High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2–7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder–decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model.

Remote sensing involves actions to obtain information about an area located on Earth. In the Amazon region, the presence of clouds is a common occurrence, and the visualization of important terrestrial information in the image, like vegetation and temperature, can be difficult. In order to estimate land surface temperature (LST) and the normalized difference vegetation index (NDVI) from satellite images with cloud coverage, the inpainting approach will be applied to remove clouds and restore the image of the removed region. This paper proposes the use of the neural network LaMa (large mask inpainting) and the scalable model named Big LaMa for the automatic reconstruction process in satellite images. Experiments are conducted on Landsat-8 satellite images of the Amazon rainforest in the state of Acre, Brazil. To evaluate the architecture’s accuracy, the RMSE (root mean squared error), SSIM (structural similarity index) and PSNR (peak signal-to-noise ratio) metrics were used. The LST and NDVI of the reconstructed image were calculated and compared qualitatively and quantitatively, using scatter plots and the chosen metrics, respectively. The experimental results show that the Big LaMa architecture performs more effectively and robustly in restoring images in terms of visual quality. And the LaMa network shows minimal superiority for the measured metrics when addressing medium marked areas. When comparing the results achieved in NDVI and LST of the reconstructed images with real cloud coverage, great visual results were obtained with Big LaMa.

Land Surface Temperature (LST) monitoring is a key factor in demonstrating global climate change. Considering that cloud cover often obscures accurate remotely sensed LST data, it is important to develop approaches to reconstruct occluded areas. This paper presents an inpainting technique, designated RePaint, which utilizes Denoising Diffusion Probabilistic Models (DDPM) for the removal of clouds and the retrieval of LST data from Landsat-8 remote sensing imagery. The DDPM was trained with cloud-free LST image patches, and binary masks were formed by the segmentation process using the Ukis Cloud Shadow Mask to condition the region to be reconstructed. To simulate cloud occlusion in the test data, three cloud cover scenarios with 15%, 22% and 50% missing rate data were investigated. The performance of the approach was evaluated through a combination of quantitative and qualitative assessments. The quantitative analysis showed that the model exhibited consistent performance during the reconstruction process, achieving a mean squared error 0.0001, a peak signal to noise ratio 41.2888, and a structural similarity index 0.9826. In order to provide a more comprehensive visual representation of the results, boxplots and the Gaussian distribution of the temperature error in the LST reconstruction were included. Images of the absolute error of the reconstruction and scatterplots were used for qualitative analysis of randomly selected LST patches. The results demonstrate that the characteristics of the land surface have been preserved, exhibiting a minimum incidence of temperature reconstruction error and spatial coherence across all scenarios.

Resumo Ilha de calor é um fenômeno que ocorre em cidades, ocasionando um aumento considerável da temperatura da região em relação a suas regiões vizinhas. Este artigo apresenta a análise temporal da temperatura de superfície do solo, conhecida como LST (Land Surface Temperature), e do índice de vegetação normalizada diferenciada NDVI (Normalized Difference Vegetation Index) no intuito de aferir o surgimento de ilhas de calor a partir do processamento digital de imagens na cidade de Rio branco, capital do estado do Acre, no período de 2000 a 2020. Para a análise, foram utilizadas imagens digitais geradas pelo do satélite Landsat-5 e Landsat-8. As etapas presentes no modelo SEBAL (Surface Energy Balance Algorithms for Land) estão sendo aplicadas para cálculo da temperatura e do índice de vegetação a partir das bandas obtidas com as imagens dos satélites. Os resultados encontrados para o NDVI indicam um decréscimo de 1,08% na vegetação média/densa quando comparados índices dos anos de 2000 e 2020, os resultados também evidenciam um aumento da temperatura média da superfície de cerca de 14,97%, principalmente devido a diminuição de áreas verdes, crescimento urbano desordenado e impermeabilização do solo devido ao revestimento asfáltico. Portanto, estes fatores contribuíram para o aparecimento de Ilhas de Calor, especificamente na área analisada é possível notar o surgimento de uma Ilha de Calor localizada no centro da cidade de Rio Branco.

Denoising diffusion probabilistic models (DDPMs) have demonstrated significant potential in addressing complex image processing challenges. This paper explores the application of DDPMs in three different areas: reconstruction of remote sensing imagery affected by cloud cover, reconstruction of facial images with occluded areas, and segmentation of bodies of water from remote sensing imagery. Inpainting involves filling in missing regions in images, while DDPMs act as data generators capable of synthesizing information that alings coherently with the context of the original data. Inspired by the inpainting technique, the RePaint approach was adapted and applied to reconstruction tasks. The WaterSegDiff approach, which uses a diffusion model as a backbone, was employed for the segmentation task. To illustrate the model’s behavior and provide examples of the tasks, experiments were carried out with both qualitative and quantitative evaluations. The qualitative results show the model’s ability to generate data for reconstruction and segmentation. Quantitatively, metrics such as MSE, PSNR, SSIM, IoU, PA and F1 score highlight the model’s proficient performance in image processing tasks. In this scenario, DDPMs have proved to be a promising tool for high-quality data reconstruction, enabling the hallucination of image regions with high visual coherence and facilitating applications in various areas, such as environmental monitoring, facial recognition, water resource mapping, among others.