scholarly journals Radar coherence and NDVI ratios as landslide early warning indicators

2020 ◽  
Author(s):  
Mylène Jacquemart ◽  
Kristy Tiampo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Detection Methods ◽  
Catastrophic Failure ◽  
Imaging Geometry ◽  
Big Sur ◽  
Landslide Stability ◽  
Novel Approach ◽  
Landslide Early Warning ◽  
The Stability

Abstract. The catastrophic failure of the Mud Creek landslide on California’s Big Sur Coast on 20 May 2017 highlighted once again how difficult it is to detect a landslide’s transition from slow moving to catastrophically unstable. Automatic detection methods that rely on InSAR displacement measurements to detect precursory acceleration are available but can be plagued by imaging geometry complexities and tedious processing algorithms. Here, we present a novel approach for assessing landslide stability by using relative interferometric coherence from Sentinel-1 and Normalized Difference Vegetation Index (NDVI) from Sentinel-2. Our method computes the ratio of mean interferometric coherence or NDVI on the unstable slope relative to that of the surrounding hillslope. We show that the coherence ratio of the Mud Creek landslide dropped by 50 % when the slide began to accelerate five months prior to its catastrophic failure in 2017. Coincidentally, the NDVI ratio began a near-linear decline. In contrast, the landslide accelerated during the rainy seasons of 2015 and 2016, but neither of those accelerations resulted in a drop of the radar coherence ratio. This suggests that radar coherence and NDVI ratios may be able to aid in both the early detection of landslides and indicate whether an acceleration critically threatens the stability of a slope.

scholarly journals Mapping Forest Fire Risk—A Case Study in Galicia (Spain)

2020 ◽  
Vol 12 (22) ◽  
pp. 3705
Author(s):  
Ana Novo ◽  
Noelia Fariñas-Álvarez ◽  
Joaquín Martínez-Sánchez ◽  
Higinio González-Jorge ◽  
José María Fernández-Alonso ◽  
...  
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fire Risk ◽  
Fire Regimes ◽  
Slope Aspect ◽  
Fire Weather Index ◽  
Novel Approach ◽  
Fuel Model ◽  
Forest Fire Risk

The optimization of forest management in roadsides is a necessary task in terms of wildfire prevention in order to mitigate their effects. Forest fire risk assessment identifies high-risk locations, while providing a decision-making support about vegetation management for firefighting. In this study, nine relevant parameters: elevation, slope, aspect, road distance, settlement distance, fuel model types, normalized difference vegetation index (NDVI), fire weather index (FWI), and historical fire regimes, were considered as indicators of the likelihood of a forest fire occurrence. The parameters were grouped in five categories: topography, vegetation, FWI, historical fire regimes, and anthropogenic issues. This paper presents a novel approach to forest fire risk mapping the classification of vegetation in fuel model types based on the analysis of light detection and ranging (LiDAR) was incorporated. The criteria weights that lead to fire risk were computed by the analytic hierarchy process (AHP) and applied to two datasets located in NW Spain. Results show that approximately 50% of the study area A and 65% of the study area B are characterized as a 3-moderate fire risk zone. The methodology presented in this study will allow road managers to determine appropriate vegetation measures with regards to fire risk. The automation of this methodology is transferable to other regions for forest prevention planning and fire mitigation.

scholarly journals An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan

2015 ◽  
Vol 8 (2) ◽  
pp. 327-335 ◽  
Author(s):  
Daniel Hölbling ◽  
Barbara Friedl ◽  
Clemens Eisank
Keyword(s):  
Change Detection ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Detection Methods ◽  
Detection And Attribution ◽  
Object Based ◽  
Wide Range ◽  
Northern Taiwan

Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.

scholarly journals COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS

2016 ◽  
Vol XLI-B7 ◽  
pp. 579-583
Author(s):  
Zhenlei Xie ◽  
Ruoming Shi ◽  
Ling Zhu ◽  
Shu Peng ◽  
Xu Chen
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Detection Method ◽  
Accuracy Assessment ◽  
Object Oriented ◽  
Detection Methods ◽  
Commission Error ◽  
Time Saving

Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.

scholarly journals COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED LAND COVER CHANGE DETECTION METHODS

2016 ◽  
Vol XLI-B7 ◽  
pp. 579-583
Author(s):  
Zhenlei Xie ◽  
Ruoming Shi ◽  
Ling Zhu ◽  
Shu Peng ◽  
Xu Chen
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Detection Method ◽  
Accuracy Assessment ◽  
Object Oriented ◽  
Detection Methods ◽  
Commission Error ◽  
Time Saving

Change detection method is an efficient way in the aim of land cover product updating on the basis of the existing products, and at the same time saving lots of cost and time. Considering the object-oriented change detection method for 30m resolution Landsat image, analysis of effect of different segmentation scales on the method of the object-oriented is firstly carried out. On the other hand, for analysing the effectiveness and availability of pixel-based change method, the two indices which complement each other are the differenced Normalized Difference Vegetation Index (dNDVI), the Change Vector (CV) were used. To demonstrate the performance of pixel-based and object-oriented, accuracy assessment of these two change detection results will be conducted by four indicators which include overall accuracy, omission error, commission error and Kappa coefficient.

scholarly journals VENµS-Derived NDVI and REIP at Different View Azimuth Angles

2022 ◽  
Vol 14 (1) ◽  
pp. 184
Author(s):  
Manuel Salvoldi ◽  
Yaniv Tubul ◽  
Arnon Karnieli ◽  
Ittai Herrmann
Keyword(s):  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Crop ◽  
Bidirectional Reflectance ◽  
Reflected Light ◽  
Change Vector ◽  
The Stability ◽  
Bidirectional Reflectance Distribution

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles.

scholarly journals COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 195-200
Author(s):  
J. S. Vinasco ◽  
D. A. Rodríguez ◽  
S. Velásquez ◽  
D. F. Quintero ◽  
L. R. Livni ◽  
...  
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Detection Methods ◽  
Landsat 8 ◽  
High Resolution Data ◽  
Water Index ◽  
Normalized Difference Water Index

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces & 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.

scholarly journals Integrated model for predicting rice yield with climate change

2018 ◽  
Vol 32 (2) ◽  
pp. 203-215 ◽  
Author(s):  
Jin-Ki Park ◽  
Amrita Das ◽  
Jong-Hwa Park
Keyword(s):  
Climate Change ◽  
Spatial Interpolation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Rice Yield ◽  
Percentage Error ◽  
Agricultural Product ◽  
South Korean ◽  
Novel Approach ◽  
Gis Methods

AbstractRice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country’s economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

scholarly journals NDVI Classification using Supervised Learning Method

2020 ◽  
Vol 9 (3) ◽  
pp. 721-728
Keyword(s):  
Machine Learning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Land Utilization ◽  
Land Quality ◽  
Novel Approach

With the blessings of Science and Technology, as the death rate is getting decreased, population is getting increased. With that, the utilization of Land is also getting increased for urbanization for which the quality of Land is degrading day by day and also the climates as well as vegetations are getting affected. To keep the Land quality at its best possible, the study on Land cover images, which are acquired from satellites based on time series, spatial and colour, are required to understand how the Land can be used further in future. Using NDVI (Normalized Difference Vegetation Index) and Machine Learning algorithms (either supervised or unsupervised), now it is possible to classify areas and predict about Land utilization in future years. Our proposed study is to enhance the acquired images with better Vegetation Index which will segment and classify the data in more efficient way and by feeding these data to the Machine Learning algorithm model, higher accuracy will be achieved. Hence, a novel approach with proper model, Machine Learning algorithm and greater accuracy is always acceptable

scholarly journals Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

2020 ◽  
Vol 12 (9) ◽  
pp. 1463 ◽  
Author(s):  
Dorijan Radočaj ◽  
Mladen Jurišić ◽  
Mateo Gašparović ◽  
Ivan Plaščak
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Multicriteria Analysis ◽  
Unsupervised Classification ◽  
Land Suitability ◽  
Coefficient Of Determination ◽  
Analytic Hierarchy ◽  
Novel Approach ◽  
Sentinel 2

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.

scholarly journals Automatic Boundary Delineation of Agricultural Fields in Multi Temporal Satellite Imagery with Segmentation

2019 ◽  
Vol 9 (2) ◽  
pp. 3527-3535
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Landscape ◽  
Performance Metrics ◽  
Satellite Image ◽  
Small Variation ◽  
Current Method ◽  
Land Use Classification ◽  
Novel Approach ◽  
Multi Temporal

A right difference in agricultural areas is the primary necessity for any sector-primarily based implementation together with estimating agricultural subsidies. Improved decision remote sensing image currently offer higher useful geographic records to delineate regions; however, their automatic managing is tedious. Its miles therefore critical to increase strategies that permit this activity to be completed right away. In any such process, a novel approach named improving the Enhanced Gustafson-Kessel-Like clustering (EGKL) version explores the use of a pc-mastering device to define agrarian areas. The current method seems for limits as either segment corners or linear traits are adjoining regions of small variation all the time series. Nearby everyday deviations from all images a while are coupled, ensuing in a sequence of extended directional edge filters. Even though, in order beautify the excellent of boundary delineation, this advised paintings is merged with sequential features of small variability across the time collection, which includes the standard deviation (STD), Near-Infra Red (NIR) band, or an index along with the Normalized Difference Vegetation Index (NDVI), or band ratios (particularly for hill us of a), or important component images. A photograph evaluation of the effects obtained with the aid of a methodology relevant to two fields of an excessive-resolution satellite image of the fractured agricultural landscape shows that it is helpful to apply the guide vector machines technique for such a task. Finally, the experimental results reveal that the proposed segmentation method is more efficient than the existing segmentation techniques in factors of each quantitative overall performance metrics and appropriateness for land-use classification.

Sign in / Sign up

Export Citation Format

Share Document