Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products

2005 ◽  
Vol 96 (2) ◽  
pp. 256-276 ◽  
Author(s):  
Willem W. Verstraeten ◽  
Frank Veroustraete ◽  
Jan Feyen
Keyword(s):  
Sensor Data ◽  
Thermal Sensor ◽  
European Forests ◽  
Processing Chain ◽  
Integrated Optical ◽  
Data Products

Breast tumor parameter estimation and interactive 3D thermal tomography using discrete thermal sensor data

2020 ◽  
Vol 7 (1) ◽  
pp. 015013
Author(s):  
Linta Antony ◽  
K Arathy ◽  
Nimmi Sudarsan ◽  
M N Muralidharan ◽  
Seema Ansari
Keyword(s):  
Parameter Estimation ◽  
Breast Tumor ◽  
Sensor Data ◽  
Thermal Sensor ◽  
Thermal Tomography ◽  
Interactive 3D

Thermal sensor for object shape and material constitution

Robotica ◽  
1988 ◽  
Vol 6 (1) ◽  
pp. 31-34 ◽  
Author(s):  
R. Andrew Russell
Keyword(s):  
Sensor Array ◽  
Robot Manipulator ◽  
Tactile Sensor ◽  
Sensor Data ◽  
Thermal Sensor ◽  
Material Constitution ◽  
Object Shape ◽  
Tactile Sensor Array

SUMMARYThis paper describes a novel tactile sensor array designed to provide information about the material constitution and shape of objects held by a robot manipulator. The sensor is modeled on the thermal touch sense which enables humans to distinguish between different materials based on how warm or cold they feel. Some results are presented and methods of analysing the sensor data are discussed.

scholarly journals BIODIVERSITY MAPPING VIA NATURA 2000 CONSERVATION STATUS AND EBV ASSESSMENT USING AIRBORNE LASER SCANNING IN ALKALI GRASSLANDS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1293-1299
Author(s):  
A. Zlinszky ◽  
B. Deák ◽  
A. Kania ◽  
A. Schroiff ◽  
N. Pfeifer
Keyword(s):  
Laser Scanning ◽  
Conservation Status ◽  
Regional Scale ◽  
Point Clouds ◽  
Natura 2000 ◽  
Global Scale ◽  
Sensor Data ◽  
Continental Scale ◽  
Data Products ◽  
Essential Biodiversity Variables

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

scholarly journals Using Open Remote Sensing Data to build an Agriculture Big Data System

2021 ◽  
Vol 12 (2) ◽  
pp. 429-436
Author(s):  
Ancy Stephen, Et. al.
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Big Data ◽  
High Performance ◽  
Remote Sensing Data ◽  
Sensor Data ◽  
Temporal Data Mining ◽  
Sensing Data ◽  
Data Mining Algorithms ◽  
Data Products

Landsat, MODIS, and Sentinel satellites are continuously producing multispectral sensor data with different spatial, temporal, and radiometric resolutions. This raw sensor data is calibrated and processed further, and additional data products are derived, which greatly reduces the burden for downstream applications from preprocessing these data. These petabyte-scale datasets are available to anyone free of charge. Remote sensing plays a key role in modern Agriculture. We can extract information about Soil, Weather, Water, and vegetation from these datasets. By processing historical remote sensing data, we can build temporal profiles of soil, weather, water, and agricultural conditions of the land. Deep learning and Spatio-temporal data mining algorithms can be applied to this data to extract hidden information. Having access to all this information via an agriculture information system, farmers will understand their land better and they will be empowered to make better decisions on a day-to-day activity. Although it looks simple from the surface, collecting, analyzing, and deriving insights from these sensor data and other data products from a multitude of sources is a big data and high-performance computing challenge. In this paper, we discuss the current open datasets and how these datasets can be used to solve various problems in agriculture. Also, we discuss implementing a cloud-based scalable agricultural information system which provides actionable insights to farmers.

scholarly journals Investigation of changes in surface urban heat-island (SUHI) in day and night using multi-temporal MODIS sensor data products (Case Study: Tehran metropolitan)

2019 ◽  
Vol 19 (52) ◽  
pp. 113-128
Author(s):  
Sirous Hashemi Darebadami ◽  
Ali Darvishi Boloorani ◽  
Seyed Kazem AlaviPanah ◽  
Mohammad Maleki ◽  
Reza Bayat ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Sensor Data ◽  
Heat Island ◽  
Data Products ◽  
Multi Temporal ◽  
Urban Heat ◽  
Surface Urban Heat Island

scholarly journals Cross-calibration of S-NPP VIIRS moderate-resolution reflective solar bands against MODIS Aqua over dark water scenes

2017 ◽  
Vol 10 (4) ◽  
pp. 1425-1444 ◽  
Author(s):  
Andrew M. Sayer ◽  
N. Christina Hsu ◽  
Corey Bettenhausen ◽  
Robert E. Holz ◽  
Jaehwa Lee ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Spectral Response ◽  
Total Error ◽  
Visible Spectral Region ◽  
Sensor Data ◽  
Absolute Calibration ◽  
Modis Aod ◽  
Data Products ◽  
Moderate Resolution ◽  
Cross Calibration

Abstract. The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRS's reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 and −7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to  ∼  0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and they are shown to decrease the bias and total error in AOD across the mid-visible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multi-sensor data continuity.

scholarly journals BIODIVERSITY MAPPING VIA NATURA 2000 CONSERVATION STATUS AND EBV ASSESSMENT USING AIRBORNE LASER SCANNING IN ALKALI GRASSLANDS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1293-1299 ◽  
Author(s):  
A. Zlinszky ◽  
B. Deák ◽  
A. Kania ◽  
A. Schroiff ◽  
N. Pfeifer
Keyword(s):  
Laser Scanning ◽  
Conservation Status ◽  
Regional Scale ◽  
Point Clouds ◽  
Natura 2000 ◽  
Global Scale ◽  
Sensor Data ◽  
Continental Scale ◽  
Data Products ◽  
Essential Biodiversity Variables

Biodiversity is an ecological concept, which essentially involves a complex sum of several indicators. One widely accepted such set of indicators is prescribed for habitat conservation status assessment within Natura 2000, a continental-scale conservation programme of the European Union. Essential Biodiversity Variables are a set of indicators designed to be relevant for biodiversity and suitable for global-scale operational monitoring. Here we revisit a study of Natura 2000 conservation status mapping via airbone LIDAR that develops individual remote sensing-derived proxies for every parameter required by the Natura 2000 manual, from the perspective of developing regional-scale Essential Biodiversity Variables. Based on leaf-on and leaf-off point clouds (10 pt/m2) collected in an alkali grassland area, a set of data products were calculated at 0.5 ×0.5 m resolution. These represent various aspects of radiometric and geometric texture. A Random Forest machine learning classifier was developed to create fuzzy vegetation maps of classes of interest based on these data products. In the next step, either classification results or LIDAR data products were selected as proxies for individual Natura 2000 conservation status variables, and fine-tuned based on field references. These proxies showed adequate performance and were summarized to deliver Natura 2000 conservation status with 80% overall accuracy compared to field references. This study draws attention to the potential of LIDAR for regional-scale Essential Biodiversity variables, and also holds implications for global-scale mapping. These are (i) the use of sensor data products together with habitat-level classification, (ii) the utility of seasonal data, including for non-seasonal variables such as grassland canopy structure, and (iii) the potential of fuzzy mapping-derived class probabilities as proxies for species presence and absence.

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