Renata Pacheco Quevedo

29 Followers
41 Following
6 Posts

Geographer | Researcher | PhD in Remote Sensing from Brazil's National Institute for Space Research (INPE)

#landslides #earthobservation #remotesensing #machinelearning #geomorphology #landcover

Pronounsshe/her/ela
Researchorcid.org/0000-0002-7528-9166
ResearchGateresearchgate.net/profile/Renata-Pacheco-Quevedo
LinkedInlinkedin.com/in/renatapachecoquevedo/

review of landslide susceptibility studies focused on LULC.

The development of this study had the collaboration of researchers from ESPOL/CIPAT-ESPOL, Ecuador, Universidad de Almería, Spain,
and University of Potsdam, Germany.

Thank you very much Andrés Velástegui Montoya, Néstor Montalván, Fernando Morante-Carballo, Oliver Korup, and Camilo Daleles Rennó.

#LULC #LUCC #landslide #landslidesusceptibility #susceptibility #Disaster #review #bibliometrics #academicresearch #phd #doctorate

And that "the importance of considering LULC or LUCC on landslide assessment relies on the impacts of human activities on slopes, mainly agricultural and forestry activities, which are also affected by global warming and call for efficient management strategies to reduce susceptibility".

So, we reviewed 536 articles and the results highlighted publication trends, co-authorship and geographic spread of the selected studies, the most used keywords and research areas, and we added a section with a

🚨 PhD paper alert 🤓

I'm very happy to share our new paper (part of my PhD at INPE) about the use of LULC/LUCC in landslide susceptibility studies. We combined bibliometrics and traditional review approaches and highlighted some future challenges.
We were guided by the idea that "considering that landslides will occur under the same conditions as past landslides might represent a limited vision, as hillslope conditions change drastically in response to human activities".
https://lnkd.in/dtushed9

Land use and land cover as a conditioning factor in landslide susceptibility: a literature review - Landslides

Landslide occurrence has become increasingly influenced by human activities. Accordingly, changing land use and land cover (LULC) is an important conditioning factor in landslide susceptibility models. We present a bibliometric analysis and review of how LULC was explored in the context of landslide susceptibility in 536 scientific articles from 2001 to 2020. The pattern of publications and citations reveals that most articles hardly focus on the relationship between LULC and landslides despite a growing interest in this topic. Most research outputs came from Asian countries (some of which are frequently affected by landslides), and mostly with prominent international collaboration. We recognised three major research themes regarding the characteristics of LULC data, different simulated scenarios of LULC changes, and the role of future scenarios for both LULC and landslide susceptibility. The most frequently studied LULC classes included roads, soils (in the broadest sense), and forests, often to approximate the negative impacts of expanding infrastructure, deforestation, or major land use changes involving agricultural practice. We highlight several articles concerned primarily with current practice and future scenarios of changing land use in the context of landslides. The relevance of LULC in landslide susceptibility analysis is growing slowly, though with much potential to be explored for future LULC scenario analysis and to close gaps in many study areas.

SpringerLink

#Newpaper #landslide
It is a pleasure to share with you our new paper about #landslide #susceptibility #modelling, applied to a Part of the Western Ghats, India. In this article, we used an ensemble of the AHP and fuzzy logic models.
Check out the full paper:

https://doi.org/10.3390/land12020468

Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps

Landslides are prevalent in the Western Ghats, and the incidences that happened in 2021 in the Koottickal area of the Kottayam district (Western Ghats) resulted in the loss of 10 lives. The objectives of this study are to assess the landslide susceptibility of the high-range local self-governments (LSGs) in the Kottayam district using the analytical hierarchy process (AHP) and fuzzy-AHP (F-AHP) models and to compare the performance of existing landslide susceptible maps. This area never witnessed any massive landslides of this dimension, which warrants the necessity of relooking into the existing landslide-susceptible models. For AHP and F-AHP modeling, ten conditioning factors were selected: slope, soil texture, land use/land cover (LULC), geomorphology, road buffer, lithology, and satellite image-derived indices such as the normalized difference road landslide index (NDRLI), the normalized difference water index (NDWI), the normalized burn ratio (NBR), and the soil-adjusted vegetation index (SAVI). The landslide-susceptible zones were categorized into three: low, moderate, and high. The validation of the maps created using the receiver operating characteristic (ROC) technique ascertained the performances of the AHP, F-AHP, and TISSA maps as excellent, with an area under the ROC curve (AUC) value above 0.80, and the NCESS map as acceptable, with an AUC value above 0.70. Though the difference is negligible, the map prepared using the TISSA model has better performance (AUC = 0.889) than the F-AHP (AUC = 0.872), AHP (AUC = 0.867), and NCESS (AUC = 0.789) models. The validation of maps employing other matrices such as accuracy, mean absolute error (MAE), and root mean square error (RMSE) also confirmed that the TISSA model (0.869, 0.226, and 0.122, respectively) has better performance, followed by the F-AHP (0.856, 0.243, and 0.147, respectively), AHP (0.855, 0.249, and 0.159, respectively), and NCESS (0.770, 0.309, and 0.177, respectively) models. The most landslide-inducing factors in this area that were identified through this study are slope, soil texture, LULC, geomorphology, and NDRLI. Koottickal, Poonjar-Thekkekara, Moonnilavu, Thalanad, and Koruthodu are the LSGs that are highly susceptible to landslides. The identification of landslide-susceptible areas using diversified techniques will aid decision-makers in identifying critical infrastructure at risk and alternate routes for emergency evacuation of people to safer terrain during an exigency.

MDPI

Today our article "#Landuse and #landcover as a conditioning factor in landslide #susceptibility: a #literaturereview" was accepted to be published in #Landslides, the leading journal on this subject.

This article is a part of my PhD thesis focused on landslide susceptibility and the influence of spatial heterogeneity and #LUCC.

I am very happy to end 2022 with this news!

Thank you to all the collaborators!!