Study of Temporal Behaviour of Homogeneity Maps for Estimating Representative Area of a Ground Sample Using Remote Sensing

2021 ◽  
pp. 93-99
Author(s):  
Prasad J. Deshpande ◽  
Anudeep Sure ◽  
Onkar Dikshit ◽  
Shivam Tripathi
Keyword(s):  
Remote Sensing ◽  
Ground Sample ◽  
Temporal Behaviour ◽  
Representative Area

scholarly journals A FRAMEWORK FOR ESTIMATING REPRESENTATIVE AREA OF A GROUND SAMPLE USING REMOTE SENSING

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 687-692
Author(s):  
P. J. Deshpande ◽  
A. Sure ◽  
O. Dikshit ◽  
S. Tripathi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Programming Language ◽  
Remote Sensing Data ◽  
Google Earth ◽  
The Other ◽  
Ground Sample ◽  
Sampling Locations ◽  
Google Earth Engine ◽  
Representative Area

<p><strong>Abstract.</strong> Modelling hydro-meteorological variables over land and atmosphere comprise of ground sampling at selected locations and predicting over the other locations. Remote sensing data can be effectively used to improve predictions by prudently choosing sampling locations of variables co-dependent on the prediction variable. This paper presents a framework for estimating the representative area of a ground sample and thereby determining the number of samples required for prediction with a given level of uncertainty and spatial resolution. Application of the proposed framework for soil moisture as the prediction variable is presented using Google Earth Engine and Scikit-learn libraries implemented in Python 3 programming language.</p>

scholarly journals Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Inherent Reflectance Variability of Vegetation

Agriculture ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 246 ◽  
Author(s):  
Baabak Mamaghani ◽  
M. Grady Saunders ◽  
Carl Salvaggio
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Time Of Day ◽  
Unmanned Aircraft ◽  
P Value ◽  
Ground Sample ◽  
Significant Difference

With the inception of small unmanned aircraft systems (sUAS), remotely sensed images have been captured much closer to the ground, which has meant better resolution and smaller ground sample distances (GSDs). This has provided the precision agriculture community with the ability to analyze individual plants, and in certain cases, individual leaves on those plants. This has also allowed for a dramatic increase in data acquisition for agricultural analysis. Because satellite and manned aircraft remote sensing data collections had larger GSDs, self-shadowing was not seen as an issue for agricultural remote sensing. However, sUAS are able to image these shadows which can cause issues in data analysis. This paper investigates the inherent reflectance variability of vegetation by analyzing six Coneflower plants, as a surrogate for other cash crops, across different variables. These plants were measured under different forecasts (cloudy and sunny), at different times (08:00 a.m., 09:00 a.m., 10:00 a.m., 11:00 a.m. and 12:00 p.m.), and at different GSDs (2, 4 and 8 cm) using a field portable spectroradiometer (ASD Field Spec). In addition, a leafclip spectrometer was utilized to measure individual leaves on each plant in a controlled lab environment. These spectra were analyzed to determine if there was any significant difference in the health of the various plants measured. Finally, a MicaSense RedEdge-3 multispectral camera was utilized to capture images of the plants every hour to analyze the variability produced by a sensor designed for agricultural remote sensing. The RedEdge-3 was held stationary at 1.5 m above the plants while collecting all images, which produced a GSD of 0.1 cm/pixel. To produce 2, 4, and 8 cm GSD, the MicaSense RedEdge-3 would need to be at an altitude of 30.5 m, 61 m and 122 m respectively. This study did not take background effects into consideration for either the ASD or MicaSense. Results showed that GSD produced a statistically significant difference (p < 0.001) in Normalized Difference Vegetation Index (NDVI, a commonly used metric to determine vegetation health), R 2 values demonstrated a low correlation between time of day and NDVI, and a one-way ANOVA test showed no statistically significant difference in the NDVI computed from the leafclip probe (p-value of 0.018). Ultimately, it was determined that the best condition for measuring vegetation reflectance was on cloudy days near noon. Sunny days produced self-shadowing on the plants which increased the variability of the measured reflectance values (higher standard deviations in all five RedEdge-3 channels), and the shadowing of the plants decreased as time approached noon. This high reflectance variability in the coneflower plants made it difficult to accurately measure the NDVI.

scholarly journals Vicarious Methodologies to Assess and Improve the Quality of the Optical Remote Sensing Images: A Critical Review

2020 ◽  
Vol 12 (24) ◽  
pp. 4029
Author(s):  
Sakib Kabir ◽  
Larry Leigh ◽  
Dennis Helder
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Quality Parameters ◽  
Optical Remote Sensing ◽  
Coherent Noise ◽  
Ground Sample ◽  
Characterization Methods ◽  
Remote Sensing Satellite ◽  
Improved Technology

Over the past decade, number of optical Earth-observing satellites performing remote sensing has increased substantially, dramatically increasing the capability to monitor the Earth. The quantity of remote sensing satellite increase is primarily driven by improved technology, miniaturization of components, reduced manufacturing, and launch cost. These satellites often lack on-board calibrators that a large satellite utilizes to ensure high quality (radiometric, geometric, spatial quality, etc.) scientific measurement. To address this issue, this work presents “best” vicarious image quality assessment and improvement techniques for those kinds of optical satellites which lack an on-board calibration system. In this article, image quality categories have been explored, and essential quality parameters (absolute and relative calibration, aliasing, etc.) have been identified. For each of the parameters, appropriate characterization methods are identified along with their specifications or requirements. In cases of multiple methods, recommendations have been made based-on the strengths and weaknesses of each method. Furthermore, processing steps have been presented, including examples. Essentially, this paper provides a comprehensive study of the criteria that need to be assessed to evaluate remote sensing satellite data quality, and the best vicarious methodologies to evaluate identified quality parameters such as coherent noise and ground sample distance.

scholarly journals Mapping Utility Poles in Aerial Orthoimages Using ATSS Deep Learning Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6070
Author(s):  
Matheus Gomes ◽  
Jonathan Silva ◽  
Diogo Gonçalves ◽  
Pedro Zamboni ◽  
Jader Perez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Urban Areas ◽  
Aerial Images ◽  
Detection Methods ◽  
Ground Sample ◽  
Sensing Applications ◽  
Bounding Box ◽  
Utility Poles ◽  
Remote Sensing Applications

Mapping utility poles using side-view images acquired with car-mounted cameras is a time-consuming task, mainly in larger areas due to the need for street-by-street surveying. Aerial images cover larger areas and can be feasible alternatives although the detection and mapping of the utility poles in urban environments using top-view images is challenging. Thus, we propose the use of Adaptive Training Sample Selection (ATSS) for detecting utility poles in urban areas since it is a novel method and has not yet investigated in remote sensing applications. Here, we compared ATSS with Faster Region-based Convolutional Neural Networks (Faster R-CNN) and Focal Loss for Dense Object Detection (RetinaNet ), currently used in remote sensing applications, to assess the performance of the proposed methodology. We used 99,473 patches of 256 × 256 pixels with ground sample distance (GSD) of 10 cm. The patches were divided into training, validation and test datasets in approximate proportions of 60%, 20% and 20%, respectively. As the utility pole labels are point coordinates and the object detection methods require a bounding box, we assessed the influence of the bounding box size on the ATSS method by varying the dimensions from 30×30 to 70×70 pixels. For the proposal task, our findings show that ATSS is, on average, 5% more accurate than Faster R-CNN and RetinaNet. For a bounding box size of 40×40, we achieved Average Precision with intersection over union of 50% (AP50) of 0.913 for ATSS, 0.875 for Faster R-CNN and 0.874 for RetinaNet. Regarding the influence of the bounding box size on ATSS, our results indicate that the AP50 is about 6.5% higher for 60×60 compared to 30×30. For AP75, this margin reaches 23.1% in favor of the 60×60 bounding box size. In terms of computational costs, all the methods tested remain at the same level, with an average processing time around of 0.048 s per patch. Our findings show that ATSS outperforms other methodologies and is suitable for developing operation tools that can automatically detect and map utility poles.

scholarly journals Analysis of Persian Oak (Quercus Brantii Lindl)’s Spectral- Temporal Behaviour Under the Stresses of Water Deficiency and Dust Particles

2019 ◽  
Vol 99 ◽  
pp. 04011
Author(s):  
Ali Darvishi Boloorani ◽  
Soghra Ranjbareslamloo ◽  
Saham Mirzaie ◽  
Hossein Ali Bahrami ◽  
Fardin Mirzapour ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Dust Storms ◽  
Dust Particles ◽  
Tree Seedlings ◽  
Mountain Range ◽  
Water Deficiency ◽  
Remote Sensing Images ◽  
Temporal Behaviour ◽  
Quercus Brantii

Persian oak (Quercus Brantii Lindl) is the most abundant tree species in Zagros mountain range that is affected by western dust storms and harsh droughts in recent years. The lack of spectral-temporal information about these trees has caused limitations on the usage of remote sensing images to make a synoptic estimation of damages caused by dust storms and drought. The objective of this research is to analyse the spectral-temporal behaviour of Persian oak under the stress of water deficiency and dust storms. The aim is to improve the competitive abilities of experts on modelling the stress of water deficiency and dust storms on trees using remote sensing images. For the purpose of this experimental investigation, a greenhouse laboratory has been built. Analysis of the experiments was carried out using 54 two-year-old oak tree seedlings; Fieldspec-3-ADD; and wind tunnel at the greenhouse of the faculty of agriculture of the Tarbiat Modares University in 2016. Results show that the water stress could be modelled much better using geometrical indices extracted from continuum removed spectrum. Area and the depth were best indices. Water stress has been modelled better that dust stress.

scholarly journals Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1246
Author(s):  
Kyle B. Larson ◽  
Aaron R. Tuor
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Biological Invasions ◽  
Spatial Information ◽  
Wildlife Habitat ◽  
Ground Sample ◽  
Sensing Applications ◽  
Combining Data ◽  
Remote Sensing Applications

Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

Low Inclination Circular Orbits for Remote Sensing Satellites

2014 ◽  
Vol 629 ◽  
pp. 298-303 ◽  
Author(s):  
Karim Kamalaldin ◽  
Mohamed Okasha
Keyword(s):  
Remote Sensing ◽  
Elevation Angle ◽  
Target Area ◽  
Circular Orbits ◽  
Satellite Imaging ◽  
Ground Sample ◽  
Orbital Parameters ◽  
Sensing Applications ◽  
Synchronous Orbit ◽  
Area Of Concern

<p class="TTPAbstract">This paper investigates the design methodology of low Earth inclined circular orbits for remote sensing satellites to satisfy certain mission requirements and constrains such as coverage, resolution, illumination conditions and attitude maneuvers capabilities. Although sun synchronous orbit (SSO) proved to be a good candidate for most remote sensing applications due to its constant lighting conditions and global coverage, low inclined orbits are distinguished by long ground track especially if the target area of concern has horizontal rectangular border. With that respect, low inclined orbits may lead to improvement of the satellite imaging accessibility to the target area per day or reduction of satellite revisit time compared to SSO. This paper exploits these characteristics for low inclined orbits. The target area considered in simulation is between 220 and 320 N latitude lines with 2.5 m ground sample resolution. The satellite maximum rolling capability is 350 and the sun elevation angle is constrained between 300 and 700 for purpose of maximizing the quality of the obtained images. Matlab and STK software are employed to define the initial orbital parameters to meet these conditions for a low inclined orbit.</p>

Seed Rain Potential in Late-season Weed Escapes can be Estimated Using Remote Sensing

Weed Science ◽  
2021 ◽  
pp. 1-21
Author(s):  
Matthew Kutugata ◽  
Chengsong Hu ◽  
Bishwa Sapkota ◽  
Muthukumar Bagavathiannan
Keyword(s):  
Remote Sensing ◽  
Seed Production ◽  
Vegetation Index ◽  
Seed Rain ◽  
Management Decision ◽  
Sample Collection ◽  
Weed Species ◽  
Ground Sample ◽  
Late Season ◽  
Canopy Volume

Abstract The presence of a soil seedbank facilitates the persistence of annual weed species in arable fields. Soil weed seedbank is replenished by many sources, but the largest one is the seeds produced by uncontrolled weed escapes present during late-season. The estimation of weed seed production potential from late-season escapes may allow farmers to make appropriate management decisions to minimize seedbank replenishment. The objective of this research was to evaluate the feasibility of using UAV-based RGB and multispectral imagery for estimating seed rain potential in late-season weed escapes in crop fields. Three case studies were used to capture images of weed escapes prior to crop harvest: waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in soybean [Glycine max (L.) Merr.], Palmer amaranth [Amaranthus palmeri (S.) Watson] in cotton (Gossypium hirsutum L.), and johnsongrass [Sorghum halepense (L.) Pers.] in soybean. Randomly selected quadrats with different density gradients of weed escapes were sampled at the time of crop maturity. High-resolution RGB and multispectral images of the experimental area were collected using drones immediately prior to ground sample collection. Normalized difference vegetation index (NDVI), excess green index (ExG), and canopy volume estimates derived from canopy height models were used to obtain weed biological measurements (biomass and seed production). Among the indices investigated, NDVI and ExG had very strong correlations (0.71 – 0.97) with weed biomass. No specific remote sensing variable was ideal across the three cases examined here, suggesting that a generalized remote sensing approach may not offer robust estimations, and case-specific applications are imperative. Nonetheless, drone imagery is a powerful tool for estimating seed production from uncontrolled weed escapes and assist with management decision making.

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