Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroon highlands (Central-Africa)

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Download Full-text

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

10.21203/rs.3.rs-19292/v3 ◽

2021 ◽

Author(s):

Alfred Homère Ngandam Mfondoum ◽

Pauline Wokwenmendam Nguet ◽

Jean Valery Mefire Mfondoum ◽

Mesmin Tchindjang ◽

Sofia Hakdaoui ◽

...

Keyword(s):

Real Time ◽

Cox Model ◽

Central Africa ◽

Daily Rainfall ◽

Landsat 8 ◽

Best Value ◽

Landslide Event ◽

Recent Landslide ◽

Dichotomous Variables ◽

Occurrence Date

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model.Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96 % . Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99% , between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Download Full-text

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

10.21203/rs.3.rs-19292/v2 ◽

2020 ◽

Author(s):

ALFRED HOMERE NGANDAM MFONDOUM ◽

Pauline Wokwenmendam Nguet ◽

Jean Valery Mefire Mfondoum ◽

Mesmin Tchindjang ◽

Sofia Hakdaoui ◽

...

Keyword(s):

Real Time ◽

Cox Model ◽

Central Africa ◽

Daily Rainfall ◽

Landsat 8 ◽

Best Value ◽

Landslide Event ◽

Recent Landslide ◽

Dichotomous Variables ◽

Occurrence Date

Abstract Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and the intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlating the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – The outcome of the SLIP adapted model is the Landslide Hazard Zonation (LHZ) map, with an overall accuracy of 96%. Further, the outcome of the DRIP adapted model states that the probability of rainfall-triggered landslides is 99.99%, for 6/9 ranges of probability between June and October. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Download Full-text

Adapting sudden landslide identification product (SLIP) and detecting real-time increased precipitation (DRIP) algorithms to map rainfall-triggered landslides in Western Cameroon highlands (Central-Africa)

Geoenvironmental Disasters ◽

10.1186/s40677-021-00189-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Alfred Homère Ngandam Mfondoum ◽

Pauline Wokwenmendam Nguet ◽

Jean Valery Mefire Mfondoum ◽

Mesmin Tchindjang ◽

Sofia Hakdaoui ◽

...

Keyword(s):

Real Time ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cox Model ◽

Daily Rainfall ◽

Landsat 8 ◽

Landslide Occurrence ◽

Starting Point ◽

Landslide Event ◽

Recent Landslide

Abstract Background NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This double method uses Landsat 8 satellite images and daily rainfall data for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods The SLIP algorithm is adapted, by integrating the inverse Normalized Difference Vegetation Index (NDVI) to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948–2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate landslide occurrence to rainfall, with the first known event in Cameroon as starting point, and using the Cox model. Results From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96%. Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99%, between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime analysis for a more flexibility in observation and prediction thresholding.

Download Full-text

Adapting the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms to map and predict rainfall-triggered landslides in the West-Cameroons’ highlands (Central-Africa)

10.21203/rs.3.rs-19292/v1 ◽

2020 ◽

Author(s):

ALFRED HOMERE NGANDAM MFONDOUM ◽

Pauline Wokwenmendam Nguet ◽

Jean Valery Mefire Mfondoum ◽

Mesmin Tchindjang ◽

Sofia Hakdaoui ◽

...

Keyword(s):

Prediction Model ◽

Control Point ◽

Central Africa ◽

Landsat 8 ◽

Landslide Event ◽

Soil Exposure ◽

Before And After ◽

Dichotomous Variables ◽

Precipitation Records ◽

Probability Density Function Pdf

Abstract Background – The SLIP and DRIP algorithms recently developed correlate Landsat 8 images and local daily precipitation records to map and time rainfall-triggered landslides. In many areas recently affected by that geohazard in west-Cameroon’s highlands, only the dry season images are available, while rainfall data are recorded on a monthly scale. Methods – The SLIP algorithm is modified, integrating the inverse NDVI to assess the soil exposure, the Modified Normalized Multi-band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slopes in degrees. They are converted into binned layers and overlaid to map the recent landslide. The DRIP algorithm is also modified, using the monthly rainfall rescaled to a daily window and the days of rainfall per month. Their probability density function (PDF) curves are superimposed and their intersection are used to propose set dichotomous variables before and after the 28 October 2019 landslide event, for a prediction model. Results – The Landslide Hazard Zonation (LHZ) map of latest landslides is effective at 100% , while the overall accuracy is 77.8% when integrating the control point around the disaster area. Moreover, for 1948-2018 individual thresholds of , and 2019 threshold of between June and October, the risk of rainfall-triggered landslide is 95% , while the 'no-landslide' probabilities are between 98.95% and 99.99% . Conclusions – Based on the SLIP and DRIP algorithms, the proposed methodology offers a new alternative in case of voids and gaps between data. Improvements and comparisons with others models are in perspective. Keywords – SLIP, DRIP, Landsat 8, geohazard, West-Cameroons’ Highlands, rainfall-triggered Landslides, LHZ, prediction model.

Download Full-text

Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan

Remote Sensing ◽

10.3390/rs13020202 ◽

2021 ◽

Vol 13 (2) ◽

pp. 202

Author(s):

Wan-Ru Huang ◽

Pin-Yi Liu ◽

Jie Hsu ◽

Xiuzhen Li ◽

Liping Deng

Keyword(s):

Real Time ◽

Complex Terrain ◽

Daily Rainfall ◽

Rainfall Estimation ◽

Rainfall Events ◽

Satellite Precipitation ◽

Different Seasons ◽

Daily Scale ◽

Rainfall Variations ◽

Southwestern Taiwan

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.).

Download Full-text

AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-771-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 771-777 ◽

Cited By ~ 3

Author(s):

Guoqing Zhou ◽

Xiang Zhou ◽

Tao Yue ◽

Yilong Liu

Keyword(s):

Real Time ◽

Detection Algorithm ◽

High Accuracy ◽

Svm Classifier ◽

Detection Accuracy ◽

Landsat 8 ◽

Cloud Detection ◽

Threshold Method ◽

Dsp Implementation ◽

Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

Download Full-text

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

10.5194/egusphere-egu21-14674 ◽

2021 ◽

Author(s):

Christos Kontopoulos ◽

Nikos Grammalidis ◽

Dimitra Kitsiou ◽

Vasiliki Charalampopoulou ◽

Anastasios Tzepkenlis ◽

...

Keyword(s):

Decision Support ◽

Surface Water ◽

Decision Support System ◽

Real Time ◽

Support System ◽

Satellite Data ◽

Hazard Mitigation ◽

Google Earth ◽

Landsat 8

Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project &#8220;EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn &#8211; industrial &#8211; critical infrastructure monitorinG usIng Combined technologies&#8221; is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide &#8220;explainable recommendations&#8221; that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.

Download Full-text

Real time black carbon measurements in West and Central Africa urban sites

Atmospheric Environment ◽

10.1016/j.atmosenv.2012.02.005 ◽

2012 ◽

Vol 54 ◽

pp. 529-537 ◽

Cited By ~ 30

Author(s):

El Hadji Thierno Doumbia ◽

Catherine Liousse ◽

Corinne Galy-Lacaux ◽

Seydi Ababacar Ndiaye ◽

Babacar Diop ◽

...

Keyword(s):

Real Time ◽

Black Carbon ◽

Central Africa ◽

West And Central Africa ◽

Urban Sites

Download Full-text

Regional rainfall thresholds for landslide occurrence using a centenary database

10.5194/nhess-2017-362 ◽

2017 ◽

Author(s):

Teresa Vaz ◽

José Luís Zêzere ◽

Susana Pereira ◽

Sérgio C. Oliveira ◽

Ricardo A. C. Garcia ◽

...

Keyword(s):

Daily Rainfall ◽

Early Warning Systems ◽

Rain Gauge ◽

Warning Systems ◽

Rainfall Thresholds ◽

Period Analysis ◽

Landslide Occurrence ◽

Landslide Event ◽

Landslide Early Warning ◽

Regional Rainfall

Abstract. This work proposes a comprehensive methodology to assess rainfall thresholds for landslide initiation, using a centenary landslide database associated with a single centenary daily rainfall dataset. The methodology is applied to the Lisbon region and include the rainfall return period analysis that was used to identify the critical rainfall combination (quantity-duration) related to each landslide event. The spatial representativeness of the reference rain gauge is evaluated and the rainfall thresholds is assessed and validated using the receiver operating characteristic (ROC) metrics. Results show that landslide events located up to 10 km from the rain gauge can be used to calculate the rainfall thresholds in the study area; however, such thresholds may be used with acceptable confidence up to 50 km distance from the rain gauge. The obtained rainfall thresholds using linear and potential regression have a good performance in ROC metrics. However, the intermediate thresholds based on the probability of landslide events, established in the zone between the lower limit threshold and the upper limit threshold are much more informative as they indicate the probability of landslide event occurrence given rainfall exceeding the threshold. This information can be easily included in landslide early warning systems, especially when combined with the probability of rainfall above each threshold.

Download Full-text

LZER0: GNSS cost-effective real-time positioning applied to landslide monitoring

10.5194/egusphere-egu2020-9506 ◽

2020 ◽

Author(s):

Lavinia Tunini ◽

David Zuliani ◽

Paolo Fabris ◽

Marco Severin

Keyword(s):

Real Time ◽

Monitoring System ◽

Low Cost ◽

Cost Effective ◽

Single Frequency ◽

Landslide Monitoring ◽

Natural Risk ◽

Near Surface ◽

Landslide Event ◽

Wide Range

The Global Navigation Satellite Systems (GNSS) provide a globally extended dataset of primordial importance for a wide range of applications, such as crustal deformation, topographic measurements, or near surface processes studies. However, the high costs of GNSS receivers and the supporting software can represent a strong limitation for the applicability to landslide monitoring. Low-cost tools and techniques are strongly required to face the plausible risk of losing the equipment during a landslide event.Centro di Ricerche Sismologiche (CRS) of Istituto Nazionale di Oceanografia e di Geofisica Sperimentale OGS in collaboration with SoluTOP, in the last years, has developed a cost-effective GNSS device, called LZER0, both for post-processing and real-time applications. The aim is to satisfy the needs of both scientific and professional communities which require low-cost equipment to increase and improve the measurements on structures at risk, such as landslides or buildings, without losing precision.The landslide monitoring system implements single-frequency GNSS devices and open source software packages for GNSS positioning, dialoguing through Linux shell scripts. Furthermore a front-end web page has been developed to show real-time tracks. The system allows measuring real-time surface displacements with a centimetre precision and with a cost ten times minor than a standard RTK GPS operational system.This monitoring system has been tested and now applied to two landslides in NE- Italy: one near Tolmezzo municipality and one near Brugnera village. Part of the device development has been included inside the project CLARA 'CLoud plAtform and smart underground imaging for natural Risk Assessment' funded by the Italian Ministry of Education, University and Research (MIUR).

Download Full-text