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Advances in Remote Sensing-based Disaster Monitoring and Assessment

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 April 2019) | Viewed by 85573

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Jungho Im

E-Mail Website
Guest Editor
Department of Civil, Urban, Earth, and Environmental Engineering, UNIST (Ulsan National Institute of Science and Technology), Ulsan, Republic of Korea
Interests: satellite remote sensing; aerosols; air quality; wild fire; urban heatwave; drought; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Haemi Park

E-Mail
Guest Editor
Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulju-gun, Ulsan 44919, Korea
Interests: remote sensing of environment; disaster monitoring; climate change effects to terrestrial ecosystems; satellite-based modeling of carbon and water cycles
Dr. Wataru Takeuchi

E-Mail Website
Guest Editor
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
Interests: atmosphere and high carbon reservoirs; agriculture; urban environment assessment; natural disaster
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extreme weather/climate events have been increasing partly due to on-going climate change. Such events become disasters where people live. Disaster monitoring and assessment are the most benefited areas by recent advances in satellite and airborne remote sensing. Consistent efforts in finding ways to operationally-monitor and assess disastrous events, such as floods, drought, heatwave, and forest fires, are consistently rewarded by integrating advanced remote sensing. Novel techniques in image analysis and the scheduled launch of a series of new sensors with enhanced specifications are also promising for disaster monitoring and assessment, which aims at reducing the risks caused by disasters.

Towards this end, a Special Issue of Remote Sensing on “Advances in Remote Sensing-based Disaster Monitoring and Assessment” has recently been announced. This Special Issue focuses on disaster monitoring and assessment caused by natural hazards, such as drought, floods, heatwave, wildfires, and landslides. You are encouraged to contribute to this Special Issue by submitting your latest research and development in the areas of, but not limited to:

  • Multi-sensor data fusion for disaster monitoring
  • Satellite-based hazard/disaster forecasting
  • Novel techniques for remote sensing-based disaster assessment
  • New approaches on vulnerability assessment for disasters
  • Remote sensing-based systems for disaster monitoring and forecasting

Dr. Jungho Im
Dr. Haemi Park
Dr. Wataru Takeuchi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Disaster monitoring
  • Risk assessment
  • Natural hazards
  • Drought
  • Heatwave
  • Floods
  • Wildfires
  • Landslides

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Published Papers (12 papers)

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Editorial

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4 pages, 153 KiB  
Editorial
Advances in Remote Sensing-Based Disaster Monitoring and Assessment
by Jungho Im, Haemi Park and Wataru Takeuchi
Remote Sens. 2019, 11(18), 2181; https://doi.org/10.3390/rs11182181 - 19 Sep 2019
Cited by 16 | Viewed by 3252
Abstract
Extreme weather/climate events have been increasing partly due to on-going climate change [...] Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)

Research

Jump to: Editorial

18 pages, 4842 KiB  
Article
Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network
by Yeonjin Lee, Daehyeon Han, Myoung-Hwan Ahn, Jungho Im and Su Jeong Lee
Remote Sens. 2019, 11(15), 1741; https://doi.org/10.3390/rs11151741 - 24 Jul 2019
Cited by 52 | Viewed by 5733
Abstract
Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather [...] Read more.
Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides information on the spatial distribution of moisture. The high-resolution TPW, together with atmospheric stability indices such as convective available potential energy (CAPE), is an effective indicator of severe weather phenomena in the pre-convective atmospheric condition. With the advent of high performing imaging instrument onboard geostationary satellites such as Advanced Himawari Imager (AHI) onboard Himawari-8 of Japan and Advanced Meteorological Imager (AMI) onboard GeoKompsat-2A of Korea, it is expected that unprecedented spatiotemporal resolution data (e.g., AMI plans to provide 2 km resolution data at every 2 min over the northeast part of East Asia) will be provided. To derive TPW from such high-resolution data in a timely fashion, an efficient algorithm is highly required. Here, machine learning approaches—random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN)—are assessed for the TPW retrieved from AHI over the clear sky in Northeast Asia area. For the training dataset, the nine infrared brightness temperatures (BT) of AHI (BT8 to 16 centered at 6.2, 6.9, 7.3, 8.6, 9.6, 10.4, 11.2, 12.4, and 13.3 μ m , respectively), six dual channel differences and observation conditions such as time, latitude, longitude, and satellite zenith angle for two years (September 2016 to August 2018) are used. The corresponding TPW is prepared by integrating the water vapor profiles from InterimEuropean Centre for Medium-Range Weather Forecasts Re-Analysis data (ERA-Interim). The algorithm performances are assessed using the ERA-Interim and radiosonde observations (RAOB) as the reference data. The results show that the DNN model performs better than RF and XGB with a correlation coefficient of 0.96, a mean bias of 0.90 mm, and a root mean square error (RMSE) of 4.65 mm when compared to the ERA-Interim. Similarly, DNN results in a correlation coefficient of 0.95, a mean bias of 1.25 mm, and an RMSE of 5.03 mm when compared to RAOB. Contributing variables to retrieve the TPW in each model and the spatial and temporal analysis of the retrieved TPW are carefully examined and discussed. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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17 pages, 2287 KiB  
Article
An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network
by Jiaxing Ye, Yuichi Kurashima, Takeshi Kobayashi, Hiroshi Tsuda, Teruyoshi Takahara and Wataru Sakurai
Remote Sens. 2019, 11(13), 1512; https://doi.org/10.3390/rs11131512 - 26 Jun 2019
Cited by 8 | Viewed by 3835
Abstract
Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are [...] Read more.
Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are being broadly acknowledged as a feasible solution to tackle the massive data generated from geo-informatics and sensing platforms to distill adequate information in the context of disaster monitoring. Aiming at detection of debris flow occurrences in a mountainous area of Sakurajima, Japan, this study demonstrates an efficient in-situ monitoring system which employs state-of-the-art machine learning techniques to exploit continuous monitoring data collected by a wireless accelerometer sensor network. Concretely, a two-stage data analysis process had been adopted, which consists of anomaly detection and debris flow event identification. The system had been validated with real data and generated favorable detection precision. Compared to other debris flow monitoring system, the proposed solution renders a batch of substantive merits, such as low-cost, high accuracy, and fewer maintenance efforts. Moreover, the presented data investigation scheme can be readily extended to deal with multi-modal data for more accurate debris monitoring, and we expect to expend addition sensory measurements shortly. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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19 pages, 2241 KiB  
Article
Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data
by Minsang Kim, Myung-Sook Park, Jungho Im, Seonyoung Park and Myong-In Lee
Remote Sens. 2019, 11(10), 1195; https://doi.org/10.3390/rs11101195 - 20 May 2019
Cited by 61 | Viewed by 7995
Abstract
This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from [...] Read more.
This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005–2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21–28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26–30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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19 pages, 7224 KiB  
Article
Time-Series Evolution Patterns of Land Subsidence in the Eastern Beijing Plain, China
by Junjie Zuo, Huili Gong, Beibei Chen, Kaisi Liu, Chaofan Zhou and Yinghai Ke
Remote Sens. 2019, 11(5), 539; https://doi.org/10.3390/rs11050539 - 5 Mar 2019
Cited by 24 | Viewed by 4028
Abstract
Land subsidence in the Eastern Beijing Plain has a long history and is always serious. In this paper, we consider the time-series evolution patterns of the eastern of Beijing Plain. First, we use the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique, with [...] Read more.
Land subsidence in the Eastern Beijing Plain has a long history and is always serious. In this paper, we consider the time-series evolution patterns of the eastern of Beijing Plain. First, we use the Persistent Scatterer Interferometric Synthetic Aperture Radar (PSI) technique, with Envisat and Radarsat-2 data, to monitor the deformation of Beijing Plain from 2007 to 2015. Second, we adopt the standard deviation ellipse (SDE) method, combined with hydrogeological data, to analyze the spatial evolution patterns of land subsidence. The results suggest that land subsidence developed mainly in the northwest–southeast direction until 2012 and then expanded in all directions. This process corresponds to the expansion of the groundwater cone of depression range after 2012, although subsidence is restricted by geological conditions. Then, we use the permutation entropy (PE) algorithm to reverse the temporal evolution pattern of land subsidence, and interpret the causes of the phenomenon in combination with groundwater level change data. The results show that the time-series evolution pattern of the land subsidence funnel edge can be divided into three stages. From 2009 to 2010, the land subsidence development was uneven. From 2010 to 2012, the land subsidence development was relatively even. From 2012 to 2013, the development of land subsidence became uneven. However, subsidence within the land subsidence funnel is divided into two stages. From 2009 to 2012, the land subsidence tended to be even, and from 2012 to 2015, the land subsidence was relatively more even. The main reason for the different time-series evolution patterns at these two locations is the annual groundwater level variations. The larger the variation range of groundwater is, the higher the corresponding PE value, which means the development of the land subsidence tends to be uneven. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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28 pages, 11311 KiB  
Article
A New Remote Sensing Dryness Index Based on the Near-Infrared and Red Spectral Space
by Jieyun Zhang, Qingling Zhang, Anming Bao and Yujuan Wang
Remote Sens. 2019, 11(4), 456; https://doi.org/10.3390/rs11040456 - 22 Feb 2019
Cited by 27 | Viewed by 7402
Abstract
Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing [...] Read more.
Soil moisture, as a crucial indicator of dryness, is an important research topic for dryness monitoring. In this study, we propose a new remote sensing dryness index for measuring soil moisture from spectral space. We first established a spectral space with remote sensing reflectance data at the near-infrared (NIR) and red (R) bands. Considering the distribution regularities of soil moisture in this space, we formulated the Ratio Dryness Monitoring Index (RDMI) as a new dryness monitoring indicator. We compared RDMI values with in situ soil moisture content data measured at 0–10 cm depth. Results showed that there was a strong negative correlation (R = −0.89) between the RDMI values and in situ soil moisture content. We further compared RDMI with existing remote sensing dryness indices, and the results demonstrated the advantages of the RDMI. We applied the RDMI to the Landsat-8 imagery to map dryness distribution around the Fukang area on the Northern slope of the Tianshan Mountains, and to the MODIS imagery to detect the spatial and temporal changes in dryness for the entire Xinjiang in 2013 and 2014. Overall, the RDMI index constructed, based on the NIR–Red spectral space, is simple to calculate, easy to understand, and can be applied to dryness monitoring at different scales. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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25 pages, 2757 KiB  
Article
Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea
by Eunna Jang, Yoojin Kang, Jungho Im, Dong-Won Lee, Jongmin Yoon and Sang-Kyun Kim
Remote Sens. 2019, 11(3), 271; https://doi.org/10.3390/rs11030271 - 30 Jan 2019
Cited by 83 | Viewed by 15043
Abstract
Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post [...] Read more.
Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50–60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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16 pages, 8288 KiB  
Article
Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China
by Meihong Ma, Changjun Liu, Gang Zhao, Hongjie Xie, Pengfei Jia, Dacheng Wang, Huixiao Wang and Yang Hong
Remote Sens. 2019, 11(2), 170; https://doi.org/10.3390/rs11020170 - 17 Jan 2019
Cited by 59 | Viewed by 7754
Abstract
Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence [...] Read more.
Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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17 pages, 4347 KiB  
Article
Flood Mapping Using Multi-Source Remotely Sensed Data and Logistic Regression in the Heterogeneous Mountainous Regions in North Korea
by Joongbin Lim and Kyoo-seock Lee
Remote Sens. 2018, 10(7), 1036; https://doi.org/10.3390/rs10071036 - 1 Jul 2018
Cited by 39 | Viewed by 6314
Abstract
Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced extensive damage to life and property almost every year due to a levee breach resulting from typhoons and heavy rainfall during the [...] Read more.
Flooding is extremely dangerous when a river overflows to inundate an urban area. From 1995 to 2016, North Korea (NK) experienced extensive damage to life and property almost every year due to a levee breach resulting from typhoons and heavy rainfall during the summer monsoon season. Recently, Hoeryeong City (2016) experienced heavy rain during Typhoon Lionrock, and the resulting flood killed and injured many people (68,900) and destroyed numerous buildings and settlements (11,600). The NK state media described it as the most significant national disaster since 1945. Thus, almost all annual repeat occurrences of floods in NK have had a severe impact, which makes it necessary to figure out the extent of floods to restore the damaged environment. However, this is difficult due to inaccessibility. Under such a situation, optical remote sensing (RS) data and radar RS data along with a logistic regression were utilized in this study to develop modeling for flood-damaged area delineation. High-resolution web-based satellite imagery was also interpreted to confirm the results of the study. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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20 pages, 6256 KiB  
Article
Multi-Scale Analysis of the Relationship between Land Subsidence and Buildings: A Case Study in an Eastern Beijing Urban Area Using the PS-InSAR Technique
by Qin Yang, Yinghai Ke, Dongyi Zhang, Beibei Chen, Huili Gong, Mingyuan Lv, Lin Zhu and Xiaojuan Li
Remote Sens. 2018, 10(7), 1006; https://doi.org/10.3390/rs10071006 - 25 Jun 2018
Cited by 64 | Viewed by 5971
Abstract
Beijing is severely affected by land subsidence, and rapid urbanisation and building construction might accelerate the land subsidence process. Based on 39 Envisat Advanced Synthetic Aperture Radar (ASAR) images acquired between 2003–2010, 55 TerraSAR-X images acquired between 2010–2016, and urban building information, we [...] Read more.
Beijing is severely affected by land subsidence, and rapid urbanisation and building construction might accelerate the land subsidence process. Based on 39 Envisat Advanced Synthetic Aperture Radar (ASAR) images acquired between 2003–2010, 55 TerraSAR-X images acquired between 2010–2016, and urban building information, we analysed the relationship between land subsidence and buildings at the regional, block, and building scales. The results show that the surface displacement rate in the Beijing urban area ranged from −109 mm/year to +13 mm/year between 2003–2010, and from −151 mm/year to +19 mm/year between 2010–2016; two subsidence bowls were mainly distributed in the eastern part of the Chaoyang District. The displacement rate agreed well with the levelling measurements, with an average bias of less than six mm/year. At the regional scale, the spatial pattern of land subsidence was mainly controlled by groundwater extraction, compressible layer thickness, and geological faults. Subsidence centres were located in the area around ground water funnels with a compressible layer depth of 50–70 m. The block-scale analysis demonstrated a clear correlation between the block construction age and the spatial unevenness of subsidence. The blocks constructed between 1998–2005 and after 2005 showed considerably more subsidence unevenness and temporal instability than the blocks constructed before 1998 during both time periods. The examination of the new blocks showed that the spatial unevenness increased with building volume variability. For the 16 blocks with a high building volume, variability, and subsidence unevenness, the building-scale analysis showed a positive relationship between building volume and settlement in most blocks, although the R2 was lower than 0.5. The results indicate that intense building construction in urban areas could cause differential settlement at the block scale in Beijing, while the settlement of single buildings could be influenced by the integrated effects of building volume, foundation structures, and the hydrogeological background. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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16 pages, 9997 KiB  
Article
Satellite-Based Evaluation of the Post-Fire Recovery Process from the Worst Forest Fire Case in South Korea
by Jae-Hyun Ryu, Kyung-Soo Han, Sungwook Hong, No-Wook Park, Yang-Won Lee and Jaeil Cho
Remote Sens. 2018, 10(6), 918; https://doi.org/10.3390/rs10060918 - 10 Jun 2018
Cited by 56 | Viewed by 8213
Abstract
The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and [...] Read more.
The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and to evaluate the recovery-time point of the post-fire processes during the past 18 years. We used three indices, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Gross Primary Production (GPP), to temporally monitor a burned area in terms of its moisture condition, vegetation biomass, and photosynthetic activity, respectively. The change of those three indices by forest recovery processes was relatively analyzed using an unburned reference area. The selected unburned area had similar characteristics to the burned area prior to the forest fire. The temporal patterns of NBR and NDVI, not only showed the forest recovery process as a result of forest management, but also statistically distinguished the recovery periods at the regions of low, moderate, and high fire severity. The NBR2.1 for all areas, calculated using 2.1 μm wavelengths, reached the unburned state in 2008. The NDVI for areas with low and moderate fire severity levels became significantly equal to the unburned state in 2009 (p > 0.05), but areas with high severity levels did not reach the unburned state until 2017. This indicated that the surface and vegetation moisture conditions recovered to the unburned state about 8 years after the fire event, while vegetation biomass and health required a longer time to recover, particularly for high severity regions. In the case of GPP, it rapidly recovered after about 3 years. Then, the steady increase in GPP surpassed the GPP of the reference area in 2015 because of the rapid growth and high photosynthetic activity of young forests. Therefore, the concluding scientific message is that, because the recovery-time point for each component of the forest ecosystem is different, using only one satellite-based indicator will not be suitable to understand the post-fire recovery process. NBR, NDVI, and GPP can be combined. Further studies will require more approaches using various terms of indices. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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17 pages, 17442 KiB  
Article
Estimating Live Fuel Moisture from MODIS Satellite Data for Wildfire Danger Assessment in Southern California USA
by Boksoon Myoung, Seung Hee Kim, Son V. Nghiem, Shenyue Jia, Kristen Whitney and Menas C. Kafatos
Remote Sens. 2018, 10(1), 87; https://doi.org/10.3390/rs10010087 - 10 Jan 2018
Cited by 31 | Viewed by 8156
Abstract
The goal of the research reported here is to assess the capability of satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer onboard both Terra and Aqua satellites, in order to replicate live fuel moisture content of Southern California chaparral ecosystems. We compared [...] Read more.
The goal of the research reported here is to assess the capability of satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer onboard both Terra and Aqua satellites, in order to replicate live fuel moisture content of Southern California chaparral ecosystems. We compared seasonal and interannual characteristics of in-situ live fuel moisture with satellite vegetation indices that were averaged over different radial extents around each live fuel moisture observation site. The highest correlations are found using the Aqua Enhanced Vegetation Index for a radius of 10 km, independently verifying the validity of in-situ live fuel moisture measurements over a large extent around each in-situ site. With this optimally averaged Enhanced Vegetation Index, we developed an empirical model function of live fuel moisture. Trends in the wet-to-dry phase of vegetation are well captured by the empirical model function on interannual time-scales, indicating a promising method to monitor fire danger levels by combining satellite, in-situ, and model results during the transition before active fire seasons. An example map of Enhanced Vegetation Index-derived live fuel moisture for the Colby Fire shows a complex spatial pattern of significant live fuel moisture reduction along an extensive wildland-urban interface, and illustrates a key advantage in using satellites across the large extent of wildland areas in Southern California. Full article
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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