The Land Surface Temperature (LST) obtained by remote sensing data is an essential indicator for analyzing the Surface Urban Heat Island (SUHI). A factor contributing to its occurrence is the reflections of the different materials in urban and rural areas, which significantly affect the energy balance near the surface. Therefore, recent studies have increasingly used the Local Climate Zones (LCZs) classification system to discriminate those urban areas. Therefore, our study aims to perform a systematic review using the PRISMA method of LCZ classification applied to understand the LST and the SUHI phenomenon. At first, 10,403 related articles were found in the scientific literature which, after passing through filtering stages, resulted in 51 that were further analyzed. Our results showed that these articles were very recent, beginning in 2016, and present an increasing trend. Even though all articles reviewed somehow try to understand the Urban Heat Island phenomenon, in more than half of them, the SUHI calculation method was not mentioned. China was the country with the most studies. Images from Landsat and TERRA/AQUA sensors appeared in 82% of the studies. The method that appears the most to LCZ definitions is from the World Urban Database. Finally, we conclude that LCZ mapping methodologies were accurate enough for SUHI studies considering the current climatic changes. Therefore, this systematic review can help new studies on SUHI identification through LCZ in different world areas using remote sensing data to estimate the LST.

The availability of accurate meteorological data is important for the modeling of atmospheric flows, enabling the understanding of climate change in a given environment over time. Therefore, this study aimed to evaluate the quality of the meteorological data of 2 m temperature (T) and mean sea level pressure (MSLP), obtained through the atmospheric reanalysis models ERA5 and ERA-Interim (ERA-i) in the northern region of the Antarctic Peninsula, covering the period from 1979 to 2018. To carry out the statistical process, local observations from nine weather stations installed in the study region were used, with data available from the Scientific Committee on Antarctic Research. Statistical analysis evaluated Pearson's linear correlation coefficient (R), standard deviation (sigma), normalized bias, normalized mean absolute error, and normalized root mean square error. Regarding the difference in (T) over bar between weather stations and reanalysis models, we found a better result for ERA5 (Delta(T) over bar = -0.34 degrees C) compared to ERA-i (Delta(T) over bar = 0.37 degrees C); however, for the difference in (MSLP) over bar the ERA-i (Delta(MSLP) over bar = -0.04 hPa) presented a better response in relation to ERA5 (Delta(MSLP) over bar = 0.24 hPa). When verifying the local meteorological observations trend, an increase of T in 0.31 degrees C decade(-1) (Esperanza station) and a reduction of MSLP between -0.56 (Bellingshausen station) and -0.80 hPa decade(-1) (Jubany station) were recorded.

Dietary guidelines urge Brazilians to increase their consumption of raw vegetables. Yet key issues must be tackled by the government and civil society, not only to foster consumers’ appetite for healthier food, but more importantly to diminish the gaps between local demand and production, determined by food and land accessibility. We examine whether vegetable production in Brazil meets the demand to provide Brazilians the daily amount of fresh food recommended by the World Health Organization (WHO). We developed demand scenarios in Brazil for 2008 and 2030, based on demand density maps built at the district level using production census surveys, household acquisition data, and population growth estimates. Results reveal an inherent inequality in vegetable consumption between the southern and central northern regions of Brazil that follows food insecurity regional indicators. Even in more urbanized regions and metropolitan areas, where the best balance between vegetable production and acquisition is found, simulated demand is far from WHO recommendations. A complementary discussion regarding land distribution and fresh food production supports our outlook on the weaknesses of existing rural policies for land reform and sustainable local fresh food production that directly affect demand and nutritional security. This work was the foundation to the Delivering Food Security on Limited Land (DEVIL) project in Brazil supported by Belmont Forum consortium.

The classification of Synthetic Aperture Radar (SAR) images by knowledge-based algorithms with elevation and backscatter thresholds were used in several studies to detect the Wet Snow Radar Zone (WSZ) in the Antarctic Peninsula. To identify it more accurately based on its seasonal variations, this study proposed the additional use of a threshold in synthetic images, created by rationing summer and winter sigma linear images. In our algorithm we used the following thresholds to detect the WSZ in Envisat ASAR imageries, using the Radarsat Antarctic Map Digital Elevation Model as ancillary data: i) -25 dB < s0 < -14 dB; ii) slinear summer / slinear winter < 0.4; iii) elevation H < 1,200 m for northern tip and H < 800 m for southern tip of the Antarctic Peninsula. The classified images were post-processed by a focal majority 5 x 5 filter and superimposed by an image of rock outcrops derived from the Antarctic Digital Database. The ratio image threshold allowed discriminating the WSZ from the Dry Snow Radar Zone and radar shadows, as well as transitional areas between this glacier zone and the Frozen Percolation Radar Zone, which would be classified incorrectly if we used only elevation and backscatter thresholds.

Urbanization in large cities has altered the urban thermal balance, creating urban heat islands. In this context, green areas are crucial in regulating the urban climate. This study uses remote sensing data to evaluate their performance using the fractional vegetation cover (FVC) and its impact on land surface temperature (LST) in Metropolitan Lima, Peru, between 1986 and 2024. The spatial and temporal relationship between FVC and LST is analyzed, and districts are classified based on their effectiveness in thermal regulation. The Mann–Kendall test was applied to identify trends along with a Spearman correlation analysis and a clustering analysis to group districts according to the cooling effectiveness of their urban green areas. The results show that urban expansion has increased LST by an average of 6.43 °C since 1990, and there is a significant negative correlation (p < 0.001) between FVC and LST, indicating positive impacts of vegetation regulating LST at a spatial level. However, it does not reduce LST at a temporal level. This suggests that, while effective locally, green areas are insufficient to counteract the overall warming of LST over time. Based on FVC and LST characteristics, the districts have been classified into four groups: those with well-preserved green areas, such as La Molina and San Isidro, which have a lower LST, compared to areas where urbanization has replaced vegetation, such as Carabayllo and Lurigancho (Chosica). Finally, this study highlights the importance of integrating green area management into urban planning to mitigate urban warming and promote sustainable development.

Assessing the fire impact on grasslands requires understanding how the environmental and anthropic relationships affect the landscape dynamics. This study carries out a systematic literature review to understand fire behavior in grasslands through remote sensing techniques. To this end, the Scopus data was used employing the PRISMA method with the cluster mapping aid. Initially, 7,881 articles were found in the literature. The methodological steps applied to them resulted in 67 articles, which were used in the analysis. The results indicated increased interest in research in the area, with Brazil having the second-highest number of studies. Several publications utilized orbital images. However, there has been recent growth in the use of images obtained from UAV-mounted sensors. In addition to the NDVI and EVI indices, other indices have been used recently for analyzing burn severity and the vegetation recovery process. These subjects are primarily related to integrated fire management, which must consider conserving biodiversity and human use to reduce the fire's intensity and severity to make them more controllable and reduce their negative impacts. Therefore, remote sensing is essential for understanding the fire spatial-temporal behavior and, consequently, serves as a scientific aid to help decision-making in burn prescription cases, considering the ecosystems maintenance and the better grassland use.