Multispectral Datasets Research Articles

Semantic segmentation of clouds in satellite images based on U-Net++ architecture and attention mechanism

The presence of clouds in satellite imagery may pose hindrances to the accurate and reliable analysis of the objects present on the land. Therefore, automatic cloud detection is a vital pre-processing step before lending the satellite images to any further analysis. This is a challenging task due to the varying thickness and densities of clouds. It is also very difficult to distinguish clouds from certain terrains such as snow and white sandy beaches. This paper proposes a deep learning based algorithm to solve the problem of cloud segmentation on the Landsat 8 multispectral dataset, 95-Cloud: SEUNet++. Specifically, the proposed model consists of a U-Net++ semantic segmentation model with a lightweight channel attention mechanism. We also experimented with using different encoder backbones in the U-Net++ encoder-decoder architecture such as ResNet (Residual Neural Networks) variants including ResNet-18, ResNet-34, ResNet-50, and ResNet-101, DenseNet-264, CSPNet (Cross Stage Partial Network), and EfficientNet-B8 and compared their performance. The experimental results show that the proposed architecture achieves an IoU (Intersection over Union) score of 91.8 %, improving the state-of-the-art on the task by 0.23 %. It also boosts the accuracy, precision, and recall values creating crisp cloud boundaries and detecting even thin layers of clouds. We also experimented using transfer learning and found that it has a positive impact on the cloud segmentation task. The proposed model also beats the original U-Net++ architecture in terms of various evaluation metrics such as the IoU score, accuracy, precision, and recall. The experimental results thereby demonstrate that our model is computationally efficient and achieves precise segmentation results.

Estimation of surface melt induced melt pond depths over Amery Ice Shelf, East Antarctica using Multispectral and ICESat-2 data

Surface melting is increasing over the Antarctica continent, with unknown impacts on glacier and ice sheet dynamics at the ice-bed interface. Surface meltwater drains to the bed of Antarctica ice sheets and outlet glaciers, causing accelerations in ice flow (up to 100% greater than the annual mean). Because melt occurrences are currently erratic (also Antarctica surface melt refreezes), effective subglacial drainage cannot be maintained, resulting in repeated short-lived ice flow disturbances and calving of ice shelves. Longterm exposure to strong surface melting promotes the formation of melt ponds and subsequently Supraglacial lakes and therefore there is possibility of hydrofracturing. The present study aims to estimate the volume of surface melt-induced melt ponds over the Amery Ice Shelf (AIS) using a physically-based radiative transfer model. The AIS, located on Antarctica's east coast, is one of the world's biggest glacier drainage basins. The ICESat-2 geolocated photon data were used to validate the depth estimates of melt ponds derived from Landsat-8 and Sentinel-2 multispectral datasets based on the model. The average depth estimated is around 1.9±0.6 m for the melt ponds adopted for the present study. The present study aims to quantify surface melt over the Antarctica continent (AIS region) in the context of India’s scientific credentials in polar science. We recommend monitoring extensive surface melt and subsurface refreezing which might result in more ponding above impermeable ice layers in the coming years.

Land Surface Temperature Regulation Ecosystem Service: A Case Study of Jaipur, India, and the Urban Island of Jhalana Reserve Forest

Although Land Surface Temperatures (LSTs) are on the rise globally, the distribution of LSTs varies depending on the land cover type. Urban Heat Island and Urban Cool Island effects act differently, especially in semi-arid regions. Therefore, we identify demi-decadal, seasonal, and zonal differences in LSTs in a semi-arid region in the city of Jaipur, where zones include rural and urban areas that encircle the Jhalana Reserve Forest (JRF). After deriving LSTs from remotely sensed thermal bands of Landsat satellites’ Multi-spectral datasets, we found that there is a significant difference in LST (p < 0.01) among the zones. In addition, LSTs were found to be significantly lower in JRF compared to Urban and Rural areas in all seasons and all study years, which indicates the urban cooling effect due to the presence of the forest. Nevertheless, summer LSTs have warmed with a mean difference of 4.8 °C between 2000 and 2020. Therefore, our study supports the promotion of Urban Forests, especially in semi-arid zones, for inculcating LST regulation ecosystem services to enrich and enhance the standard of living of the human population.

Robust Multispectral Visual-Inertial Navigation With Visual Odometry Failure Recovery

Besides the large amount of information multispectral imaging offers, multispectral visual odometry remains overlooked due to the dissimilarity between modalities. In order to tackle the challenging feature matching between multispectral stereo images and to overcome the lack of robust multispectral visual localisation solutions, a novel approach is proposed in this paper. It consists in tracking features in each modality simultaneously, in a monocular manner and then, estimating motion in a windowed bundle adjustment framework and using the geometry of the stereo setup to recover the missing scale. The estimation is robustified by selecting adequate keyframes based on feature parallax and by maximising the mutual information between all the features reprojected in the stereo pair. Furthermore, the proposed multispectral visual odometry solution is integrated in an error-state Kalman filter framework to deal with challenging environments, where the quality of images is reduced. Two measurements models, using absolute and relative camera poses, are presented. The superiority of relative poses is then shown by providing a failure recovery algorithm which relies on inertial data when visual data are not accessible. The algorithm was tested on innovative series of visible-thermal multispectral datasets, acquired from a car with real driving conditions. An overall error of around 2% of the travelled distance was achieved on these datasets.

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

Monocular Depth Estimation from a Single Infrared Image

Thermal infrared imaging is attracting much attention due to its strength against illuminance variation. However, because of the spectral difference between thermal infrared images and RGB images, the existing research on self-supervised monocular depth estimation has performance limitations. Therefore, in this study, we propose a novel Self-Guided Framework using a Pseudolabel predicted from RGB images. Our proposed framework, which solves the problem of appearance matching loss in the existing framework, transfers the high accuracy of Pseudolabel to the thermal depth estimation network by comparing low- and high-level pixels. Furthermore, we propose Patch-NetVLAD Loss, which strengthens local detail and global context information in the depth map from thermal infrared imaging by comparing locally global patch-level descriptors. Finally, we introduce an Image Matching Loss to estimate a more accurate depth map in a thermal depth network by enhancing the performance of the Pseudolabel. We demonstrate that the proposed framework shows significant performance improvement even when applied to various depth networks in the KAIST Multispectral Dataset.

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

A Bayesian optimal convolutional neural network approach for classification of coal and gangue with multispectral imaging

The precise classification of coal and gangue is a crucial link for effective sorting and efficient utilization. However, there are some shortcomings in traditional methods, such as water consumption, coal slime pollution, and great influence of environmental factors, and so on. Here, multispectral imaging technology combined with the convolutional neural network (CNN) was applied to classify coal and gangue, in which the hyperparameters of the CNN model were optimized by Bayesian algorithm. The multispectral images in the range of 675–975 nm of 209 pieces of coal and 201 pieces of gangue, which came from the Huainan mining area, were collected. The CNN and traditional modeling methods (combination strategy of image feature extraction and classifier) were employed to develop identification models, and the classification results were analyzed and compared on the multispectral dataset of coal and gangue. The identification analysis model based on CNN had the best performance, and the F1 score reached 1.00. At this time, the hyperparameters of the model are as follows: network depth was 1, initial learning rate was 0.012939, random gradient descent momentum was 0.83813, and L2 regularization intensity was 0.0099852. Moreover, the robustness of the CNN identification model was verified by introducing different levels of noise signals. The identification analysis model based on the CNN can quickly and accurately identify coal and gangue without complex image processing steps, and the model has certain anti-interference ability, which will promote the progress of automatic separation technology for coal and gangue.

Pixelwise instance segmentation of leaves in dense foliage

• Pixelwise Instance segmentation of leaves is performed on mixes of plant species. • A new shape-based loss function is proposed and applied to each connected component. • The segmentation output is refined using a Watershed and a DeepIndices approach. • A benchmark of the method is performed on “leaf segmentation challenges”. • A new multi-spectral dataset of 300 images acquired in natural light is shared. Detecting and identifying plants using image analysis is a key step for many applications in precision agriculture (from phenotyping to site specific weed management). Instance segmentation is usually carried on to detect entire plants. However, the shape of the detected objects changes between individuals and growth stages. A relevant approach to reduce these variations is to narrow the detection on the leaf. Nevertheless, segmenting leaves is a difficult task, when images contain mixes of plant species, and when individuals overlap, particularly in an uncontrolled outdoor environment. To leverage this issue, this study based on recent Convolutional Neural Network mechanisms, proposes a pixelwise instance segmentation to detect leaves in dense foliage environment. It combines “deep contour aware” (to separate the inner of big leaves from its edges), “Leaf Segmentation trough classification of edges” (to separate instances with a specific inner edges) and “Pyramid CNN for Dense Leaves” (to consider edges at different scales). But the segmentation output is also refined using a Watershed and a method to compute optimized vegetation indices (DeepIndices). The method is compared to others running the leaf segmentation challenge (provided by the International Network on Plant Phenotyping) and applied on an external dataset of Komatsuna plants. In addition, a new multispectral dataset of 300 images of bean plants is introduced (with dense foliage, individuals overlapping, mixes of species and natural lighting conditions). The ground truth (e.g. the leaves boundaries) is defined by labelled polygons and can be used to train and assess the performance of various algorithms dedicated to leaf detection or crop/weed classification. On the usual datasets, the performances of the proposed method are similar to those of the usual methods involved in the leaf segmentation challenges. On the new dataset, their results are strongly better than those of the usual RCNN method. Remaining errors are bad fusion between neighboring areas and over segmentation of multi-foliate leaves. Structural analysis methods could be studied in order to overcome these deficiencies.

V2T-GAN: Three-Level Refined Light-Weight GAN with Cascaded Guidance for Visible-to-Thermal Translation.

Infrared image simulation is challenging because it is complex to model. To estimate the corresponding infrared image directly from the visible light image, we propose a three-level refined light-weight generative adversarial network with cascaded guidance (V2T-GAN), which can improve the accuracy of the infrared simulation image. V2T-GAN is guided by cascading auxiliary tasks and auxiliary information: the first-level adversarial network uses semantic segmentation as an auxiliary task, focusing on the structural information of the infrared image; the second-level adversarial network uses the grayscale inverted visible image as the auxiliary task to supplement the texture details of the infrared image; the third-level network obtains a sharp and accurate edge by adding auxiliary information of the edge image and a displacement network. Experiments on the public dataset Multispectral Pedestrian Dataset demonstrate that the structure and texture features of the infrared simulation image obtained by V2T-GAN are correct, and outperform the state-of-the-art methods in objective metrics and subjective visualization effects.

Change detection for multispectral images using modified semantic segmentation network

Change detection is a significant issue for understanding the changes occurring on the land surface. We propose a change detection approach based on a semantic segmentation network from multispectral (MS) images. Different from the traditional approaches that learn deep features from the change index or establish mapping relations from patches, the proposed approach employs the semantic segmentation network UNet++ for end-to-end change detection. Nevertheless, in UNet++, the deep feature is directly upsampled from the node in the lower level and does not involve much information from the nodes in the other levels. To cope with this problem and further enhance its robustness, the zigzag UNet++ (ZUNet++) is developed. In ZUNet++, the zigzag connection between nodes can be found, so the inputs of the node involve not only the upsampled deep feature but also the downsampled shallow feature, i.e., the network fuses multiple feature information. In addition, as few MS training datasets are available, we designed a strategy in which each MS image is transferred into several pseudo-RGB images; thus the network is trained by available RGB training sets and can be applied to the testing MS datasets. In the experiment, three real testing MS datasets that reflect different types of changes in Xi’an City are used. Experimental results show that, upon determining the appropriate parameter, the proposed ZUNet++ outperforms the other state-of-the-art approaches, demonstrating its feasibility and effectiveness.

Palmprint Translation Network for Cross-Spectral Palmprint Recognition

Nowadays, palmprint recognition has been well developed since plenty of promising algorithms have emerged. Palmprints have also been applied under various authentication scenarios. However, these approaches are designed and tested only when the registration images and probe images are taken under the same illumination condition; thus, a cross-spectral performance degradation is speculated. Therefore, we test the cross-spectral performance of extended binary orientation co-occurrence vector (E-BOCV), which is unsatisfactory, illustrating the necessity of a specific algorithm. Trying to achieve the cross-spectral palmprint recognition with image-to-image translation, we have made efforts in the following two aspects. First, we introduce a scheme to evaluate the images of different spectra, which is a reliable basis for translation direction determination. Second, in this paper, we propose a palmprint translation convolutional neural network (PT-net) and the performance of translation from NIR to blue is tested on the PolyU multispectral dataset, which achieves a 91% decrease in Top-1 error using E-BOCV as the recognition framework.

A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil

The recent developments in the performance and miniaturization of uncrewed aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation.

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.

A Quadratic Morphological Deep Neural Network Fusing Radar and Optical Data for the Mapping of Burned Areas

Wildfires are considered as one of the most disturbing factors in forest areas and high-density vegetation regions. The mapping of wildfires is particularly important for fire prediction and burned biomass estimation. Therefore, accurate and timely mapping of burned areas is of great importance and has a key role in disaster management. Estimation of burned areas from multi-spectral datasets is challenging, because of the complexity of the background and the different reflections of wildfires on the targets. To this end, this study presents a novel burned-area mapping framework based on fusion of multi-temporal Sentinel-1 coherence imagery and post-event Sentinel-2 imagery. The proposed framework uses hybrid quadratic morphological (QM) operations and convolution layers for deep feature extraction. The proposed architecture is known as the QMDNN-Net, where the overall framework of QMDNN-Net is based on a deep Siamese network. QMDNN-Net has double streams for extracting deep features from multi-temporal coherence data and from Sentinel-2 imagery. The streams are similar to each other, and have the same number of group-dilated convolution blocks and QM layers. QMDNN-Net is defined based on quadratic dilation and erosion, and then it takes the average of these as output. The results of wildfire mapping with QMDNN-Net are evaluated here with two real-world wildfire datasets based on Sentinel-1 and Sentinel-2 imagery. The results show that QMDNN-Net achieves an overall accuracy and Kappa coefficient of 95.5% and 0.9, respectively, and outperforms other state-of-the-art methods.

Unsupervised Multiple Change Detection in Remote Sensing Images via Generative Representation Learning Network

With abundant temporal, spectral, and spatial information, multispectral images are proficient for acquiring a superior comprehension of the Earth’s condition and its changes, which enables the achievement of multiple change detection (CD) tasks. However, high temporal, spatial, and spectral information of data brings obstacles to perform multiple change analysis due to the lack of effective feature extraction operation. In addition, the traditional multiple CD methods rely too much on manual participation. Here, a generative representation learning network (GRN) and a cyclic clustering technique are combined into a unified model, which is driven to learn spatial–temporal–spectral features for unsupervised multiple CD. GRN aims to efficiently extract and merge robust difference information with a recurrent learning mechanism for self-adaptive classification refinement, in which different types of changes can be identified and highlighted. Furthermore, a cyclic training strategy is designed to refine the clustering-friendly features, in which similar change types are gradually merged into the same classes. Meanwhile, the number of change types will be optimized through a self-adaptive way and eventually converge to its stable state, which is close to the real distribution. Experimental results on real multispectral datasets demonstrate the effectiveness and superiority of the proposed model on multiple CD.

A Spectral-Spatial Feature Extraction Method With Polydirectional CNN for Multispectral Image Compression

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.

Unsupervised thermal-to-visible domain adaptation method for pedestrian detection

Pedestrian detection is a common task in the research area of video analysis and its results lay the foundations of a wide range of applications. It is commonly known that under challenging illumination and weather conditions, conventional visible cameras perform poorly and this limitation could be catered using thermal imagery. But, due to the fact that annotated thermal datasets are less available compared to the visible ones, in this paper we emphasis the need for leveraging information from the visible domain to perform detection in the thermal domain at no additional annotation cost. Precisely, we propose a domain adaptation method by incorporating feature distribution alignments into Faster R-CNN architecture at different levels and at two phases of the network. The resulting proposed adaptive detector has the advantage of covering different aspects of the domain shift in order to improve the overall performance. The proposed detector is evaluated on KAIST multispectral dataset and the obtained results demonstrate its effectiveness by improving the adaptability in the thermal domain. Also, by means of comparisons to other existing works, better results are obtained. Additional experiments are conducted on other datasets to further justify the obtained results.

Monitoring of simulated clandestine graves of dismembered victims using UAVs, electrical tomography, and GPR over one year to aid investigations of human rights violations in Colombia, South America.

In most Latin American countries, there are significant numbers of missing people and forced disappearances, over 120,000 in Colombia alone. Successful detection of shallow buried human remains by forensic search teams is difficult in varying terrain and climates. Previous research has created controlled simulated clandestine graves of murder victims to optimize search techniques and methodologies. This paper reports on a study on controlled test site results over four simulated dismembered victims' clandestine graves as this is sadly a common scenario encountered in Latin America. Multispectral images were collected once post-burial, electrical resistivity surveys were collected 4 times, and ground penetrating radar (GPR) surveys were collected three times up to the end of the 371day survey monitoring period. After data processing, results showed that the multispectral data set could detect the simulated clandestine and control graves, with electrical resistivity imaging relative high resistances over some of the simulated graves but not over the empty control graves. GPR results showed good imaging on the Day 8surveys, medium imaging on the Day 294surveys, and medium to good imaging on the Day 371surveys. Study implications suggest that, while clandestine graves of dismembered homicide victims would likely result in smaller-sized graves when compared to graves containing intact bodies, these graves can still potentially be detected using remote sensing and geophysical methods.

A New Multispectral Data Augmentation Technique Based on Data Imputation

Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters they need to learn. The high density of information present in remote sensing imagery with high spectral resolution can make the application of DL models to large scenes challenging. Methods such as patch-based classification require large amounts of data to be processed during the training and prediction stages, which translates into long processing times and high energy consumption. One of the solutions to decrease the computational cost of these models is to perform segment-based classification. Segment-based classification schemes can significantly decrease training and prediction times, and also offer advantages over simply reducing the size of the training datasets by randomly sampling training data. The lack of a large enough number of samples can, however, pose an additional challenge, causing these models to not generalize properly. Data augmentation methods are used to generate new synthetic samples based on existing data to increase the classification performance. In this work, we propose a new data augmentation scheme using data imputation and matrix completion methods for segment-based classification. The proposal has been validated using two high-resolution multispectral datasets from the literature. The results obtained show that the proposed approach successfully increases the classification performance across all the scenes tested and that data imputation methods applied to multispectral imagery are a valid means to perform data augmentation. A comparison of classification accuracy between different imputation methods applied to the proposed scheme was also carried out.