scholarly journals Identifying Species and Monitoring Understorey from UAS-Derived Data: A Literature Review and Future Directions

Drones ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 9 ◽  
Author(s):  
Lorna Hernandez-Santin ◽  
Mitchel L. Rudge ◽  
Renee E. Bartolo ◽  
Peter D. Erskine
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Frequency ◽  
Species Discrimination ◽  
Unmanned Aerial Systems ◽  
Understorey Vegetation ◽  
Species Specific ◽  
Understorey Plants ◽  
Aerial Systems ◽  
Derived Data

Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species.

scholarly journals Aerial Imagery Based on Commercial Flights as Remote Sensing Platform

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1658 ◽  
Author(s):  
Toni Mastelic ◽  
Josip Lorincz ◽  
Ivan Ivandic ◽  
Matea Boban
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Aerial Photography ◽  
Storage Capacity ◽  
Image Data ◽  
Aerial Imagery ◽  
Unmanned Aerial Systems ◽  
Sensing Platform ◽  
Temporal And Spatial ◽  
Aerial Systems

Remote sensing is commonly performed via airborne platforms such as satellites, specialized aircraft, and unmanned aerial systems (UASs), which perform airborne photography using mounted cameras. However, they are limited by their coverage (UASs), irregular flyover frequency (aircraft), and/or low spatial resolution (satellites) due to their high altitude. In this paper, we examine the utilization of commercial flights as an airborne platform for remote sensing. Namely, we simulate a situation where all aircraft on commercial flights are equipped with a mounted camera used for airborne photography. The simulation is used to estimate coverage, the temporal and spatial resolution of aerial imagery acquired this way, as well as the storage capacity required for storing all imagery data. The results show that Europe is 83.28 percent covered with an average of one aerial photography every half an hour and a ground sampling distance of 0.96 meters per pixel. Capturing such imagery results in 20 million images or four petabytes of image data per day. More detailed results are given in the paper for separate countries/territories in Europe, individual commercial airlines and alliances, as well as three different cameras.

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 Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

2021 ◽  
Vol 13 (22) ◽  
pp. 4671
Author(s):  
Bing Lu ◽  
Yuhong He
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Chlorophyll Content ◽  
Spatial Resolution ◽  
Ecosystem Management ◽  
Remote Sensing Images ◽  
Species Classification ◽  
Growth Status ◽  
Species Specific ◽  
Estimation Models

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

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.

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