Banca de QUALIFICAÇÃO: LARISSA INGRID MARQUES LINHARES

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : LARISSA INGRID MARQUES LINHARES
DATE: 19/12/2024
TIME: 10:00
LOCAL: Remoto - Google Meet
TITLE:

CLIMATE ASPECTS AND SURFACE MONITORING OF AREAS SUSCEPTIBLE TO DESERTIFICATION IN BRAZIL

KEY WORDS:

aridity index, semiarid, climate change, desertification, Northeast Brazil, remote sensing

PAGES: 60
BIG AREA: Ciências Exatas e da Terra
AREA: Geociências
SUBÁREA: Meteorologia
SPECIALTY: Climatologia
SUMMARY:

In a general context, desertification refers to the degradation of arid, semi-arid, and dry subhumid areas, driven by climatic characteristics and exacerbated by anthropogenic activities. Global climate change has consequently increased the extent of desertified and desertification-prone areas worldwide. Future projections indicate greater precipitation variability, rising temperatures, and the gradual loss of vegetation cover, which, combined with human activities, further intensify these scenarios. In Brazil, desertification is an ongoing environmental phenomenon, particularly in its drylands. This thesis aims to enhance the understanding of desertification in Brazil through three studies: a climatic analysis of aridity, the identification and characterization of desertified areas via remote sensing, and an attempt to attribute the causes of desertification to either climatic or anthropogenic factors. In the first study, the aridity index was evaluated for the entire country from both a climatological perspective and an annual frequency analysis for two periods: 1961-1990 and 1991-2020. The results aligned with similar studies, indicating a gradual increase in aridity, particularly in the Northeast region, primarily associated with recent precipitation variability despite rising temperatures observed nationwide. Furthermore, there is evidence of an expansion of arid lands toward the northeastern Amazon region in Maranhao, northern Minas Gerais, and western Mato Grosso do Sul near the Bolivian border. The second study proposes the use of various remote sensing-derived indices in a machine learning-based image classification model to detect the temporal evolution of desertification in desertification-prone areas of the country. Finally, the third study seeks to apply a Human Appropriation of Net Primary Production (HANPP) model, also based on satellite monitoring, to attribute the observed desertification to either natural climatic or anthropogenic causes.

COMMITTEE MEMBERS:
Presidente - 3217859 - PEDRO RODRIGUES MUTTI
Interno - 2086472 - BERGSON GUEDES BEZERRA
Externa à Instituição - BEATRIZ FUNATSU
Externo à Instituição - VINCENT DUBREUIL