scholarly journals UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018 ◽  
Vol 10 (8) ◽  
pp. 1216 ◽  
Author(s):  
Jonathan Dash ◽  
Grant Pearse ◽  
Michael Watt
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Physiological Stress ◽  
Abiotic Factors ◽  
Vegetation Indices ◽  
Detection Threshold ◽  
Forest Trees ◽  
Multispectral Imagery

The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

scholarly journals Damage assessment of chilli thrips using high resolution multispectral satellite data

2021 ◽  
Vol 21 (4) ◽  
pp. 480-487
Author(s):  
Mathyam Prabhakar ◽  
Merugu Thirupathi, ◽  
G. Srasvan Kumar ◽  
U. Sai Sravan ◽  
M. Kalpana ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Indices ◽  
Superior Performance ◽  
Water Index ◽  
Sensing Technology ◽  
Significant Difference ◽  
Normalized Difference Water Index ◽  
Sentinel 2A

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

The Use of DEM and Satellite Data for Regional Scale Soil Databases

2002 ◽  
Vol 51 (1-2) ◽  
pp. 263-272 ◽  
Author(s):  
Endre Dobos ◽  
B. Norman ◽  
B. Worstell ◽  
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Data Sources ◽  
Quality Data ◽  
Small Scale ◽  
The World ◽  
Soil Landscape ◽  
Avhrr Data ◽  
Small Scale Soil

New, quantitative methods and data sources for characterizing small scale soil resources have been demonstrated. AVHRR and coarse spatial resolution DEM were designed for mapping large areas of the world quickly and cost effectively. The method combines digital elevation data, “ground truth” information, including the soil taxonomic class for measured soil locations, and a time series of satellite images to form a digital soil database. The results show that using ancillary information such as AVHRR data and DEM derivatives from the national to continental level surveys is among the most promising tools for geographers and soil surveyors. The AVHRR data is often used for land cover studies but its usefulness in soil studies has not yet been proven. This study is a representative example of the usefulness of AVHRR data in characterizing the soil-forming environment and delineating soil patterns, particularly when integrated with other data for describing the soil landscape, such as the DEM, slope, curvature and PDD. The predictive power of AVHRR and similar low spatial resolution satellite data sources could be further improved with the development of soil sensitive filters. Mention should be made of the potential improvement of the products derived from these data sources with the use of better quality data provided by satellites that have been launched recently. Neither the AVHRR nor the DEM-derivatives show high correlation with the soil classes, but both represent a great portion of the environmental variability. In general, the more uncorrelated information is extracted from DEM and AVHRR, the better explanation of the spatial soil variability is achieved with an integrated use of them. The images of AVHRR time series show a relatively low correlation, thus each of the new dates adds much potential information on the soils. The studies also highlighted the great help of surface vegetation in soil remote sensing, as indicated by the high R² value of Band 1 and NDVI. The importance of the short-term weather history of the study area was also demonstrated.  Terrain information and terrain variables were primarily developed for large scale local studies. Small scale mapping of large regions presents different issues, like over-generalization and over-smoothing of the soil information. The terrain features with smaller extents are dissolved into a larger neighborhood. As a smoother terrain map is created, a lot of detail is lost and less variability is observable. Many of the terrain attributes are useless with this approach. Elevation, slope, relief intensity, potential drainage density and the curvature variables are the most informative digital variables for characterizing the soil-landscape in small scale inventories.  The resulting soil databases will have all the advantages of quantitatively derived databases, including consistency, homogeneity, and reduced data generalization and edge-matching problems. Although the results from the above procedures are believed to be accurate enough to serve as a basis for global and regional studies, they should be checked and further revised by local and regional experts to ensure quality. Research should continue on improving the procedures, augmenting the pedon data with new field sampling, and incorporating new image and DEM data sources. One of the most important results of these studies is the demonstration of the usefulness of these data sources for small scale soil mapping and the overall validity and representatitivity of the AVHRR-terrain/soil correlation within the temperate region of the world. Further studies will need to be performed to test the use of AVHRR and terrain data for other climate zones of the World, where potential problems, like continuous cloud cover, may occur.

Weather forecasting by interactive analysis of radar and satellite imagery

1988 ◽  
Vol 324 (1579) ◽  
pp. 299-315 ◽  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Weather Forecasting ◽  
Display System ◽  
Time Data ◽  
Interactive Analysis ◽  
Short Period ◽  
Weather Situation ◽  
Real Time Data

At the Meteorological Office, Bracknell, quantitative rainfall maps from a network of ground-based radars, augmented by cloud images from Meteosat , are used to produce analyses and very-short-period forecasts of precipitation. These remotely sensed images provide the only way of presenting the current weather situation quickly enough and with the required spatial resolution and areal coverage. The processing of the radar and satellite data is highly automated, but there are some tasks that require judgements based upon many strands of information and an understanding of meteorological processes. To this end, forecasters use a specially developed display system to interact with the imagery. The facilities for interacting with the pictures have been optimized so that the forecaster, who is kept very busy in active weather situations, can keep pace with the flow of real-time data. Even so, as more radars are added to the network, ways must be found of reducing the burden of the forecaster’s interpretive and judgemental functions by automating some of them and making others easier to perform.

Quantifying mortality of tropical rain forest trees using high-spatial-resolution satellite data

2004 ◽  
Vol 7 (1) ◽  
pp. 52-59 ◽  
Author(s):  
David B. Clark ◽  
Carlomagno Soto Castro ◽  
Luis Diego Alfaro Alvarado ◽  
Jane M. Read
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Rain Forest ◽  
Tropical Rain Forest ◽  
High Spatial Resolution ◽  
Forest Trees

scholarly journals Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

2020 ◽  
Vol 12 (23) ◽  
pp. 3952
Author(s):  
Lei Yang ◽  
Jinling Song ◽  
Lijuan Han ◽  
Xin Wang ◽  
Jing Wang
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Vital Role ◽  
Dynamic Monitoring ◽  
Fusion Model ◽  
Spatiotemporal Resolution ◽  
Reconstruction Methods

High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

scholarly journals Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

2018 ◽  
Vol 15 (9) ◽  
pp. 2723-2742 ◽  
Author(s):  
Yasmina Loozen ◽  
Karin T. Rebel ◽  
Derek Karssenberg ◽  
Martin J. Wassen ◽  
Jordi Sardans ◽  
...  
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Mediterranean Forests ◽  
N Content ◽  
N Concentration ◽  
Chlorophyll Index ◽  
Canopy Nitrogen ◽  
The Relationship

Abstract. Canopy nitrogen (N) concentration and content are linked to several vegetation processes. Therefore, canopy N concentration is a state variable in global vegetation models with coupled carbon (C) and N cycles. While there are ample C data available to constrain the models, widespread N data are lacking. Remotely sensed vegetation indices have been used to detect canopy N concentration and canopy N content at the local scale in grasslands and forests. Vegetation indices could be a valuable tool to detect canopy N concentration and canopy N content at larger scale. In this paper, we conducted a regional case-study analysis to investigate the relationship between the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) time series from European Space Agency (ESA) Envisat satellite at 1 km spatial resolution and both canopy N concentration (%N) and canopy N content (N g m−2, of ground area) from a Mediterranean forest inventory in the region of Catalonia, in the northeast of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20, 15, 10 and 5 km) and at the original spatial resolution of 1 km. The results at higher spatial resolution (1 km) yielded significant log–linear relationships between MTCI and both canopy N concentration and content: r2 = 0.32 and r2 = 0.17, respectively. We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N concentration was strongest for deciduous broadleaf and mixed plots (r2 = 0.24 and r2 = 0.44, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r2 = 0.19). At the species level, canopy N concentration was strongly related to MTCI for European beech plots (r2 = 0.69). These results present a new perspective on the application of MTCI time series for canopy N detection.

scholarly journals Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

2019 ◽  
Vol 11 (21) ◽  
pp. 2538 ◽  
Author(s):  
Joanna Suliga ◽  
Joy Bhattacharjee ◽  
Jarosław Chormański ◽  
Ann van Griensven ◽  
Boud Verbeiren
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Original Data ◽  
Data Exploration ◽  
Plant Canopy ◽  
Raster Data ◽  
Northeastern Poland

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

scholarly journals ANALYSIS OF THE INFLUENCE OF CLIMATE FACTORS ON DIFFERENT CROPS USING MULTIVARIABLE ANALYSIS AND REMOTELY SENSED DATA

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Bojan Popović ◽  
Igor Ruskovski ◽  
Stevan Milovanov ◽  
Miro Govedarica ◽  
Dušan Jovanović
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Vegetation Indices ◽  
Multivariable Analysis ◽  
Air Pressure ◽  
Plant Health ◽  
Climate Factors ◽  
Correlation Model ◽  
Remotely Sensed Data ◽  
Crop Types

Phenology modeling of the most common agricultural cultures based on time series of high spatialresolution satellite imagery and vegetation indices was conducted for several crop types. This datawas used as an indicator of crops state in the multivariable correlation model as dependent variables.The influence of climate factors (temperature, air pressure, precipitation, insolation, cloudiness andhumidity) on crops state was determined using multivariable correlation. This model allowsprediction of delayed influence of climate factors on plant health and state through the values ofNDVI.

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