Landslides triggered by 2019 extreme rainfall and flood events in Iran: Results from satellite remote sensing and field survey

Between mid-March and the beginning of April 2019, extremely high precipitation affected the whole Iran, leading to widespread flash flooding and landslides. Approximately 10 million people were affected, among them 2 million were in humanitarian needs. The event caused 78 fatalities, more than 1000 injuries and widespread damage in 25 out of the 31 provinces.In this work, we use both high resolution &#8211; spatial and temporal &#8211; optical and radar satellite remote sensing to characterize spatiotemporal pattern of landslide occurrence related to the main hydro-meteorological triggering events in Golestan province, North Iran. Large-area landslide detection has been performed in a semi-automated way using time series of optical Planet Scope and Sentinel-2A/B data. The obtained satellite remote sensing based results were evaluated by field surveys conducted in September 2019 in cooperation between the GFZ Potsdam and the Forest, Range and Watershed Management Organization of Iran (FRWM) being responsible for landslide hazard and risk assessment as well as the design and implementation of mitigation measures.Moreover, we report on our deformation monitoring using Sentinel-1/B based differential interferometric synthetic aperture radar (DInSAR) on hot-spots areas to investigate whether any of the catastrophic landslides that happened in spring of 2019 have shown precursory signs in form of preparatory deformation. In particular, we present our detailed investigation for Hossein Abad Kalpush landslide, located at the border between Golestan and Semnan provinces. In April 2019, this slide slipped at an unprecedented scale, causing total destruction of one part of the village nearby with complete destruction of 250 houses. Using an integrated approach exploring satellite imagery, in-situ measurements and field survey, we perform detailed time-series analysis of the evolution of Hossein Abad Kalpush landslide and examine the role of meteorological and anthropogenic influencing factors in controlling the behaviour of this landslide.

Download Full-text

Multi-scale analysis of landslide occurrence and evolution using optical and radar time series

10.5194/egusphere-egu2020-20702 ◽

2020 ◽

Author(s):

Sigrid Roessner ◽

Robert Behling ◽

Mahdi Motagh ◽

Hans Ulrich-wetzel

Keyword(s):

Remote Sensing ◽

Time Series ◽

High Resolution ◽

Intense Rainfall ◽

Large Area ◽

Landslide Occurrence ◽

Multi Scale ◽

Multi Temporal ◽

Hazard And Risk

Landslides represent a worldwide natural hazard and often occur as cascading effects related to triggering events, such as earthquakes and hydrometeorological extremes. Recent examples are the Kaikoura earthquake in New Zealand (November 2016), the Gorkha earthquake in Nepal (April/May 2015), and the Typhoon Morakot in Taiwan (August 2009) as well as less intense rainfall events persisting over unusually long periods of time as observed for Central Asia (spring 2017) and Iran (spring 2019). Each of these events has caused thousands of landslides that account substantially to the primary disaster&#8217;s impact. Moreover, their initial failure usually represents the onset of long-term progressing slope destabilization leading to multiple reactivations and thus to long-term increased hazard and risk. Therefore, regular systematic high-resolution monitoring of landslide prone regions is of key importance for characterization, understanding and modelling of spatiotemporal landslide evolution in the context of different triggering and predisposing settings. Because of the large extent of the affected areas of up to several ten thousands km2, the use of multi-temporal and multi-scale remote sensing methods is of key importance for large area process analysis. In this context, new opportunities have opened up with the increasing availability of satellite remote sensing data of suitable spatial and temporal resolution (Sentinels, Planet) as well as the advances in UAV based very high resolution monitoring and mapping.During the last decade, we have been pursuing extensive methodological developments in remote sensing based time series analysis including optical and radar observations with the goal of performing large area and at the same time detailed spatiotemporal analysis of landslide prone regions. These developments include automated post-failure landslide detection and mapping as well as assessment of the kinematics of pre- and post-failure slope evolution.&#160; Our combined optical and radar remote sensing approaches aim at an improved understanding of spatiotemporal dynamics and complexities related to evolution of landslide prone slopes at different spatial and temporal scales. &#160;In this context, we additionally integrate UAV-based observation for deriving volumetric changes also related to globally available DEM products, such as SRTM and ALOS. &#160;We present results for selected settings comprising large area co-seismic landslide occurrence related to the Kaikoura 2016 and the Nepal 2015 earthquakes. For the latter one we also analyzed annual pre- and post-seismic monsoon related landslide activity contributing to a better understanding of the interplay between these main triggering factors. Moreover, we report on ten years of large area systematic landslide monitoring in Southern Kyrgyzstan resulting in a multi-temporal regional landslide inventory of so far unprecedented spatiotemporal detail and completeness forming the basis for further analysis of the obtained landslide concentration patterns. We also present first results of our analysis of landslides triggered by intense rainfall and flood events in spring of 2019 in the North of Iran. We conclude that in all cases, the obtained results are crucial for improved landslide prediction and reduction of future landslide impact. Thus, our methodological developments represent an important contribution towards improved hazard and risk assessment as well as rapid mapping and early warning

Download Full-text

Prediction of grape yields from time-series vegetation indices using satellite remote sensing and a machine-learning approach

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2021.100485 ◽

2021 ◽

Vol 22 ◽

pp. 100485

Author(s):

Sara Tokhi Arab ◽

Ryozo Noguchi ◽

Shusuke Matsushita ◽

Tofael Ahamed

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Satellite Remote Sensing ◽

Vegetation Indices ◽

Learning Approach ◽

Machine Learning Approach

Download Full-text

An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

Remote Sensing ◽

10.3390/rs11091088 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1088 ◽

Cited By ~ 4

Author(s):

Yulong Wang ◽

Xingang Xu ◽

Linsheng Huang ◽

Guijun Yang ◽

Lingling Fan ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Winter Wheat ◽

Crop Yield ◽

Satellite Remote Sensing ◽

Wheat Yield ◽

Growing Season ◽

Remote Sensing Images ◽

Casa Model ◽

Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Download Full-text

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Biogeosciences ◽

10.5194/bg-11-4305-2014 ◽

2014 ◽

Vol 11 (16) ◽

pp. 4305-4320 ◽

Cited By ~ 135

Author(s):

S. T. Klosterman ◽

K. Hufkens ◽

J. M. Gray ◽

E. Melaas ◽

O. Sonnentag ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Curve Fitting ◽

Satellite Remote Sensing ◽

Vegetation Index ◽

Deciduous Forest ◽

Spatial Scales ◽

Visual Assessment ◽

Statistical Uncertainty ◽

Near Surface

Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensor-based time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

Download Full-text

Deformation Monitoring of Posttensioned Bridges Using High-Resolution Satellite Remote Sensing

Journal of Bridge Engineering ◽

10.1061/(asce)be.1943-5592.0001479 ◽

2019 ◽

Vol 24 (12) ◽

pp. 04019115 ◽

Cited By ~ 1

Author(s):

Edward J. Hoppe ◽

Fabrizio Novali ◽

Alessio Rucci ◽

Alfio Fumagalli ◽

Sara Del Conte ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Remote Sensing ◽

Deformation Monitoring

Download Full-text

Integrative and comprehensive Understanding on Polar Environments (iCUPE): the concept and initial results

10.5194/acp-2019-1217 ◽

2020 ◽

Cited By ~ 1

Author(s):

Tuukka Petäjä ◽

Ella-Maria Duplissy ◽

Ksenia Tabakova ◽

Julia Schmale ◽

Barbara Altstädter ◽

...

Keyword(s):

Remote Sensing ◽

Natural Resources ◽

Satellite Remote Sensing ◽

Integrated Approach ◽

The Arctic ◽

Polar Regions ◽

Comprehensive Understanding ◽

In Situ Observations ◽

Initial Results

Abstract. The role of polar regions increases in terms of megatrends such as globalization, new transport routes, demography and use of natural resources consequent effects of regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project iCUPE – integrative and Comprehensive Understanding on Polar Environments to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth Observations (EO) and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns and satellites to deliver data products, metrics and indicators to the stakeholders concerning the environmental status, availability and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and provision of novel data in atmospheric pollution, local sources and transboundary transport, characterization of arctic surfaces and their changes, assessment of concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, quantification of emissions from natural resource extraction and validation and optimization of satellite Earth Observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of integration of comprehensive in situ observations, satellite remote sensing and multiscale modeling in the Arctic context.

Download Full-text

Multi-fractal-interslipface angle curves of a morphologically simulated sand dune

Discrete Dynamics in Nature and Society ◽

10.1155/s1026022600000418 ◽

2000 ◽

Vol 5 (1) ◽

pp. 71-74 ◽

Cited By ~ 2

Author(s):

B. S. Daya Sagar

Keyword(s):

Remote Sensing ◽

Time Series ◽

Satellite Remote Sensing ◽

Sand Dune ◽

Fractal Dimensions ◽

Morphological Transformation ◽

Non Linear

A sand dune is simulated by means of a non-linear mathematical morphological transformation of which the fractal dimensions with corresponding interslipface angles are computed. This exercise has relevance to test the Validity of the model by considering various time series sand dune data that can be retrieved from the robust satellite remote sensing sensors.

Download Full-text