Detection of archaeological crop marks in Cyprus using vegetation indices from Landsat TM/ETM+ satellite images and field spectroscopy measurements

2010 ◽  
Author(s):  
Athos Agapiou ◽  
Diofantos G. Hadjimitsis ◽  
K. Themistocleous ◽  
Giorgos Papadavid ◽  
Leonidas Toulios
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Landsat Tm ◽  
Field Spectroscopy

Defining the Landsat TM/ETM+ and CHRIS/PROBA spectral regions in which turbidity can be retrieved in inland waterbodies using field spectroscopy

2014 ◽  
Vol 35 (5) ◽  
pp. 1674-1692 ◽  
Author(s):  
Christiana Papoutsa ◽  
Adrianos Retalis ◽  
Leonidas Toulios ◽  
Diofantos G. Hadjimitsis
Keyword(s):  
Landsat Tm ◽  
Field Spectroscopy

Effect of Soil Depth on the Quantification of Soil Moisture Content Value Estimated from NOAA Satellite Images

2014 ◽  
Vol 567 ◽  
pp. 705-710
Author(s):  
Abdalhaleem A. Hassaballa ◽  
Abdul Nasir Matori ◽  
Helmi Z.M. Shafri
Keyword(s):  
Soil Moisture ◽  
Critical Parameter ◽  
Satellite Images ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Spatial Estimation ◽  
Optical Images

Soil moisture (MC) is considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes especially over humid areas. However, MC is very critical parameter to measure because of its variability in both space and time. The fluctuation of MC along the soil depth in turn, makes it so difficult to assess from optical satellite techniques. The study aims to produce a rectified satellite’s surface temperature (Ts) in order to enhance the spatial estimation of MC. The study also aims to produce MC estimates from three variable depths of the soil using optical images from NOAA 17 in order to examine the potential of satellite techniques in assessing the MC along the soil depths. The universal triangle (UT) algorithm was used for MC assessment based on Ts, vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths. The study area was divided into three classes according to the nature of surface cover. The resultant MC extracted from the UT method with rectified Ts, produced accuracies of MC ranging from 0.65 to 0.89 when validated with in-situ measured MC at depths 5cm and 10 cm respectively.

scholarly journals Assessment of Interventions in Fuel Management Zones Using Remote Sensing

2020 ◽  
Vol 9 (9) ◽  
pp. 533 ◽  
Author(s):  
Ricardo Afonso ◽  
André Neves ◽  
Carlos Viegas Damásio ◽  
João Moura Pires ◽  
Fernando Birra ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fuel Management ◽  
K Nearest Neighbors ◽  
Management Zones ◽  
Vector Machines ◽  
Sentinel 2

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.

scholarly journals Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1371
Author(s):  
Gaurav Kumar ◽  
Rajiv Gupta
Keyword(s):  
Information System ◽  
Spatial Data ◽  
Satellite Images ◽  
Moving Average ◽  
Vegetation Indices ◽  
Geographical Information ◽  
One Dimensional ◽  
Regression Methods ◽  
Gis Tools ◽  
Normalized Difference Vegetation Indices

This paper is an approach to forecast the spatial data in time series domain. Normally in GIS (Geographical Information System), we need raster forecasting. Moving average, exponential smoothing, and linear regression methods of forecasting are used over one-dimensional data. Present work concentrates on using these methods on satellite images applying them from pixel to pixel of historical temporal satellite data. An example set of satellite images from years 2011 to 2015 has been used to forecast the image in the year 2016. GIS tools have been developed in ArcGIS 10.1 using python to implement the methods of forecasting. Forecasted and actual images of the year 2016 have been compared by calculating the Normalized Difference Vegetation Indices (NDVI) and change detection to identify the best method.

Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data

Polar Record ◽  
2011 ◽  
Vol 48 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Bernt E. Johansen ◽  
Stein Rune Karlsen ◽  
Hans Tømmervik
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Landsat Tm ◽  
Inland Waters ◽  
Vegetation Density ◽  
Ancillary Data ◽  
Vegetation Map ◽  
Digital Format ◽  
Different Types ◽  
Classified Image

ABSTRACTThe overall objective of this paper is to present and discuss the most recently developed vegetation map for Svalbard, Arctic Norway. The map is based on satellite images in which several Landsat TM/ETM+ images were processed through six operational stages involving: (1) automatic image classification, (2) spectral similarity analysis, (3) generation of classified image mosaics, (4) ancillary data analysis, (5) contextual correction, and (6) standardisation of the final map products. The developed map is differentiated into 18 map units interpreted from 37 spectral classes. Among the 18 units separated, six of the units comprise rivers, lakes and inland waters, glaciers, as well as non- to sparsely vegetated areas. The map unit 7 is a result of shadow effects and different types of distortions in the satellite image. The vegetation of the remaining eleven units varies from dense marshes and moss tundra communities to sparsely vegetated polar deserts and moist gravel snowbeds. The accuracy of the map is evaluated in areas were access to traditional maps have been available. The vegetation density and fertility is reflected in computed NDVI values. The map product is in digital format, which gives the opportunity to produce maps in different scales. A map sheet portraying the entire archipelago is one of the main products from this study, produced at a scale of 1:500,000.

Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems

2002 ◽  
Vol 32 (8) ◽  
pp. 1301-1315 ◽  
Author(s):  
David Riaño ◽  
Emilio Chuvieco ◽  
Javier Salas ◽  
Alicia Palacios-Orueta ◽  
Aitor Bastarrika
Keyword(s):  
Satellite Images ◽  
Structural Characteristics ◽  
Remote Sensing Data ◽  
Mediterranean Ecosystems ◽  
Landsat Tm ◽  
Fuel Type ◽  
Ancillary Data ◽  
Vegetation Height ◽  
Digital Elevation ◽  
Elevation Data

This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in central Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal discrepancy or boundary displacement with vegetation maps was apparent.

scholarly journals A NEW IDL IMPLEMENTATION OF THE JUPP METHOD FOR BATHYMETRY EXTRACTION IN SHALLOW WATERS

2016 ◽  
Vol XLI-B7 ◽  
pp. 467-474
Author(s):  
M. Deidda ◽  
A. Pala ◽  
G. Sanna
Keyword(s):  
Remote Sensing ◽  
Coastal Erosion ◽  
Satellite Images ◽  
Landsat Tm ◽  
Shallow Waters ◽  
Depth Information ◽  
Ongoing Effort ◽  
Sensing Platforms ◽  
Current Implementation

There is an ongoing effort in using imagery from remote sensing platforms to obtain information about the sea depth; this allows to monitor the dynamics of coastal erosion without the need for costly and repeated local surveys. We worked on a new implementation of the Jupp method to extract depth information from satellite images. Our software is based on previous implementations of the algorithm in the IDL language, but we made our current implementation more modular in order to make possible experimentations with different approaches. We used this implementation on a series of six images (three from the Landsat TM sensor and three from the Landsat OLI sensor) in order to improve the available tools. We established an iterative workflow for working on the Landat-8 images widely exposed in this paper.

scholarly journals Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

2018 ◽  
Vol 49 (2) ◽  
pp. 127-135
Author(s):  
J. Kumhálová ◽  
P. Novák ◽  
M. Madaras
Keyword(s):  
Winter Wheat ◽  
Spatial Variability ◽  
Satellite Images ◽  
Meteorological Data ◽  
Vegetation Indices ◽  
Growing Season ◽  
Yield Component ◽  
Small Field ◽  
Agronomic Management ◽  
Time Series Images

Abstract Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth; spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO-1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11.5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO-1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to evaluate a field of 11.5 ha.

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