This project presents a methodology to identify deforestation in the Matahuayo sector. Tahuayo, which arises from the problem of the expansion of deforestation in tropical forests from Peru. The main objective of this study is to implement a methodology that allows detecting and quantify the deforested areas in the aforementioned sector, located in the department of Loreto. 36 Landsat 5, 7 and 8 L1T level image data were used for the time 2007 - 2015 with spatial resolution of 30 m x 30 m. The implemented methodology consists of three phases: (i) The preprocessing stage, the radiometric correction was developed, the TOA reflectance was calculated and atmospheric correction to obtain the reflectance of the ground surface. (ii) In processing Spatial and temporal distribution maps were produced from different biophysical indices (NDVI, RVI) and transformations (Tasseled Cap, PCA) that const...

La concentración de clorofila-a en los océanos es un indicador confiable de biomasa de fitoplancton que desempeña un papel importante en el control del ecosistema marino. El objetivo principal de este estudio es analizar la variabilidad de la concentración de clorofila-a (Chl-a) y temperatura superficial del mar (TSM) en el ecosistema de afloramiento peruano en la escala de tiempo interanual, utilizando información satelital del sensor MODIS a bordo del satélite Aqua en el periodo de 2003 hasta 2021. El área de estudio está delimitada por la isobata de 1mg m−3 de clorofila-a y las latitudes de 5°S y 20°S, esta área se divide en dos zonas, norte-centro (5°S-16°S) y sur (16°S-20°S). Iniciamos investigando las tendencias lineales, donde los resultados indicaron que se produjeron tendencias negativas de los dos parámetros en el área de estudio, notando que para la TSM es ...

We quantified the influence of sea surface temperature (SST) in the Niño 1+2 region and precipitation on the NDVI dynamics of the dry forest along Peru’s northern coast during 2003–2023. We used monthly series from MODIS (NDVI), CHIRPS (precipitation), and NOAA SST, seasonally standardized (z-score). We applied a 36-month rolling correlation, time–frequency analysis (XWT/WTC), and autoregressive models with exogenous regressors (ARX). Cross-correlation showed that NDVI responds with a positive one-month lag to both SST and precipitation, with correlation coefficients of 0.764 and 0.613, respectively. Among four AR(2) models evaluated, the ARX with lagged SST provided the best fit (AIC = 295.35), slightly outperforming the combined SST+precipitation model (AIC = 295.46). Akaike weights favored the former on grounds of parsimony. The results indicate a positive sensitivity of NDVI t...