sensitive to nitrogen content. The Normalized Difference Canopy Shadow Index (NDCSI) was applied to remove the shadows from UAV hyperspectral images, thus yielding the canopy spectral information. The MCCM was then used to screen the bands sensitive to nitrogen content and to construct spectral characteristic parameters. Finally, the optimal model for nitrogen content retrieval was established and selected. As a result, the screened sensitive wavelengths for nitrogen content selected were 470, 474, 490, 514, 582, 634, and 682 nm, respectively. Among the nitrogen content retrieval models, the best model was the Support Vector Machine (SVM) model. In the training set, this model outperformed the other models with an R2 of 0.733, RMSE of 6.00%, an nRMSE of 12.76%, and a MAE of 4.49%. Validated by the ground-measured nitrogen content, this model yielded good performance with an R2 of 0.671, an RMSE of 4.73%, an nRMSE of 14.83%, and a MAE of 3.98%. This study can provide a new method for vegetation nutrient content retrieval based on UAV hyperspectral data.

Actual Evapotranspiration from UAV Images: A Multi-Sensor Data Fusion Approach

Multispectral imaging using Unmanned Aerial Vehicles (UAVs) has changed the pace of precision agriculture. Actual evapotranspiration (ETa) from the very high spatial resolution of UAV images over agricultural fields can help farmers increase their production at the lowest possible cost. ETa estimation using UAVs requires a full package of sensors capturing the visible/infrared and thermal portions of the spectrum. Therefore, this study focused on a multi-sensor data fusion approach for ETa estimation (MSDF-ET) independent of thermal sensors. The method was based on sharpening the Landsat 8 pixels to UAV spatial resolution by considering the relationship between reference ETa fraction (ETrf) and a Vegetation Index (VI). Four Landsat 8 images were processed to calculate ETa of three UAV images over three almond fields. Two flights coincided with the overpasses and one was in between two consecutive Landsat 8 images. ETrf was chosen instead of ETa to interpolate the Landsat 8-derived ETrf images to obtain an ETrf image on the UAV flight. ETrf was defined as the ratio of ETa to grass reference evapotranspiration (ETr), and the VIs tested in this study included the Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and Land Surface Water Index (LSWI). NDVI performed better under the study conditions. The MSDF-ET-derived ETa showed strong correlations against measured ETa, UAV- and Landsat 8-based METRIC ETa. Also, visual comparison of the MSDF-ET ETa maps was indicative of a promising performance of the method. In sum, the resulting ETa had a higher spatial resolution compared with thermal-based ETa without the need for the Albedo and hot/cold pixels selection procedure. However, wet soils were poorly detected, and in cases of continuous cloudy Landsat pixels the long interval between the images may cause biases in ETa estimation from the MSDF-ET method. Generally, the MSDF-ET method reduces the need for very high resolution thermal information from the ground, and the calculations can be conducted on a moderate-performance computer system because the main image processing is applied on Landsat images with coarser spatial resolutions.

AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture

Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA.

The Influence of Shadow Effects on the Spectral Characteristics of Glacial Meltwater

The phenomenon of shadows due to glaciers is investigated in Antarctica. The observed shadow effect disrupts analyses conducted by remote sensing and is a challenge in the assessment of sediment meltwater plumes in polar marine environments. A DJI Inspire 2 drone equipped with a Zenmuse x5s camera was used to generate a digital surface model (DSM) of 6 King George Island glaciers: Ecology, Dera, Zalewski, Ladies, Krak, and Vieville. On this basis, shaded areas of coves near glaciers were traced. For the first time, spectral characteristics of shaded meltwater were observed with the simultaneous use of a Sequoia+ spectral camera mounted on a Parrot Bluegrass drone and in Landsat 8 satellite images. In total, 44 drone flights were made, and 399 satellite images were analyzed. Among them, four drone spectral images and four satellite images were selected, meeting the condition of a visible shadow. For homogeneous waters (deep, low turbidity, without ice phenomena), the spectral properties tend to change during the approach to an obstacle casting a shadow especially during low shortwave downward radiation. In this case, in the shade, the amount of radiation reflected in the green spectral band decreases by 50% far from the obstacle and by 43% near the obstacle, while in near infrared (NIR), it decreases by 42% and 21%, respectively. With highly turbid, shallow water and ice phenomena, this tendency does not occur. It was found that the green spectral band had the highest contrast in the amount of reflected radiation between nonshaded and shaded areas, but due to its high sensitivity, the analysis could have been overestimated. The spectral properties of shaded meltwater differ depending on the distance from the glacier front, which is related to the saturation of the water with sediment particles. We discovered that the pixel aggregation of uniform areas caused the loss of detailed information, while pixel aggregation of nonuniform, shallow areas with ice phenomena caused changes and the loss of original information. During the aggregation of the original pixel resolution (15 cm) up to 30 m, the smallest error occurred in the area with a homogeneous water surface, while the greatest error (over 100%) was identified in the places where the water was strongly cloudy or there were ice phenomena.

Incorporation of Unmanned Aerial Vehicle (UAV) Point Cloud Products into Remote Sensing Evapotranspiration Models

In recent years, the deployment of satellites and unmanned aerial vehicles (UAVs) has led to production of enormous amounts of data and to novel data processing and analysis techniques for monitoring crop conditions. One overlooked data source amid these efforts, however, is incorporation of 3D information derived from multi-spectral imagery and photogrammetry algorithms into crop monitoring algorithms. Few studies and algorithms have taken advantage of 3D UAV information in monitoring and assessment of plant conditions. In this study, different aspects of UAV point cloud information for enhancing remote sensing evapotranspiration (ET) models, particularly the Two-Source Energy Balance Model (TSEB), over a commercial vineyard located in California are presented. Toward this end, an innovative algorithm called Vegetation Structural-Spectral Information eXtraction Algorithm (VSSIXA) has been developed. This algorithm is able to accurately estimate height, volume, surface area, and projected surface area of the plant canopy solely based on point cloud information. In addition to biomass information, it can add multi-spectral UAV information to point clouds and provide spectral-structural canopy properties. The biomass information is used to assess its relationship with in situ Leaf Area Index (LAI), which is a crucial input for ET models. In addition, instead of using nominal field values of plant parameters, spatial information of fractional cover, canopy height, and canopy width are input to the TSEB model. Therefore, the two main objectives for incorporating point cloud information into remote sensing ET models for this study are to (1) evaluate the possible improvement in the estimation of LAI and biomass parameters from point cloud information in order to create robust LAI maps at the model resolution and (2) assess the sensitivity of the TSEB model to using average/nominal values versus spatially-distributed canopy fractional cover, height, and width information derived from point cloud data. The proposed algorithm is tested on imagery from the Utah State University AggieAir sUAS Program as part of the ARS-USDA GRAPEX Project (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) collected since 2014 over multiple vineyards located in California. The results indicate a robust relationship between in situ LAI measurements and estimated biomass parameters from the point cloud data, and improvement in the agreement between TSEB model output of ET with tower measurements when employing LAI and spatially-distributed canopy structure parameters derived from the point cloud data.

Validation of digital surface models (DSMs) retrieved from unmanned aerial vehicle (UAV) point clouds using geometrical information from shadows

Theoretically, the appearance of shadows in aerial imagery is not desirable for researchers because it leads to errors in object classification and bias in the calculation of indices. In contrast, shadows contain useful geometrical information about the objects blocking the light. Several studies have focused on estimation of building heights in urban areas using the length of shadows. This type of information can be used to predict the population of a region, water demand, etc., in urban areas. With the emergence of unmanned aerial vehicles (UAVs) and the availability of high- to super-high-resolution imagery, the important questions relating to shadows have received more attention. Three-dimensional imagery generated using UAV-based photogrammetric techniques can be very useful, particularly in agricultural applications such as in the development of an empirical equation between biomass or yield and the geometrical information of canopies or crops. However, evaluating the accuracy of the canopy or crop height requires labor-intensive efforts. In contrast, the geometrical relationship between the length of the shadows and the crop or canopy height can be inversely solved using the shadow length measured. In this study, object heights retrieved from UAV point clouds are validated using the geometrical shadow information retrieved from three sets of high-resolution imagery captured by Utah State University's AggieAir UAV system. These flights were conducted in 2014 and 2015 over a commercial vineyard located in California for the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program. The results showed that, although this approach could be computationally expensive, it is faster than fieldwork and does not require an expensive and accurate instrument such as a real-time kinematic (RTK) GPS.

The impact of shadows on partitioning of radiometric temperature to canopy and soil temperature based on the contextual two-source energy balance model (TSEB-2T)

Tests of the most recent version of the two-source energy balance model have demonstrated that canopy and soil temperatures can be retrieved from high-resolution thermal imagery captured by an unmanned aerial vehicle (UAV). This work has assumed a linear relationship between vegetation indices (VIs) and radiometric temperature in a square grid (i.e., 3.6 m × 3.6 m) that is coarser than the resolution of the imagery acquired by the UAV. In this method, with visible, near infrared (VNIR), and thermal bands available at the same high-resolution, a linear fit can be obtained over the pixels located in a grid, where the x-axis is a vegetation index (VI) and the y-axis is radiometric temperature. Next, with an accurate VI threshold that separates soil and vegetation pixels from one another, the corresponding soil and vegetation temperatures can be extracted from the linear equation. Although this method is simpler than other approaches, such as TSEB with Priestly-Taylor (TSEB-PT), it could be sensitive to VIs and the parameters that affect VIs, such as shadows. Recent studies have revealed that, on average, the values of VIs, such as normalized difference vegetation index (NDVI) and leaf area index (LAI), that are located in sunlit areas are greater than those in shaded areas. This means that involving or compensating for shadows will affect the linear relationship parameters (slope and bias) between radiometric temperature and VI, as well as thresholds that separate soil and vegetation pixels. This study evaluates the impact of shadows on the retrieval of canopy and soil temperature data from four UAV images before and after applying shadow compensation techniques. The retrieved temperatures, using the TSEB-2T approach, both before and after shadow correction, are compared to the average temperature values for both soil and canopy in each grid. The imagery was acquired by the Utah State University AggieAir UAV system over a commercial vineyard located in California as part of the USDA Agricultural Research Service Grape Remote sensing Atmospheric Profile and Evapotranspiration Experiment (GRAPEX) Program during 2014 to 2016. The results of this study show when it is necessary to employ shadow compensation methods to retrieve vegetation and soil temperature directly.

Evaluation of TSEB turbulent fluxes using different methods for the retrieval of soil and canopy component temperatures from UAV thermal and multispectral imagery

The thermal-based Two-Source Energy Balance (TSEB) model partitions the evapotranspiration (ET) and energy fluxes from vegetation and soil components providing the capability for estimating soil evaporation (E) and canopy transpiration (T). However, it is crucial for ET partitioning to retrieve reliable estimates of canopy and soil temperatures and net radiation, as the latter determines the available energy for water and heat exchange from soil and canopy sources. These two factors become especially relevant in row crops with wide spacing and strongly clumped vegetation such as vineyards and orchards. To better understand these effects, very high spatial resolution remote-sensing data from an unmanned aerial vehicle were collected over vineyards in California, as part of the Grape Remote sensing and Atmospheric Profile and Evapotranspiration eXperiment and used in four different TSEB approaches to estimate the component soil and canopy temperatures, and ET partitioning between soil and canopy. Two approaches rely on the use of composite T rad, and assume initially that the canopy transpires at the Priestley-Taylor potential rate. The other two algorithms are based on the contextual relationship between optical and thermal imagery partition T rad into soil and canopy component temperatures, which are then used to drive the TSEB without requiring a priori assumptions regarding initial canopy transpiration rate. The results showed that a simple contextual algorithm based on the inverse relationship of a vegetation index and T rad to derive soil and canopy temperatures yielded the closest agreement with flux tower measurements. The utility in very high-resolution remote-sensing data for estimating ET and E and T partitioning at the canopy level is also discussed.