scholarly journals A New Passive Microwave Tool for Operational Forest Fires Detection: A Case Study of Siberia in 2019

2020 ◽  
Vol 12 (5) ◽  
pp. 835 ◽  
Author(s):  
Costas A. Varotsos ◽  
Vladimir F. Krapivin ◽  
Ferdenant A. Mkrtchyan
Keyword(s):  
Remote Sensing ◽  
Decision Making ◽  
Forest Fires ◽  
Forest Canopy ◽  
Hydrological Model ◽  
Microwave Remote Sensing ◽  
Sensing Platform ◽  
Decision Making System ◽  
Microwave Radiometers

The purpose of this paper is to present a new method for early detection of forest fires, especially in forest zones prone to fires using microwave remote sensing and information-modeling tools. A decision-making system is developed as a tool for operational coupled analysis of modeling results and remote sensing data. The main operating structure of this system has blocks that calculate the moisture of forest canopy, the soil-litter layer, and the forest physical temperature using the observed brightness temperature provided by the flying platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies. The hydrological parameters of the forest are assessed with both a developed regional hydrological model and remote sensing observations. The hydrological model allows for the detection of fire-prone zones that are subject to remote sensing when modeling results are corrected and thermal temperatures are evaluated. An approach for the real time forest fires classification via daytime remote sensing observations is proposed. The relative theoretical and experimental results presented here have allowed us to use a new approach to forests monitoring during periods of potential fire. A decision-making algorithm is presented that aims at analyzing data flows from radiometers located on the remote sensing platform to calculate the probability of forest fire occurring in geographical pixels. As case study, the state of forest fires that occurred in Siberia in 2019 using microwave remote sensing measurements conducted by a flying IL-18 laboratory is presented. This remote sensing platform is equipped with optical and microwave tools that allow the optical and microwave images of the observed forest areas. The main operating frequencies of microwave radiometers are 1.43, 13.3 and 37.5 GHz. Microwave radiometers provide data on water content in the forest canopy and on litter and physical temperatures. Based on the long-term measurements made in Siberia, the possible improvement of the proposed decision-making system for future relevant studies is discussed in detail. The basic idea of cost-effective monitoring of forested areas consists of a two-stage exploration of fire risk zones. The first monitoring stage is performed using the hydrological model of the study area to identify low moisture areas of the forest canopy and litter. The second stage of monitoring is conducted using the remote sensing platform only in the local fire-dangerous areas in order to more precisely identify the areas prone to fire and to detect and diagnose real burning zones. The developed algorithm allows the calculation of physical temperatures and the detection of temperature anomalies based on measured brightness temperatures. Finally, the spatial distribution of the probability of forest fire occurrence is given as an example of the decision-making system along with a comparison of this distribution with the satellite images provided by the EOSDIS Land data.

scholarly journals Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

2008 ◽  
Vol 12 (3) ◽  
pp. 751-767 ◽  
Author(s):  
T. Vischel ◽  
G. G. S. Pegram ◽  
S. Sinclair ◽  
W. Wagner ◽  
A. Bartsch
Keyword(s):  
South Africa ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Hydrological Model ◽  
Regional Scale ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Good Correspondence ◽  
Physically Based

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

scholarly journals Assessing Urban Forest Canopy Cover Using Airborne or Satellite Imagery

2008 ◽  
Vol 34 (6) ◽  
pp. 334-340
Author(s):  
Jeffrey Walton ◽  
David Nowak ◽  
Eric Greenfield
Keyword(s):  
Remote Sensing ◽  
New York ◽  
Forest Canopy ◽  
Urban Forest ◽  
Canopy Cover ◽  
Classification Methods ◽  
Remote Sensing Techniques ◽  
Many Sources ◽  
Forest Managers

With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote sensing techniques used to assess urban forest canopy cover is presented. A case study comparing canopy cover percentages for Syracuse, New York, U.S. interprets the multiple values developed using different methods. Most methods produce relatively similar results, but the estimate based on the National Land Cover Database is much lower.

scholarly journals Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4163 ◽  
Author(s):  
Chisheng Wang ◽  
Junzhuo Ke ◽  
Wenqun Xiu ◽  
Kai Ye ◽  
Qingquan Li
Keyword(s):  
Remote Sensing ◽  
Emergency Response ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Flood Damage ◽  
Sensing Data ◽  
Sensing Platform ◽  
Damage Mapping ◽  
Passenger Aircraft

Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and geocoding procedures, these images can be processed into a mosaic orthoimage for rapid ground disaster mapping. Notable, due to the relatively low flight latitude, small clouds can be easily removed by stacking multi-angle tilt images in the VPARS method. A case study on the 2019 Guangdong flood monitoring validates these advantages. The frequent aircraft revisit time, intensive flight coverage, multi-angle images and low cost of the VPARS make it a potential way to complement traditional remote sensing methods in emergency response.

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