scholarly journals Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity

2020 ◽  
Vol 12 (1) ◽  
pp. 136 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Archaeological Site ◽  
Area Of Interest ◽  
First Case

Subsurface targets can be detected from space-borne sensors via archaeological proxies, known in the literature as cropmarks. A topic that has been limited in its investigation in the past is the identification of the optimal spatial resolution of satellite sensors, which can better support image extraction of archaeological proxies, especially in areas with spectral heterogeneity. In this study, we investigated the optimal spatial resolution (OSR) for two different cases studies. OSR refers to the pixel size in which the local variance, of a given area of interest (e.g., archaeological proxy), is minimized, without losing key details necessary for adequate interpretation of the cropmarks. The first case study comprises of a simulated spectral dataset that aims to model a shallow buried archaeological target cultivated on top with barley crops, while the second case study considers an existing site in Cyprus, namely the archaeological site of “Nea Paphos”. The overall methodology adopted in the study is composed of five steps: firstly, we defined the area of interest (Step 1), then we selected the local mean-variance value as the optimization criterion of the OSR (Step 2), while in the next step (Step 3), we spatially aggregated (upscale) the initial spectral datasets for both case studies. In our investigation, the spectral range was limited to the visible and near-infrared part of the spectrum. Based on these findings, we determined the OSR (Step 4), and finally, we verified the results (Step 5). The OSR was estimated for each spectral band, namely the blue, green, red, and near-infrared bands, while the study was expanded to also include vegetation indices, such as the Simple Ratio (SR), the Atmospheric Resistance Vegetation Index (ARVI), and the Normalized Difference Vegetation Index (NDVI). The outcomes indicated that the OSR could minimize the local spectral variance, thus minimizing the spectral noise, and, consequently, better support image processing for the extraction of archaeological proxies in areas with high spectral heterogeneity.

scholarly journals Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 346-353 ◽  
Author(s):  
Francisca López-Granados ◽  
Montse Jurado-Expósito ◽  
Jose M. Peña-Barragán ◽  
Luis García-Torres
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Research ◽  
Aerial Images ◽  
Wild Oat ◽  
Late Season ◽  
High Resolution Satellite Imagery ◽  
Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

scholarly journals Deserted Medieval Village Reconstruction Using Applied Geosciences

2020 ◽  
Vol 12 (12) ◽  
pp. 1975
Author(s):  
Alexandru Hegyi ◽  
Apostolos Sarris ◽  
Florin Curta ◽  
Cristian Floca ◽  
Sorin Forțiu ◽  
...  
Keyword(s):  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Archaeological Site ◽  
Point Of View ◽  
Surface Model ◽  
Small Satellites ◽  
High Resolution Images ◽  
Remote Sensing Techniques

This study presents a new way to reconstruct the extent of medieval archaeological sites by using approaches from the field of geoinformatics. Hence, we propose a combined use of non-invasive methodologies which are used for the first time to study a medieval village in Romania. The focus here will be on ground-based and satellite remote-sensing techniques. The method relies on computing vegetation indices (proxies), which have been utilized for archaeological site detection in order to detect the layout of a deserted medieval town located in southwestern Romania. The data were produced by a group of small satellites (3U CubeSats) dispatched by Planet Labs which delivered high-resolution images of the Earth’s surface. The globe is encompassed by more than 150 satellites (dimensions: 10 × 10 × 30 cm) which catch different images for the same area at moderately short intervals at a spatial resolution of 3–4 m. The four-band Planet Scope satellite images were employed to calculate a number of vegetation indices such as NDVI (Normalized Difference Vegetation Index), DVI (Difference Vegetation Index), SR (Simple Vegetation Ratio) and others. For better precision, structure from motion (SfM) techniques were applied to generate a high-resolution orthomosaic and a digital surface model in which the boundaries of the medieval village of “Șanțul Turcilor” in Mașloc, Romania, can be plainly observed. Additionally, this study contrasts the outcomes with a geophysical survey that was attempted inside the central part of the medieval settlement. The technical results of this study also provide strong evidence from an historical point of view: the first documented case of village systematization during the medieval period within Eastern Europe (particularly Romania) found through geoscientific methods.

Evaluation of a Low-cost Camera for Agricultural Applications

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdon Francisco Aureliano Netto ◽  
Rodrigo Nogueira Martins ◽  
Guilherme Silverio Aquino De Souza ◽  
Fernando Ferreira Lima Dos Santos ◽  
Jorge Tadeu Fim Rosas
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Paspalum Notatum ◽  
Crop Canopy ◽  
N Application ◽  
Satisfactory Accuracy ◽  
Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

scholarly journals Seasonal dynamics of lingonberry and blueberry spectra

Silva Fennica ◽  
2019 ◽  
Vol 53 (2) ◽  
Author(s):  
Petri Forsström ◽  
Jouni Peltoniemi ◽  
Miina Rautiainen
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Reference Data ◽  
Leaf Growth ◽  
Vegetation Indices ◽  
Growing Season ◽  
Moisture Stress ◽  
Stress Index ◽  
Infrared Spectral

Accurate mapping of the spatial distribution of understory species from spectral images requires ground reference data which represent the prevailing phenological stage at the time of image acquisition. We measured the spectral bidirectional reflectance factors (BRFs, 350–2500 nm) at varying view angles for lingonberry ( L.) and blueberry ( L.) throughout the growing season of 2017 using Finnish Geospatial Research Institute’s FIGIFIGO field goniometer. Additionally, we measured spectra of leaves and berries of both species, and flowers of lingonberry. Both lingonberry and blueberry showed seasonality in visible and near-infrared spectral regions which was linked to occurrences of leaf growth, flowering, berrying, and leaf senescence. The seasonality of spectra differed between species due to different phenologies (evergreen vs. deciduous). Vegetation indices, normalized difference vegetation index (NDVI), moisture stress index (MSI), plant senescence reflectance index (PSRI), and red-edge inflection point (REIP2), showed characteristic seasonal trends. NDVI and PSRI were sensitive to the presence of flowers and berries of lingonberry, while with blueberry the effects were less evident. Off-nadir observations supported differentiating the dwarf shrub species from each other but showed little improvement for detection of flowers and berries. Lingonberry and blueberry can be identified by their spectral signatures if ground reference data are available over the entire growing season. The spectral data measured in this study are reposited in the publicly open SPECCHIO Spectral Information System.Vaccinium vitis-idaeaVaccinium myrtillus

scholarly journals Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

2020 ◽  
Vol 12 (7) ◽  
pp. 1207 ◽  
Author(s):  
Jian Zhang ◽  
Chufeng Wang ◽  
Chenghai Yang ◽  
Tianjin Xie ◽  
Zhao Jiang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Monitoring ◽  
Multispectral Images ◽  
Area Index ◽  
Uav Images

The spatial resolution of in situ unmanned aerial vehicle (UAV) multispectral images has a crucial effect on crop growth monitoring and image acquisition efficiency. However, existing studies about optimal spatial resolution for crop monitoring are mainly based on resampled images. Therefore, the resampled spatial resolution in these studies might not be applicable to in situ UAV images. In order to obtain optimal spatial resolution of in situ UAV multispectral images for crop growth monitoring, a RedEdge Micasense 3 camera was installed onto a DJI M600 UAV flying at different heights of 22, 29, 44, 88, and 176m to capture images of seedling rapeseed with ground sampling distances (GSD) of 1.35, 1.69, 2.61, 5.73, and 11.61 cm, respectively. Meanwhile, the normalized difference vegetation index (NDVI) measured by a GreenSeeker (GS-NDVI) and leaf area index (LAI) were collected to evaluate the performance of nine vegetation indices (VIs) and VI*plant height (PH) at different GSDs for rapeseed growth monitoring. The results showed that the normalized difference red edge index (NDRE) had a better performance for estimating GS-NDVI (R2 = 0.812) and LAI (R2 = 0.717), compared with other VIs. Moreover, when GSD was less than 2.61 cm, the NDRE*PH derived from in situ UAV images outperformed the NDRE for LAI estimation (R2 = 0.757). At oversized GSD (≥5.73 cm), imprecise PH information and a large heterogeneity within the pixel (revealed by semi-variogram analysis) resulted in a large random error for LAI estimation by NDRE*PH. Furthermore, the image collection and processing time at 1.35 cm GSD was about three times as long as that at 2.61 cm. The result of this study suggested that NDRE*PH from UAV multispectral images with a spatial resolution around 2.61 cm could be a preferential selection for seedling rapeseed growth monitoring, while NDRE alone might have a better performance for low spatial resolution images.

Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

2021 ◽  
Author(s):  
Gustau Camps-Valls ◽  
Manuel Campos-Taberner ◽  
Alvaro Moreno-Martinez ◽  
Sophia Walther ◽  
Grégory Duveiller ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Observation Data ◽  
Terrestrial Biosphere ◽  
Time Resolved ◽  
Area Index ◽  
Nonlinear Generalization

<p>Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data.</p><p><span> Reference: <strong>"<span>A Unified Vegetation Index for Quantifying the Terrestrial Biosphere</span>"</strong>, </span><span>Gustau Camps-Valls, Manuel Campos-Taberner, Álvaro Moreno-Martı́nez, Sophia Walther, Grégory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Muñoz-Marı́, Francisco Javier Garcı́a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. </span><em><span><span>Science Advances, in press</span></span><span>, </span> <span>2021</span> </em></p>

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Measurement Method for Height-Independent Vegetation Indices Based on an Active Light Source

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1830
Author(s):  
Yongqian Ding ◽  
Yizhuo Jiang ◽  
Hongfeng Yu ◽  
Chuanlei Yang ◽  
Xueni Wu ◽  
...  
Keyword(s):  
Light Source ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Application Rate ◽  
Test Point ◽  
Central Wavelength ◽  
Nitrogen Application Rate

A coefficient CW, which was defined as the ratio of NIR (near infrared) to the red reflected spectral response of the spectrometer, with a standard whiteboard as the measuring object, was introduced to establish a method for calculating height-independent vegetation indices (VIs). Two criteria for designing the spectrometer based on an active light source were proposed to keep CW constant. A designed spectrometer, which was equipped with an active light source, adopting 730 and 810 nm as the central wavelength of detection wavebands, was used to test the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) in wheat fields with two nitrogen application rate levels (NARLs). Twenty test points were selected in each kind of field. Five measuring heights (65, 75, 85, 95, and 105 cm) were set for each test point. The mean and standard deviation of the coefficient of variation (CV) for NDVI in each test point were 3.85% and 1.39% respectively, the corresponding results for RVI were 2.93% and 1.09%. ANOVA showed the measured VIs possessed a significant ability to discriminate the NARLs and had no obvious correlation with the measurement heights. The experimental results verified the feasibility and validity of the method for measuring height-independent VIs.

scholarly journals Unpiloted Aerial System (UAS)-Supported Biogeomorphic Analysis of Restored Sierra Nevada Montane Meadows

2020 ◽  
Vol 12 (11) ◽  
pp. 1828
Author(s):  
Jerry Davis ◽  
Leonhard Blesius ◽  
Michelle Slocombe ◽  
Suzanne Maher ◽  
Michael Vasey ◽  
...  
Keyword(s):  
Sierra Nevada ◽  
Cross Sections ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Survey Methods ◽  
Vegetation Indices ◽  
Field Studies ◽  
Red Edge ◽  
Montane Meadows

The benefits of meadow restoration can be assessed by understanding the connections among geomorphology, hydrology, and vegetation; and multispectral imagery captured from unpiloted aerial systems (UASs) can provide the best method in terms of cost, resolution, and support for vegetation indices. Our field studies were conducted on northern Sierra montane meadows (with ≤70 km2 watershed area). The meadows exist in various stages of ecological restoration. Field survey methods included GPS + laser-leveling channel survey, cross-sections, LiDAR, vegetation sampling, soil measurements, and UAS imaging. A sensor captured calibrated blue (465–485 nm), green (550–570 nm), red (663–673 nm), near infrared (NIR) (820–860 nm), and red-edge (712–722 nm) bands at 5.5 cm resolution (as well as thermal at 81 cm resolution) and provided multispectral images and derivative vegetation indices such as the normalized difference vegetation index (NDVI) and red-edge chlorophyll index (Clre). This fine-scale imagery extended our morphometric assessment of post-restoration channel bedform patterns and sinuosity related to Carex-influenced soil properties and Salix influence, and also documented groundwater-related effects via Carex patterns evident from spring snowmelt images, as well as NDVI and Clre (derived from spring and summer images) in growing to senescent phenological stages. Carex was significantly associated with low bulk density and high soil moisture, NDVI, and Clre in low-lying areas, and channel sinuosity was significantly associated with willow influence. Our methods can be applied by restoration managers to assess where projects are threatened by renewed incision and to document levels of carbon sequestration significant to addressing climate change.

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

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