A procedure is introduced for the analysis of seasonal trends in time series of Earth observation imagery. Called Seasonal Trend Analysis (STA), the procedure is based on an initial stage of harmonic analysis of each year in the series to extract the annual and semi‐annual harmonics. Trends in the parameters of these harmonics over years are then analysed using a robust median‐slope procedure. Finally, images of these trends are used to create colour composites highlighting the amplitudes and phases of seasonality trends. The technique specifically rejects high‐frequency sub‐annual noise and is robust to short‐term interannual variability up to a period of 29% of the length of the series. It is, thus, a very effective procedure for focusing on the general nature of longer‐term trends in seasonality.

Indigenous territories are considered important for conservation, but little is known about their role in maintaining human health. Here we quantified the potential human health and economic benefits of protecting these territories in the Brazilian Amazon, by using cardiovascular and respiratory diseases cases, pollutant and forest cover data. Between 2010 and 2019, 1.68 tons of Particulate Matter of small size (PM_2.5) were released every year, with negative effects for human health. A lower number of diseases and infections was also found in municipalities with more forested areas, and with a low level of fragmentation, which probably is related to the potential capacity of the Amazon Forest to absorb PM_2.5 (26,376.66 tons year⁻¹, 27% of this absorption capacity in Indigenous territories). Our estimates indicate that by protecting Amazon Indigenous territories, over 15 million of respiratory and cardiovascular cases could be avoided every year, with ~$2 billion USD being saved only in health costs.

Landsat-8 derived Land Surface Temperature (LST) is used to measure Surface Urban Heat Island (SUHI) patterns and intensity in Worcester, MA, USA. Additionally, near-surface air temperature variability is measured using in-situ sensors to further contextualize the urban-to-rural land-cover driven thermal patterns in the study area. Despite the widespread applicability of thermal data, many SUHI studies do not compare LST with in-situ information. Comparisons between satellite-based and in-situ measurements of land surface temperature (LST) are important for establishing confidence in the utility of remotely sensed information to monitor and help improve the lived experience in cities. The objective of this study is to determine the capability of Landsat-8 Thermal Infrared Scanner (TIRS) to measure fine-scale temperature variation in a moderately sized urban area with a mixture of land-cover types. Ground-Level Temperature (GLT) was measured at 13 sites using iButton® Thermochron® temperature sensors from 13 June 2013 to 28 October 2014. Landsat-derived LST was compared to in-situ GLT using 30 Landsat-8 TIRS images for the commensurate time period. Sites with 1) eastern solar exposure; 2) low tree canopy coverage; and 3) proximity to impervious surfaces have higher annual temperature and greater offset from Landsat-derived LST. Sites with more than 47% tree canopy coverage have a more consistent LST and GLT relationship, (e.g. MAE < 3.74°C), show lower annual variability (e.g. r² > 0.85), and also experienced low LST variability over the time series (e.g. coefficient of variation 0.007). Results indicate that site characteristics and land-cover type affect the offset between in-situ air temperature measurements and satellite-derived LST.

Increased wildfire activity and altered post-fire climate in the Southern Rocky Mountains has the potential to influence forest resilience. The Southern Rocky Mountains are a leading edge of climate change and have experienced record-breaking fires in recent years. The change in post-fire regeneration and forest resilience could potentially include future ecological trajectories. In this paper, we examined patterns of post-fire spectral recovery using Landsat time series. Additionally, we utilized random forest models to analyze the impact of climate and burn severity on three fire events in the Southern Rocky Mountains. Fifteen years following the fires, none of the burned stands fully recovered to their pre-fire spectral states. The results suggested that burn severity significantly impacted post-fire spectral recovery, but that influence may decrease as time since fire increases. The biggest difference in forest recovery was among fire events, indicating that post-fire climate may be influential in post-fire recovery. The mean and minimum growing-season temperatures were more significant to post-fire recovery than the variability in precipitation, which is consistent with field-based analysis. The present study indicated that, as warming continues, there may be changes in forest density where forests are not regenerating to their pre-fire spectral states. Additionally, this study emphasizes how high-elevation forests continue to regenerate after fires, but that regeneration is markedly affected by post-fire climate.