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.

scholarly journals Detection of Spatial and Temporal Variability of Wheat Cultivars by High-Resolution Vegetation Indices

Agronomy ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 226 ◽  
Author(s):  
Stefano Marino ◽  
Arturo Alvino
Keyword(s):  
High Resolution ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Aerial Vehicle ◽  
High Resolution Images

An on-farm research study was carried out on two small-plots cultivated with two cultivars of durum wheat (Odisseo and Ariosto). The paper presents a theoretical approach for investigating frequency vegetation indices (VIs) in different areas of the experimental plot for early detection of agronomic spatial variability. Four flights were carried out with an unmanned aerial vehicle (UAV) to calculate high-resolution normalized difference vegetation index (NDVI) and optimized soil-adjusted vegetation index (OSAVI) images. Ground agronomic data (biomass, leaf area index (LAI), spikes, plant height, and yield) have been linked to the vegetation indices (VIs) at different growth stages. Regression coefficients of all samplings data were highly significant for both the cultivars and VIs at anthesis and tillering stage. At harvest, the whole plot (W) data were analyzed and compared with two sub-areas characterized by high agronomic performance (H) yield 20% higher than the whole plot, and low performances (L), about 20% lower of yield related to the whole plot). The whole plot and two sub-areas were analyzed backward in time comparing the VIs frequency curves. At anthesis, more than 75% of the surface of H sub-areas showed a VIs value higher than the L sub-plot. The differences were evident also at the tillering and seedling stages, when the 75% (third percentile) of VIs H data was over the 50% (second percentile) of the W curve and over the 25% (first percentile) of L sub-plot. The use of high-resolution images for analyzing the frequency value of VIs in different areas can be a useful approach for the detection of agronomic constraints for precision agriculture purposes.

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 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 COMPARISON OF VEGETATION INDICES FROM RPAS AND SENTINEL-2 IMAGERY FOR DETECTING PERMANENT PASTURES

2018 ◽  
Vol XLII-3 ◽  
pp. 1381-1387 ◽  
Author(s):  
M. Piragnolo ◽  
G. Lusiani ◽  
F. Pirotti
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Monitoring And Control ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Permanent pastures (PP) are defined as grasslands, which are not subjected to any tillage, but only to natural growth. They are important for local economies in the production of fodder and pastures (Ali et al. 2016). Under these definitions, a pasture is permanent when it is not under any crop-rotation, and its production is related to only irrigation, fertilization and mowing. Subsidy payments to landowners require monitoring activities to determine which sites can be considered PP. These activities are mainly done with visual field surveys by experienced personnel or lately also using remote sensing techniques. The regional agency for SPS subsidies, the Agenzia Veneta per i Pagamenti in Agricoltura (AVEPA) takes care of monitoring and control on behalf of the Veneto Region using remote sensing techniques. The investigation integrate temporal series of Sentinel-2 imagery with RPAS. Indeed, the testing area is specific region were the agricultural land is intensively cultivated for production of hay harvesting four times every year between May and October. The study goal of this study is to monitor vegetation presence and amount using the Normalized Difference Vegetation Index (NDVI), the Soil-adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Built Index (NDBI). The overall objective is to define for each index a set of thresholds to define if a pasture can be classified as PP or not and recognize the mowing.

scholarly journals Use of aerial image in the estimation of volume and biomass of Eucalyptus sp. forest stand

2020 ◽  
pp. 286-294
Author(s):  
Thallita R. S. Mendes ◽  
Eder P. Miguel ◽  
Pedro G. A. Vasconcelos ◽  
Marco B. X. Valadão ◽  
Alba V. Rezende ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Basal Area ◽  
Aerial Image ◽  
Biomass Estimation ◽  
Fit Statistics ◽  
Remote Sensing Techniques ◽  
The Individual

Assessing forest stands is crucial for managing and planning the use of these resources. Forest inventory is the instrument that provides information about the stand situation, which can be costly and time consuming. In order to facilitate and reduce the time spent obtaining these data, the main objective of this work was to evaluate the accuracy of volume and biomass estimates per unit area with data from remote sensing. Forty sample units were allocated and georeferenced, in which all trees with diameter at breast height (DBH) ≥ 5 cm were inventoried. Sequentially, the cubage was performed in order to obtain individual biomass, volume, and adjustment of the individual models. With data from georeferenced images of the study area, the vegetation indices MSAVI (Modified Soil-Adjusted Vegetation Index) and NDVI (Normalized Difference Vegetation Index) were obtained. The volume and biomass estimation using remote sensing variables were carried out through the adjustment of sigmoidal models by regression analysis, which used a combination of the average values of the vegetation indices and the basal area of the plot/hectares as an independent variable. The fit statistics and the accuracy of the tested models presented consistent results to estimate forest production. The results showwd that indices derived from remote sensing techniques associated with forest variables information could accurately estimate the volume and biomass of Eucalyptus spp. plantations.

scholarly journals Shadow detection improvement using spectral indices and morphological operators in high resolution images from urban areas

2015 ◽  
Vol XL-7/W3 ◽  
pp. 587-592 ◽  
Author(s):  
S. C. Azevedo ◽  
E. A. Silva ◽  
M. M. Pedrosa
Keyword(s):  
High Resolution ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Color Space ◽  
Tall Buildings ◽  
Shadow Detection ◽  
Detection Accuracy ◽  
Spectral Indices ◽  
High Resolution Images

While high-resolution remote sensing images have increased application possibilities for urban studies, the large number of shadow areas has created challenges to processing and extracting information from these images. Furthermore, shadows can reduce or omit information from the surface as well as degrading the visual quality of images. The pixels of shadows tend to have lower radiance response within the spectrum and are often confused with low reflectance targets. In this work, a shadow detection method was proposed using a morphological operator for dark pattern identification combined with spectral indices. The aims are to avoid misclassification in shadow identification through properties provided by them on color models and, therefore, to improve shadow detection accuracy. Experimental results were tested applying the panchromatic and multispectral band of WorldView-2 image from São Paulo city in Brazil, which is a complex urban environment composed by high objects like tall buildings causing large shadow areas. Black top-hat with area injunction was applied in PAN image and shadow identification performance has improved with index as Normalized Difference Vegetation Index (NDVI) and Normalized Saturation-Value Difference Index (NSDVI) ratio from HSV color space obtained from pansharpened multispectral WV-2 image. An increase in distinction between shadows and others objects was observed, which was tested for the completeness, correctness and quality measures computed, using a created manual shadow mask as reference. Therefore, this method can contribute to overcoming difficulties faced by other techniques that need shadow detection as a first necessary preprocessing step, like object recognition, image matching, 3D reconstruction, etc.

scholarly journals Assessment of Olive Tree Canopy Characteristics and Yield Forecast Model Using High Resolution UAV Imagery

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 385 ◽  
Author(s):  
Dimitrios Stateras ◽  
Dionissios Kalivas
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
New Technologies ◽  
Digital Terrain Model ◽  
Olive Tree ◽  
Tree Canopy ◽  
Tree Level ◽  
Surface Model

Greek agriculture is mainly based on olive tree cultivation. Farmers have always been concerned about annual olive orchard production. The necessity for the improvement of farming practices initiated the development of new technological tools that are useful in agriculture. The main goal of this study is the utilization of new technologies in order to define the geometry of olive tree configuration, while the development of a forecasting model of annual production in a non-linear olive grove, planted on a hilly uneven terrain is the secondary goal. The field’s orthomosaic, its Digital Terrain Model (DTM) and Digital Surface Model (DSM) were created by employing high resolution multispectral imagery. The Normalized Difference Vegetation Index (NDVI) thematic map has also been developed. The trees’ crowns were isolated employing the field’s orthomosaic, rendering individual polygons for each tree through Object Based Image Analysis (OBIA). The measurements were conducted in a Geographic Information System (GIS) environment and were also verified by ground ones. Tree crown height, surface, and volume were calculated, and thematic maps for each variable were created, allowing for the observation of the spatial distribution for each parameter. The compiled data were statistically analyzed revealing important correlations among different variables. These were employed to produce a model, which would enable production forecasting in kilograms per tree. The spatial distribution of the variables gave noteworthy results due to the similar pattern they followed. Future crop yield optimization, even at a tree level, can be based on the results of the present study. Its conclusions may lead to the development and implementation of precision olive tree cultivation practices.

Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions?

2021 ◽  
Vol 64 (3) ◽  
pp. 879-891
Author(s):  
Sindhuja Sankaran ◽  
Afef Marzougui ◽  
J. Preston Hurst ◽  
Chongyuan Zhang ◽  
James C. Schnable ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Yield Potential ◽  
Breeding Programs ◽  
Time Points ◽  
High Resolution Satellite Imagery

HighlightsVegetation indices (NDVI, GNDVI, and SAVI) extracted from high-resolution satellite imagery were significantly associated with vegetation indices extracted from UAV imagery.High-resolution satellite data can be used to predict maize yield at breeding plot scale.Breeding plot sizes and the variability between maize genotypes may be associated with prediction accuracies.Abstract. The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) based imagery data as a complement to ground data collection. With breeding trials across multiple geographic locations, UAV imaging is not always convenient. Hence, we anticipate that, similar to UAV imaging, phenotyping of individual test plots from high-resolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38 to 48 cm pixel-1) was compared to imagery acquired using a UAV for its ability to phenotype maize grown in two-row and six-row breeding plots. Statistics (mean, median, sum) of color (red, green, blue), near-infrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p < 0.001) was observed at different time points. The correlation of both UAV and satellite image features with yield potential was maximum (p < 0.001) at the third time point (milk/dough growth stages). For example, Pearson’s correlation coefficients between mean NDVI, GNDVI, and SAVI features with yield potential were 0.52, 0.54, and 0.51 for data derived from UAV imagery, and 0.34, 0.41, and 0.40 for data derived from satellite imagery, respectively. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso) regression, were evaluated for yield prediction using vegetation index features that were significantly correlated with observed yield. The relationship between satellite imagery with crop performance can be a function of plot size in addition to crop variability. Nevertheless, with the ongoing improvement of satellite technologies, there is a possibility for the integration of satellite data into breeding programs, thus improving phenotyping efficiencies. Keywords: Image processing, Machine learning, Plant breeding, Statistical analysis, Unmanned aerial vehicles.

scholarly journals Multitemporal analysis of vegetation indices for the superior region of the Moxotó River hydrographic basin

2018 ◽  
Vol 7 (5) ◽  
pp. 272
Author(s):  
Ulisses Alencar Bezerra ◽  
Leidjane Maria Maciel De Oliveira ◽  
Antônio Celso De Sousa Leite ◽  
Débora Natália Oliveira de Almeida ◽  
Ana Lúcia Bezerra Candeias ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Activity ◽  
Vegetation Indices ◽  
Vegetation Coverage ◽  
Radiometric Calibration ◽  
Thematic Maps ◽  
Sparse Vegetation ◽  
Remote Sensing Techniques ◽  
Semi Arid Region

The semi-arid region of Northeastern part of Brazil is under changes pressures driven by human activities or climate changes. This study aims to assess the vegetation coverage in two periods, before the transposition of the São Francisco River-East axis, and after your implementation, in the Moxotó River basin using remote sensing techniques to determine vegetation indices, and investigate the behavior of vegetation in the catchment area. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). TM-Landsat5 image of 13/01/2009, and OLI-Landsat8 image of 04/02/2017 are used here. Radiometric calibration steps, reflectance are developed to generate thematic maps of NDVI and SAVI. The NDVI showed average values for 2009 and 2017 like 0,256 and 0,264, respectively, setting a growth of vegetation cover and photosynthetic activity. The SAVI had an average of 0,147 and 0,155 to years of 2009 and 2017, respectively. Differences were found between vegetable toppings determined by NDVI and by SAVI. The exposed soil class had greater expression when observed in the thematic maps of NDVI, once the SAVI, has the precept to reduce the brightness of the ground, and this index had a higher representation in the sparse vegetation.

scholarly journals A Comparative Study for Performance of Five Landsat-based Vegetation Indices: Their Relations to Some Ecological and Terrain Variables

2020 ◽  
Vol 1 (1) ◽  
pp. 20-37
Author(s):  
Ayad Al-Quraishi ◽  
Hawar Razvanchy ◽  
Heman Gaznayee
Keyword(s):  
Aspect Ratio ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Point Of View ◽  
Vegetation Growth ◽  
Spectral Vegetation Indices ◽  
Landsat 7 ◽  
Total Study Area

Spectral vegetation indices and their relations to some ecological and terrain variables in the Iraqi Kurdistan Region (IKR) is the main objective of this study. A mosaic of two Landsat-7 ETM+ images was utilized to produce five spectral vegetation indices, and Terra ASTER Digital Elevation Model (DEM) dataset were employed. The Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Optimized Soil Adjusted Vegetation Index (OSAVI), Tasseled Cap Greenness, Land Surface Temperature (LST) were utilized for this study. The results of the current study revealed that MSAVI2 is more reliable and accurate in depicting the vegetation presence in the IKR, which is occupied 34.7% of the total study area in 2014. In terms of terrain variables, all vegetation indices responded to variation of aspect ratio variation. It was found that the densest vegetation exists between 180 to 350°. Mainly, in the South (157.5°-202.5°), Southwest (202.5°-247.5°), West (247.5°-292.5°), Northwest (292.5°-337.5°), and North (337.5°-360°). In contrast, from the aspect ratio point of view, vegetation cover growth was in its maximum status in the shaded side of the mountains, more than the sunny side. Additionally, the adequate slope for vegetation growth in the mountainous lands is 9-17%. Statistically, the LST appeared negative relations with vegetation indices and elevation

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