New advancements in satellite technology enable more accurate observation of woody population dynamics, providing greater insights into the underlying processes that influence their change. In this study, we evaluate the use of PlanetScope NDVI time series to track the phenology of individual trees in the Sahel, where ground- based environmental surveys are scarce. Five-year NDVI time series were produced for 398 trees with known species recorded in Mali, Senegal, and Niger. Clouds and high aerosol contamination were filtered using MODIS products and focused on the dry season to minimize the influence of background NDVI directly (through crown influence) or indirectly (through adjacency effects). Each NDVI time series profile was fitted with a spline model to obtain the minimum NDVI day of year during the dry season. PlanetScope NDVI time series accurately captured the photosynthetic phenology of individual tree crowns in the Sahel, with discernable differences between individuals and species. When species were grouped based on four phenology types, deciduous and inverse deciduous species exhibited a relatively consistent phenological pattern across all sites. The phenology of semi- evergreen species and evergreen species, which include species with few leaves, was more heterogeneous. Intraspecies variation was relatively modest between sites, and most species maintained a similar NDVI profile, with shifts in leaf phenology events correlating with the timing of the wet season in each site. Overlap between the different phenology groups indicates that transitions between phenology types and species are not clear-cut, and even individuals of the same species can demonstrate plasticity. Furthermore, NDVI profiles were extracted for 500 randomly selected tree samples within eight 10 km2 clip boxes distributed along the West African rainfall gradient from 9.9 degrees to 16.6 degrees latitude at-1.6 degrees longitude. This analysis showed a strong relationship between the phenology of woody plants and the timing and distribution of rainfall at each latitude. Green-up of woody vegetation before herbaceous vegetation was marked in the more southern Sahelo-Sudanian latitudes. Additionally, despite the prolonged dry season in the more northern semi-arid latitudes, trees retained their greenness remarkably late into the dry season. Increased air temperature and dryness as a result of climate change could impact tree function in this region and needs individual-based monitoring.

Determining the physical dimensions of hypervelocity impact structures is challenging due to erosion of their primary relief features on Earth. Critical measurements, such as outer rim diameter, are important for estimating the kinetic energy released and subsequent environmental effects. We developed a Radial Profile Analysis System algorithm, which uses a digital elevation model (DEM) to identify topographic rings surrounding a complex impact structure through an iterative process that assigns the most consistently identified positive relief ring as the apparent outer rim. We investigated five terrestrial impact structures across a range of apparent diameters whose physical dimensions are well established from geological or geophysical studies. Multiple DEM data sets from 2 to 30 m horizontal resolution were evaluated to determine the role of spatial resolution in estimating apparent outer rim diameters. The most reliable predictions were achieved using the 12 m TanDEM-X data set with an estimated error ≤16%. We then achieved an estimated error ≤8% when applying the algorithm to three Martian peak ring impact structures (Kepler, Lowell, and Lyot) using 200 m resolution DEMs, and 1%–13% when applied to a 600 m resolution DEM of the Mead multi-ringed impact structure on Venus. Our results indicated that apparent outer rim diameters of complex impact structures can be estimated using these methods with reasonable reliability, but prediction efficacy decreased with decreasing DEM vertical fidelity. Application of these methods to additional impact structures is required to quantify prediction uncertainty before applying this methodology to more recent impact structures with questionable diameters.

The expansion of trees on croplands in the Sahel has been promoted as a nature-based solution to climate challenges, particularly through Farmer Managed Natural Regeneration (FMNR). Yet large-scale assessments of cropland tree dynamics remain scarce. Here, we combine 25 years of Landsat imagery with a deep learning model trained on 9.9 billion trees to reconstruct annual tree cover at 15 m resolution across 2.4 million km² of Sahelian croplands. We find that 12% of all Sahelian trees occur on croplands and that 9.2 million hectares have gained tree cover since 1999. Gains are concentrated near villages and in regions with longstanding FMNR promotion, especially Maradi and Zinder. Areas with moderate to high FMNR intensity show greater increases than low-intensity areas. These findings provide large-scale evidence that farmer-led management has driven cropland greening, offering insights for sustainable land management, and establishing a framework for monitoring scattered trees in drylands.

The very high resolution commercial satellite constellation of Maxar offers unique opportunities for a wide range of Earth science research and applications.The key to their widespread and effective use is stable and consistent calibration.In this article, we characterized the long-term calibration trends and cross-calibration coefficients for the four Maxar satellites (GeoEye-1, QuickBird-2, WorldView-2, and WorldView-3) using the Multi-Angle Implementation of Atmospheric Correction (MA-IAC) processing technique.Utilizing MAIAC Moderate Resolution Imaging Spectroradiometer (MODIS) atmosphere and surface products, we calculated top-of-atmosphere (TOA) reflectance for the Blue, Green, Red, and near-infrared, or "NIR1" (NIR) bands over the Libya-4 desert site.To ensure data consistency, we applied geometric normalization to account for variations in TOA reflectance arising from different view geometries.In addition, a spatial transfer technique was employed to increase the number of samples and yield more robust statistical trend analysis.Our analysis revealed that half of the bands exhibited statistically significant calibration trends.These trends were found to be 2-3 times higher in magnitude compared with those observed in the early Collection 6 MODIS.After detrending, Maxar sensors were crosscalibrated to MODIS Aqua, considered as a calibration standard.In this process, German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer hyperspectral measurements were used for spectral conversion required to align Maxar with MODIS bands.The cross-calibration analysis shows that GeoEye-1, WorldView-2, and WorldView-3 were systematically higher than MODIS Aqua by 2%-4% in the Blue, Green, and NIR bands, and by 7%-8% in the Red band.Detrending and cross-calibration to MODIS Aqua effectively transforms the Maxar constellation into a common sensor system enhancing spatiotemporal coverage and broadening the potential range of applications.

The baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we use sub-metre-resolution satellite imagery to identify the presence of nearly 2.8 million (underestimation bias 27.1%) baobab trees in the Sahel, a dryland region of 2.4 million km2. This achievement is considered an essential step towards an improved management and monitoring system of valuable woody species. Using Senegal as a case country, we find that 94% of rural buildings have at least one baobab tree in their immediate surroundings and that the abundance of baobabs is associated with a higher likelihood of people consuming a highly nutritious food group: dark green leafy vegetables. The generated database showcases the feasibility of mapping the location of single tree species at a sub-continental scale, providing vital information in times when deforestation and climate change cause the extinction of numerous tree species.

Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = −6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Rapidly warming temperatures in the Arctic are driving increasing tundra vegetation productivity, evidenced in both the satellite derived normalized difference vegetation index (NDVI) imagery and field studies. These trends, however, are not uniformly positive across the circumpolar Arctic. One notable region of negative linear NDVI trends that have persisted over the last 15 years is southwest Alaska's Yukon-Kuskokwim Delta (YKD). Negative NDVI trends in the YKD region appear inconsistent with our understanding since tundra vegetation is temperature-limited and air temperatures have increased on the YKD. Analysis over a 40-yr record from 1982 to 2021 reveals distinct decadal variability in the NDVI time series, which continues to produce negative linear trends. Similar decadal variability is also evident in summer warmth and 100-km coastal zone spring sea ice concentrations. This suggests that decadal climate variations can dominate the trends of NDVI through their influence on the drivers of tundra vegetation, namely, coastal sea ice concentrations and summer warmth. The relationships among sea ice, summer warmth, and NDVI have changed over the 40-yr record. Seasonality analysis since 1982 shows declining sea ice concentration in spring is followed by trends of increasing temperatures, but weakly declining NDVI during the growing season. An additional key finding is that since early 2010s, the relationships between sea ice concentration and summer warmth, and sea ice concentration and NDVI have strengthened, while the relationship between NDVI and summer warmth has weakened, indicating that temperature may no longer be the primary limiting factor for Arctic tundra vegetation on the YKD.

Based on the recent success of our strained-layer superlattice (SLS)-based infrared (IR) camera that performed Earth imaging from the International Space Station (ISS) in 2019 we have built, what we consider, to be the next generation multi-band SLS imaging system. The Compact Thermal Imager (CTI) was installed on the Robotic Refueling Mission 3 (RRM3) and attached to the exterior of the ISS. From this location we were able to capture 15 million images of a multitude of fires around the globe in 2019. This unexpected trove of data initiated quite a bit of scientific interest to further utilize this imaging capability but would include features to more precisely monitor terrestrial fires and other surface phenomena. To this end, we developed a technique to install specific bandpass filters directly onto the SLS detector hybrid assembly. Utilizing this technique we have built a CTI-2 camera system with two filters, 4 and 11μm, and have made a second detector assembly with six filter bands from 4- 12μm. This second system will also be used to supplement Landsat remote imaging monitoring approximate land surface temperatures, monitor evapotranspiration, sea ice and glacier dynamics. The CTI-2 camera is based on a 1,024x1,024 (1kx1k) format SLS detector hybridized to a FLIR ISC0404 readout integrated circuit (ROIC). The six band SLS focal plane array is based on the 640x512 FLIR ISC 9803 ROIC. This camera system is based on the Landsat 8 and 9 Thermal IR Sensors (TIRS) instrument and one of its purposes is to perform ground truthing for the Landsat 8/9 data at higher spectral resolution. Both Landsat TIRS instruments are dual band thermal IR sensors centered on 11 and 12μm (each with about a 1μm bandpass). Both of our SLS systems utilize a Ricor K548 cryocooler. To streamline costs and development time we used commercial optics and both commercial and custom NASA electronic components. A primary feature of these camera systems is the incorporation of specific filters to collect fire data at ~3.9μm and thermal data at ~11μm. The CTI- 2 instrument is designed for 37 m /pixel spatial resolution from 410km orbit (ISS orbit). In this paper, we will present the design and performance of the focal plane, optics, electronics and mechanical structure of the dual-band CTI-2 and the focal plane performance of the six-band focal plane.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year-1 rainfall zone by coupling field data19, machine learning20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha-1 and 63 kg C tree-1 in the arid zone to 3.7 Mg C ha-1 and 98 kg tree-1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

The iconic baobab tree (Adansonia digitata L.) is an integral part of rural livelihoods throughout the African continent. However, the combined effects of climate change and increasing global demand for baobab products are currently exerting pressure on the sustainable utilization of these resources. Here we employ sub-meter resolution satellite imagery to identify the presence of nearly 2.8 (± 27.1%) million baobab trees in the Sahel, a dryland region of 1.5 million km2. This achievement is considered an essential step towards an improved management and monitoring system of valuable woody species. Using Senegal as a case country, we find that 94% of rural households have at least one baobab tree in their immediate surrounding, and that the abundance of baobabs is associated with a higher likelihood of people consuming a highly nutritious food group: dark green leafy vegetables. The generated database showcases the feasibility of mapping the location of single tree species at a sub-continental scale, providing vital information in times where human mismanagement and climate change cause the extinction of numerous tree species.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Abstract The consistent monitoring of trees both inside and outside of forests is key to mitigating climate change. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we make use of the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 demonstrates that a precise assessment of all tree-based systems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover, such as in croplands and grassland. Such accurate mapping of tree cover at metric resolution down to the level of individual trees and consistent among countries has the potential to redefine land use impacts, move beyond the need for forest definitions, build the basis for natural climate solutions, and provide a new scientific basis for tree related studies.

During 2019, an infrared camera, the compact thermal imager (CTI), recorded 15 million images of the Earth from the International Space Station. CTI is based on strained-layer superlattice (SLS) detector technology. The camera covered the spectral range from 3 to 11 µm in two spectral channels, 3.3-5.4 and 7.8-10.7 µm. Individual image frames were 26×21km2 projected on the ground, with 82 m pixel resolution. A frame time of 2.54 s created continuous image swaths with a 13% along-track image overlap. Upper limits determined on the ground and in flight for the electronic offset, read noise, and dark current demonstrated the stability of the SLS detector and camera over many months. Temperature calibration was established using a combination of preflight and in-flight measurements. A narrowband approximation of temperature as a function of photon counts produced an analytic relationship covering a temperature range of 0°-400°C. Examples of CTI images illustrate temperature retrievals over sea ice, urban and agricultural areas, desert, and wildfires.

Alpine glaciers in the low- and mid-latitudes respond more quickly than large polar ice sheets to changes in temperature, precipitation, cloudiness, humidity, and radiation. Many high-altitude glaciers are monitored by ground observations, aerial photography, and satellite-borne sensors. Regardless of latitude and elevation, nearly all nonpolar glaciers and ice caps are undergoing mass loss, which compromises the records of past climate preserved within them. Almost without exception, the retreat of these ice fields is persistent, and a very important driver is the recent warming of the tropical troposphere and oceans. Here we present data on the decrease in the surface area of four glaciers from low- to mid-latitude mountainous regions: the Andes of Peru and northern Bolivia, equatorial east Africa, equatorial Papua, Indonesia, and the western Tibetan Plateau. Climate records based on oxygen isotopic ratios (delta O-18) measured in ice cores drilled from several glaciers in these regions reveal that the records from elevations below similar to 6000 m above sea level have been substantially modified by seasonal melting and the movement of meltwater through porous upper firn layers. Fortunately, delta O-18 records recovered from higher altitude sites still contain well-preserved seasonal variations to the surface; however, the projected increase in the rate of atmospheric warming implies that climate records from higher elevation glaciers will eventually also be degraded. A long-term ice core collection program on the Quelccaya ice cap in Peru, Earth's largest tropical ice cap, illustrates that the deterioration of its climate record is concomitant with the increase in mid-troposphere temperatures. The melting ice and resulting growth of proglacial lakes presents an imminent hazard to nearby communities. The accelerating melting of glaciers, if sustained, ensures the eventual loss of unique and irreplaceable climate histories, as well as profound economic, agricultural, and cultural impacts on local communities.

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

NASA's Commercial Smallsat Data Acquisition (CSDA) program was initiated with a goal of acquiring data from commercial sources that support NASA's Earth science research and application goals. Over the last several years, the CSDA program has evolved into a long-term sustained program. This paper presents an overview of the program, a featured innovative application, and data management capabilities.

Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 km2 and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha−1 of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 km2 and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.