scholarly journals sUAS Remote Sensing of Vineyard Evapotranspiration Quantifies Spatiotemporal Uncertainty in Satellite-Borne ET Estimates

2020 ◽  
Vol 12 (19) ◽  
pp. 3251
Author(s):  
Michael Kalua ◽  
Anna M. Rallings ◽  
Lorenzo Booth ◽  
Josué Medellín-Azuara ◽  
Stefano Carpin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Irrigation Management ◽  
Estimation Methods ◽  
Observation System ◽  
Unmanned Aerial Systems ◽  
Sensing Platforms ◽  
Earth Observation System ◽  
Commercial Vineyard ◽  
Using Data ◽  
Aerial Systems

Small Unmanned Aerial Systems (sUAS) show promise in being able to collect high resolution spatiotemporal data over small extents. Use of such remote sensing platforms also show promise for quantifying uncertainty in more ubiquitous Earth Observation System (EOS) data, such as evapotranspiration and consumptive use of water in agricultural systems. This study compares measurements of evapotranspiration (ET) from a commercial vineyard in California using data collected from sUAS and EOS sources for 10 events over a growing season using multiple ET estimation methods. Results indicate that sUAS ET estimates that include non-canopy pixels are generally lower on average than EOS methods by >0.5 mm day−1. sUAS ET estimates that mask out non-canopy pixels are generally higher than EOS methods by <0.5 mm day−1. Masked sUAS ET estimates are less variable than unmasked sUAS and EOS ET estimates. This study indicates that limited deployment of sUAS can provide important estimates of uncertainty in EOS ET estimations for larger areas and to also improve irrigation management at a local scale.

scholarly journals Error simulations of uncorrected NDVI and DCVI during remote sensing measurements from UAS

2014 ◽  
Vol 18 (2) ◽  
pp. 35-45 ◽  
Author(s):  
Michał T. Chiliński ◽  
Marek Ostrowski
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Vegetation Index ◽  
Vegetation Mapping ◽  
Radiative Transfer Model ◽  
Unmanned Aerial Systems ◽  
Transfer Model ◽  
Digital Cameras ◽  
Aerial Systems

Abstract Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

scholarly journals A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing

2020 ◽  
Vol 32 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Sha Huang ◽  
Lina Tang ◽  
Joseph P. Hupy ◽  
Yang Wang ◽  
Guofan Shao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unmanned Aerial Systems ◽  
Atmospheric Effect ◽  
Saturation Phenomenon ◽  
Near Ir ◽  
Critical Problems ◽  
Low Costs ◽  
Aerial Systems

AbstractThe Normalized Difference Vegetation Index (NDVI), one of the earliest remote sensing analytical products used to simplify the complexities of multi-spectral imagery, is now the most popular index used for vegetation assessment. This popularity and widespread use relate to how an NDVI can be calculated with any multispectral sensor with a visible and a near-IR band. Increasingly low costs and weights of multispectral sensors mean they can be mounted on satellite, aerial, and increasingly—Unmanned Aerial Systems (UAS). While studies have found that the NDVI is effective for expressing vegetation status and quantified vegetation attributes, its widespread use and popularity, especially in UAS applications, carry inherent risks of misuse with end users who received little to no remote sensing education. This article summarizes the progress of NDVI acquisition, highlights the areas of NDVI application, and addresses the critical problems and considerations in using NDVI. Detailed discussion mainly covers three aspects: atmospheric effect, saturation phenomenon, and sensor factors. The use of NDVI can be highly effective as long as its limitations and capabilities are understood. This consideration is particularly important to the UAS user community.

scholarly journals Long‐duration fully autonomous operation of rotorcraft unmanned aerial systems for remote‐sensing data acquisition

2019 ◽  
Vol 37 (1) ◽  
pp. 137-157 ◽  
Author(s):  
Danylo Malyuta ◽  
Christian Brommer ◽  
Daniel Hentzen ◽  
Thomas Stastny ◽  
Roland Siegwart ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Acquisition ◽  
Remote Sensing Data ◽  
Unmanned Aerial Systems ◽  
Sensing Data ◽  
Autonomous Operation ◽  
Long Duration ◽  
Aerial Systems

Making the invisible visible: using UAS-based high-resolution color-infrared imagery to identify buried medieval monastery walls

2015 ◽  
Vol 3 (2) ◽  
pp. 58-67 ◽  
Author(s):  
Jan Rudolf Karl Lehmann ◽  
Keturah Zoe Smithson ◽  
Torsten Prinz
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
High Resolution ◽  
Vegetation Index ◽  
Archaeological Site ◽  
Unmanned Aerial Systems ◽  
Surrounding Water ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Techniques ◽  
Aerial Systems

Remote sensing techniques have become an increasingly important tool for surveying archaeological sites. However, budgeting issues in archaeological research often limit the application of satellite or airborne imagery. Unmanned aerial systems (UAS) provide a flexible, quick, and more economical alternative to commonly used remote sensing techniques. In this study, the buried features of the archaeological site of the Kleinburlo monastery, near Münster, Germany, were identified using high-resolution color–infrared (CIR) images collected from a UAS platform. Based on these CIR images, a modified normalised difference vegetation index (NDVIblue) was calculated, showing reflectance spectra of vegetation anomalies caused by water stress. In the presented study, the vegetation growing on top of the buried walls was better nourished than the surrounding plants because very wet conditions over the days previous to data collection caused higher levels of water stress in the surrounding water-drenched land. This difference in water stress was a good indicator for detecting archaeological remains.

scholarly journals Visualizing the Spatiotemporal Trends of Thermal Characteristics in a Peatland Plantation Forest in Indonesia: Pilot Test Using Unmanned Aerial Systems (UASs)

2018 ◽  
Vol 10 (9) ◽  
pp. 1345 ◽  
Author(s):  
Kotaro Iizuka ◽  
Kazuo Watanabe ◽  
Tsuyoshi Kato ◽  
Niken Putri ◽  
Sisva Silsigia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Characteristics ◽  
Unmanned Aerial Systems ◽  
Surface Model ◽  
Plantation Forest ◽  
Vegetation Height ◽  
Spatiotemporal Trends ◽  
New Findings ◽  
Thermal Data ◽  
Aerial Systems

The high demand for unmanned aerial systems (UASs) reflects the notable impact that these systems have had on the remote sensing field in recent years. Such systems can be used to discover new findings and develop strategic plans in related scientific fields. In this work, a case study is performed to describe a novel approach that uses a UAS with two different sensors and assesses the possibility of monitoring peatland in a small area of a plantation forest in West Kalimantan, Indonesia. First, a multicopter drone with an onboard camera was used to collect aerial images of the study area. The structure from motion (SfM) method was implemented to generate a mosaic image. A digital surface model (DSM) and digital terrain model (DTM) were used to compute a canopy height model (CHM) and explore the vegetation height. Second, a multicopter drone combined with a thermal infrared camera (Zenmuse-XT) was utilized to collect both spatial and temporal thermal data from the study area. The temperature is an important factor that controls the oxidation of tropical peats by microorganisms, root respiration, the soil water content, and so forth. In turn, these processes can alter the greenhouse gas (GHG) flux in the area. Using principal component analysis (PCA), the thermal data were processed to visualize the thermal characteristics of the study site, and the PCA successfully extracted different feature areas. The trends in the thermal information clearly show the differences among land cover types, and the heating and cooling of the peat varies throughout the study area. This study shows the potential for using UAS thermal remote sensing to interpret the characteristics of thermal trends in peatland environments, and the proposed method can be used to guide strategical approaches for monitoring the peatlands in Indonesia.

The Application of Airborne Remote Sensing Technology in Land and Resources

2014 ◽  
Vol 644-650 ◽  
pp. 4360-4363
Author(s):  
Li Na Dong ◽  
Jing Tong ◽  
Chen Yang Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Observation System ◽  
Airborne Remote Sensing ◽  
Geological Investigation ◽  
Good Efficiency ◽  
Sensing Technology ◽  
Earth Observation System ◽  
Low Altitude

Airborne and space remote sensing system are all the important parts of the earth observation system, also being good supplements to each other. Airborne remote sensing has the advantages of being high resolution, good efficiency and flexibility, which makes itself an effective method to rapidly acquire high resolution remote sensing data. Particularly, the technologies of conducting low altitude remote sensing investigation by unmanned aerial vehicles are rapidly developed with a great progress achieved, so there is no doubt that it will plays an important role in the remote sensing geological investigation.

Soil moisture estimation from high-resolution UASs imagery based on machine learning approaches for land cover classification

2021 ◽  
Author(s):  
Teresa Pizzolla ◽  
Silvano Fortunato Dal Sasso ◽  
Ruodan Zhuang ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Thermal Inertia ◽  
Bare Soil ◽  
Unmanned Aerial Systems ◽  
Learning Approaches ◽  
Land Use Classification ◽  
Aerial Systems

&lt;p&gt;Soil moisture (SM) is an essential variable in the earth system as it influences water, energy and, carbon fluxes between the land surface and the atmosphere. The SM spatio-temporal variability requires detailed analyses, high-definition optics and fast computing approaches for near real-time SM estimation at different spatial scales. Remote Sensing-based Unmanned Aerial Systems (UASs) represents the actual solution providing low-cost approaches to meet the requirements of spatial, spectral and temporal resolutions [1; 3; 4]. In this context, a proper land use classification is crucial in order to discriminate the behaviors of vegetation and bare soil in such high-resolution imagery. Therefore, high-resolution UASs-based imagery requires a specific images classification approach also considering the illumination conditions. In this work, the land use classification was carried out using a methodology based on a combined machine learning approaches: k-means clustering algorithm for removing shadow pixels from UASs images and, binary classifier for vegetation filtering. This approach led to identifying the bare soil on which SM estimation was computed using the Apparent Thermal Inertia (ATI) method [2]. The estimated SM values were compared with field measurements obtaining a good correlation (R&lt;sup&gt;2&lt;/sup&gt; = 0.80). The accuracy of the results shows good reliability of the procedure and allows extending the use of UASs also in unclassified areas and ungauged basins, where the monitoring of the SM is very complex.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;[1] Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring, Remote Sensing, 2018, 10, 641.&lt;/p&gt;&lt;p&gt;[2] Minacapilli, M., Cammalleri, C., Ciraolo, G., D&amp;#8217;Asaro, F., Iovino, M., and Maltese, A. Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment. Soil. Sci. Soc. Amer. J. 2012, vol.76, n.1, pp. 92&amp;#8211;100&lt;/p&gt;&lt;p&gt;[3] Paruta, A., P. Nasta, G. Ciraolo, F. Capodici, S. Manfreda, N. Romano, E. Bendor, Y. Zeng, A. Maltese, S. F. Dal Sasso and R. Zhuang, A geostatistical approach to map near-surface soil moisture through hyper-spatial resolution thermal inertia, IEEE Transactions on Geoscience and Remote Sensing, 2020.&lt;/p&gt;&lt;p&gt;[4] Petropoulos, G.P., A. Maltese, T. N. Carlson, G. Provenzano, A. Pavlides, G. Ciraolo, D. Hristopulos, F. Capodici, C. Chalkias, G. Dardanelli, S. Manfreda, Exploring the use of UAVs with the simplified &amp;#8220;triangle&amp;#8221; technique for Soil Water Content and Evaporative Fraction retrievals in a Mediterranean setting, International Journal of Remote Sensing, 2020.&lt;/p&gt;

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