scholarly journals The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods

2003 ◽  
Vol 3 (1/2) ◽  
pp. 115-128 ◽  
Author(s):  
C. Domenikiotis ◽  
A. Loukas ◽  
N. R. Dalezios
Keyword(s):  
Remote Sensing ◽  
Natural Disasters ◽  
Forest Fires ◽  
Damage Assessment ◽  
Large Scale ◽  
Vegetation Index ◽  
Anthropogenic Factors ◽  
Central Greece ◽  
Sensing Applications ◽  
Noaa Avhrr

Abstract. The increasing number of extreme natural phenomena, which are related to the climate variability and are mainly caused by anthropogenic factors, escalate the frequency and severity of natural disasters. Operational monitoring of natural hazards and assessment of the affected area impose quick and efficient methods based on large-scale data, readily available to the agencies. The growing number of satellite systems and their capabilities give rise to remote sensing applications to all types of natural disasters, including forest fires and floods. Remote sensing techniques can be used in all three aspects of disaster management viz: forecasting, monitoring and damage assessment. The purpose of this paper is to highlight the importance of satellite remote sensing for monitoring and near-real time assessment of the affected by forest fires and floods areas. As a tool, two satellite indices are presented, namely the Normalized Difference Vegetation Index (NDVI) and the Surface Temperature (ST), extracted by the meteorological satellite NOAA/AVHRR. In the first part of the paper, a review of utilized techniques using NDVI and ST is given. In the second part, the application of various methodologies to three case studies are presented: the forest fire of 21–24 July 1995 in Penteli Mountain near Athens and 16 September 1994 in Pelion Mountain in Thessaly region, central Greece, and finally the flood of 17–23 October 1994 in Thessaly region, central Greece. For all studies the NDVI has been utilized for hazard assessment. The method of ST has been applied to the flood event in Thessaly, for the estimation of the areal extent of the floods. As emerged from the studies, remote sensing data can be decisive for monitoring and damage assessment, caused by forest fires and floods.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Ecosystem Drought Response Timescales from Thermal Emission versus Shortwave Remote Sensing

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Erika Andujar ◽  
Nir Y. Krakauer ◽  
Chuixiang Yi ◽  
Felix Kogan
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Plant Stress ◽  
Meteorological Drought ◽  
Condition Index ◽  
Vegetation Health ◽  
Vegetation Health Index

Remote sensing is used for monitoring the impacts of meteorological drought on ecosystems, but few large-scale comparisons of the response timescale to drought of different vegetation remote sensing products are available. We correlated vegetation health products derived from polar-orbiting radiometer observations with a meteorological drought indicator available at different aggregation timescales, the Standardized Precipitation Evapotranspiration Index (SPEI), to evaluate responses averaged globally and over latitude and biome. The remote sensing products are Vegetation Condition Index (VCI), which uses normalized difference vegetation index (NDVI) to identify plant stress, Temperature Condition Index (TCI), based on thermal emission as a measure of surface temperature, and Vegetation Health Index (VHI), the average of VCI and TCI. Globally, TCI correlated best with 2-month timescale SPEI, VCI correlated best with longer timescale droughts (peak mean correlation at 13 months), and VHI correlated best at an intermediate timescale of 4 months. Our results suggest that thermal emission (TCI) may better detect incipient drought than vegetation color (VCI). VHI had the highest correlations with SPEI at aggregation times greater than 3 months and hence may be the most suitable product for monitoring the effects of long droughts.

scholarly journals Facing and managing natural disasters in the Sporades islands, Greece

2014 ◽  
Vol 14 (4) ◽  
pp. 995-1005 ◽  
Author(s):  
P. Karanikola ◽  
T. Panagopoulos ◽  
S. Tampakis ◽  
M. I. Karantoni ◽  
G. Tsantopoulos
Keyword(s):  
Natural Disasters ◽  
Natural Disaster ◽  
Forest Fires ◽  
Local Population ◽  
Civil Defense ◽  
Natural Phenomena ◽  
Public Response ◽  
Central Greece ◽  
Good Management ◽  
Perceptions And Attitudes

Abstract. The region of the Sporades islands located in central Greece is at the mercy of many natural phenomena, such as earthquakes due to the marine volcano Psathoura and the rift of Anatolia, forest fires, floods, landslides, storms, hail, snowfall and frost. The present work aims at studying the perceptions and attitudes of the residents regarding how they face and manage natural disasters. A positive public response during a hazard crisis depends not only upon the availability and good management of a civil defense plan but also on the knowledge and perception of the possible hazards by the local population. It is important for the stakeholders to know what the citizens expect so that the necessary structures can be developed in the phase of preparation and organization. The residents were asked their opinion about what they think should be done by the stakeholders after a catastrophic natural disaster, particularly about the immediate response of stakeholders and their involvement and responsibilities at different, subsequent intervals of time following the disaster. The residents were also asked about the most common disasters that happen in their region and about the preparation activities of the stakeholders.

scholarly journals Introducing a New Remote Sensing-Based Model for Forecasting Forest Fire Danger Conditions at a Four-Day Scale

2019 ◽  
Vol 11 (18) ◽  
pp. 2101 ◽  
Author(s):  
M. Ahmed ◽  
Quazi Hassan ◽  
Masoud Abdollahi ◽  
Anil Gupta
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Fires ◽  
Land Surface ◽  
Vegetation Index ◽  
Significant Risk ◽  
Fire Danger ◽  
Area Of Interest ◽  
Forest Fire Danger ◽  
Very High

Forest fires are natural disasters that create a significant risk to the communities living in the vicinity of forested landscape. To minimize the risk of forest fires for the resilience of such urban communities and forested ecosystems, we proposed a new remote sensing-based medium-term (i.e., four-day) forest fire danger forecasting system (FFDFS) based on an existing framework, and applied the system over the forested regions in the northern Alberta, Canada. Hence, we first employed moderate resolution imaging spectroradiometer (MODIS)-derived daily land surface temperature (Ts) and surface reflectance products along with the annual land cover to generate three four-day composite for Ts, normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) at 500 m spatial resolution for the next four days over the forest-dominant regions. Upon generating these four-day composites, we calculated the variable-specific mean values to determine variable-specific fire danger maps with two danger classes (i.e., high and low). Then, by assuming the cloud-contaminated pixels as the low fire danger areas, we combined these three danger maps to generate a four-day fire danger map with four danger classes (i.e., low, moderate, high, and very high) over our study area of interest, which was further enhanced by incorporation of a human-caused static fire danger map. Finally, the four-day scale fire danger maps were evaluated using observed/ground-based forest fire occurrences during the 2015–2017 fire seasons. The results revealed that our proposed system was able to detect about 75% of the fire events in the top two danger classes (i.e., high and very high). The system was also able to predict the 2016 Horse River wildfire, the worst fire event in Albertian and Canadian history, with about 67% agreement. The higher accuracy outputs from our proposed model indicated that it could be implemented in the operational management, which would be very useful for lessening the adverse impact of such fire events.

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

scholarly journals The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

2021 ◽  
Vol 886 (1) ◽  
pp. 012100
Author(s):  
Munajat Nursaputra ◽  
Siti Halimah Larekeng ◽  
Nasri ◽  
Andi Siady Hamzah
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Forest Degradation ◽  
Google Earth ◽  
Aerial Images ◽  
Forest Monitoring ◽  
Forest Density ◽  
Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

scholarly journals A REVIEW ON SPATIAL QUALITY ASSESSMENT METHODS FOR EVALUATION OF PAN-SHARPENED SATELLITE IMAGERY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 255-261
Author(s):  
F. Dadras Javan ◽  
F. Samadzadegan ◽  
S. Mehravar ◽  
A. Toosi
Keyword(s):  
Remote Sensing ◽  
Quality Assessment ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Large Scale ◽  
Spatial Distortion ◽  
Sensing Applications ◽  
Fused Image ◽  
Spatial Quality

Abstract. Nowadays, high-resolution fused satellite imagery is widely used in multiple remote sensing applications. Although the spectral quality of pan-sharpened images plays an important role in many applications, spatial quality becomes more important in numerous cases. The high spatial quality of the fused image is essential for extraction, identification and reconstruction of significant image objects, and will result in producing high-quality large scale maps especially in the urban areas. This paper introduces the most sensitive and effective methods in detecting the spatial distortion of fused images by implementing a number of spatial quality assessment indices that are utilized in the field of remote sensing and image processing. In this regard, in order to recognize the ability of quality assessment indices for detecting the spatial distortion quantity of fused images, input images of the fusion process are affected by some intentional spatial distortions based on non-registration error. The capabilities of the investigated metrics are evaluated on four different fused images derived from Ikonos and WorldView-2 initial images. Achieved results obviously explicate that two methods namely Edge Variance Distortion and the spatial component of QNR metric called Ds are more sensitive and responsive to the imported errors.

scholarly journals Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

2019 ◽  
Vol 11 (2) ◽  
pp. 417 ◽  
Author(s):  
Qingqing Ma ◽  
Linrong Chai ◽  
Fujiang Hou ◽  
Shenghua Chang ◽  
Yushou Ma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Field Experiments ◽  
Grazing Intensity ◽  
Remote Sensing Data ◽  
Gansu Province ◽  
Livestock Grazing ◽  
Scientific Management ◽  
Sensing Data

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity (p < 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

Experience in assessing the dynamic state of vegetation based on a large-scale map of modern vegetation (on the example of the area “Levashovskiy les”, St. Petersburg)

2019 ◽  
pp. 39-56 ◽  
Author(s):  
E. A. Volkova ◽  
V. N. Khramtsov
Keyword(s):  
Plant Communities ◽  
Forest Fires ◽  
Vegetation Cover ◽  
Large Scale ◽  
Anthropogenic Impacts ◽  
Dynamic State ◽  
Anthropogenic Factors ◽  
Natural State ◽  
Short Term

The article is devoted to the vegetation mapping of the “Levashovskiy les”— a large forest-mire massif located in the northern part of St. Petersburg (Fig. 1). It continues a series of articles on the vegetation of existing and proposed specially protected natural areas of St. Petersburg (Volkova, Khramtsov, 2018). Large-scale map of modern vegetation (Fig. 2) is presented; the map legend includes 67 main numbers, the signs and numeric indexes at the numbers made it possible to show 93 mapping units (associations and their variants). Brief description of the main types of plant communities (spruce, pine, birch, aspen, gray alder and black alder forests; raised bogs, transitional mires and fens, floodplain and upland meadows) reveals the content of the legend. Vegetation cover is characterized by the dominance of secondary communities. The main anthropogenic impacts on modern vegetation are following: drainage reclamation, deforestation and former agricultural use, forest fires, gas pipelines, highways. Most of the forest communities are secondary ones; they have grown under the pressure of various anthropogenic factors and at different time. Nowadays an active process of natural regeneration of conife­rous (mainly spruce) trees goes in the forests. Plant community structure and species composition were taken into account as well as their dynamic state. To assess the degree of disturbance of plant communities and the potential for their restoration, the analysis of all mapped vegetation categories with respect to their position in the ranks of restorative successions was made. Then an assessment map “Dynamic state of plant communities” (Fig. 3) was compiled. The map shows following categories of dynamic types of communities: conventionally primary; relatively long-term secondary and stable long-term secondary (Sukachev, 1938; Isachenko, 1964; Karpenko, 1965; Gribova, Isachenko, 1972); short-term secondary that were divided into 3 categories representing different stages of restorative series. Present state of the vegetation cover of the “Levashovskiy les” can be determined by the ratio of the areas of conventionally primary and secondary communities. Areal analysis of dynamic categories of plant communities showed that only a bit more than 20 % of the territory is occupied by conventionally primary communities and about 60 % – by short-term secondary ones with good restorative potential. Without strong anthropogenic and natural disturbances, a significant part of the disturbed plant communities will be able to self-restore to their natural state. The establishment of a specially protected natural area as well as the regulation of conservation regime will support restoration process of nature ecosystems.

Sign in / Sign up

Export Citation Format

Share Document