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As offshore fish farms are established farther away from the shore, increased exposure to the elements prevents regular operations from being performed safely with vessels moored alongside the flexible fish cage and personnel performing tasks standing on the collar. Due to the higher environmental impact at more exposed locations, new concepts and solutions for automating daily aquaculture operations need to be developed. One solution that has been proposed is to carry out operations using a robotic arm mounted on the main deck of a service vessel while it does stationkeeping next to the cage. The purpose of this article is to summarise our research on the viability of this concept. Vessel motions are simulated for a representative vessel model and realistic sea states, and a robotic arm does motion-compensated trajectory tracking while mounted on a hexapod platform moving according to the simulated vessel motions. Relevant challenges in marine aquaculture operations are summarised, the method used to obtain realistic simulated vessel motions is documented, the results of the experiments are presented, and the remaining open questions to evaluate the potential of the proposed system are discussed. • Simulated station-keeping performance of a vessel at an exposed aquaculture site. • Modelled autonomous fish cage operations with vessel-mounted robotic arm. • Performed motion-compensated tracking with robotic arm mounted on hexapod platform. [ABSTRACT FROM AUTHOR]

The impact of human activities via land use/cover changes on NDVI trends is critical for an improved understanding of satellite-observed changes in vegetation productivity in drylands. The dominance of positive NDVI trends in the Sahel, the so-called re-greening, is sometimes interpreted as a combined effect of an increase in rainfall and cropland expansion or agricultural intensification. Yet, the impact of changes in land use has yet to be thoroughly tested and supported by empirical evidence. At present, no studies have considered the importance of the different seasonal NDVI signals of cropped and fallowed fields when interpreting NDVI trends, as both field types are commonly merged into a single ‘cropland’ class. We make use of the distinctly different phenology of cropped and fallowed fields and use seasonal NDVI curves to separate these two field types. A fuzzy classifier is applied to quantify cropped and fallowed areas in a case study region in the southern Sahel (Fakara, Niger) on a yearly basis between 2000 and 2014. We find that fallowed fields have a consistently higher NDVI than unmanured cropped fields and by using two seasonal NDVI metrics (the amplitude and the decreasing rate) derived from the MODIS time series, a clear separation between classes of fields is achieved ( r = 0.77). The fuzzy classifier can compute the percentage of a pixel (250 m) under active cultivation, thereby alleviating the problem of small field sizes in the region. We find a predominant decrease in NDVI over the period of analysis associated with an increased area of cropped fields at the expense of fallowed fields. Our findings couple cropping abandonment (more frequent fallow years) with positive NDVI trends and an increase in the percentage of the cropped area (fallow period shortening) with negative trends. These findings profoundly impact our understanding of greening and browning trends in agrarian Sahelian drylands and in other drylands of developing countries characterized by limited use of fertilizers. [ABSTRACT FROM AUTHOR]

Woody plants play a major role for the resilience of drylands and in peoples' livelihoods. However, due to their scattered distribution, quantifying and monitoring woody cover over space and time is challenging. We develop a phenology driven model and train/validate MODIS (MCD43A4, 500 m) derived metrics with 178 ground observations from Niger, Senegal and Mali to estimate woody cover trends from 2000 to 2014 over the entire Sahel. The annual woody cover estimation at 500 m scale is fairly accurate with an RMSE of 4.3 (woody cover %) and r 2 = 0.74. Over the 15 year period we observed an average increase of 1.7 (± 5.0) woody cover (%) with large spatial differences: No clear change can be observed in densely populated areas (0.2 ± 4.2), whereas a positive change is seen in sparsely populated areas (2.1 ± 5.2). Woody cover is generally stable in cropland areas (0.9 ± 4.6), reflecting the protective management of parkland trees by the farmers. Positive changes are observed in savannas (2.5 ± 5.4) and woodland areas (3.9 ± 7.3). The major pattern of woody cover change reveals strong increases in the sparsely populated Sahel zones of eastern Senegal, western Mali and central Chad, but a decreasing trend is observed in the densely populated western parts of Senegal, northern Nigeria, Sudan and southwestern Niger. This decrease is often local and limited to woodlands, being an indication of ongoing expansion of cultivated areas and selective logging. We show that an overall positive trend is found in areas of low anthropogenic pressure demonstrating the potential of these ecosystems to provide services such as carbon storage, if not over-utilized. Taken together, our results provide an unprecedented synthesis of woody cover dynamics in the Sahel, and point to land use and human population density as important drivers, however only partially and locally offsetting a general post-drought increase. [ABSTRACT FROM AUTHOR]

Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages. In this study, we assess the capability of a long-term VOD dataset (1992–2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel. Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r 2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r 2 = 0.90). Overall, in spite of the coarse resolution, the study shows that VOD is an efficient proxy for estimating green biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas. [ABSTRACT FROM AUTHOR]

From in situ measured woody cover we develop a phenology driven model to estimate the canopy cover of woody species in the Sahelian drylands at 1 km scale. The model estimates the total canopy cover of all woody phanerophytes and the concept is based on the significant difference in phenophases of dryland trees, shrubs and bushes as compared to that of the herbaceous plants. Whereas annual herbaceous plants are only green during the rainy season and senescence occurs shortly after flowering towards the last rains, most woody plants remain photosynthetically active over large parts of the year. We use Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l'Observation de la Terre (SPOT) — VEGETATION (VGT) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series and test 10 metrics representing the annual FAPAR dynamics for their ability to reproduce in situ woody cover at 43 sites (163 observations between 1993 and 2013) in the Sahel. Both multi-year field data and satellite metrics are averaged to produce a steady map. Multiple regression models using the integral of FAPAR from the onset of the dry season to the onset of the rainy season, the start date of the growing season and the rate of decrease of the FAPAR curve achieve a cross validated r 2 /RMSE (in % woody cover) of 0.73/3.0 (MODIS) and 0.70/3.2 (VGT). The extrapolation to Sahel scale shows agreement between VGT and MODIS at an almost nine times higher woody cover than in the global tree cover product MOD44B which only captures trees of a certain minimum size. The derived woody cover map of the Sahel is made publicly available and represents an improvement of existing products and a contribution for future studies of drylands quantifying carbon stocks, climate change assessment, as well as parametrization of vegetation dynamic models. [ABSTRACT FROM AUTHOR]

In the past 50 years, the Sahel has experienced significant tree- and land cover changes accelerated by human expansion and prolonged droughts during the 1970s and 1980s. This study uses remote sensing techniques, supplemented by ground-truth data to compare pre-drought woody vegetation and land cover with the situation in 2011. High resolution panchromatic Corona imagery of 1967 and multi-spectral RapidEye imagery of 2011 form the basis of this regional scaled study, which is focused on the Dogon Plateau and the Seno Plain in the Sahel zone of Mali. Object-based feature extraction and classifications are used to analyze the datasets and map land cover and woody vegetation changes over 44 years. Interviews add information about changes in species compositions. Results show a significant increase of cultivated land, a reduction of dense natural vegetation as well as an increase of trees on farmer's fields. Mean woody cover decreased in the plains (−4%) but is stable on the plateau (+1%) although stark spatial discrepancies exist. Species decline and encroachment of degraded land are observed. However, the direction of change is not always negative and a variety of spatial variations are shown. Although the impact of climate is obvious, we demonstrate that anthropogenic activities have been the main drivers of change. [ABSTRACT FROM AUTHOR]

• Trends in vegetation and their drivers are studied for all major cities worldwide. • Clearly contrasting trends are observed between cities of different continents. • 70% of all cities show an increasing trend in vegetation during 2000–2018. • 68% of all cities show a stronger increasing trend in their surrounding areas. • Socio-economic and climatic changes mainly control the observed changes globally. Urban greening can enhance quality of life by generating ecosystem services and has been proposed as a way of mitigating adverse consequences of global warming for human health. However, there is limited knowledge on global trends in urban vegetation and their relation to economic development and climate change. Here we studied 1,688 major cities worldwide and show that 70% (1,181) show an increase in vegetation derived from satellite observations (2000–2018). For 68% (1,138) of the cities studied, the increase in the urban vegetation is less strong as compared to the vegetation increase found in the surroundings of these cities. Overall, positive vegetation trends are widely observed in cities in Europe and North America, whereas negative vegetation trends in cities occur primarily in Africa, South America and Asia. Gross Domestic Product growth, population growth as well as temperature are found to be the main underlying drivers of the observed contrasts in changes in urban vegetation as compared to surrounding areas across continents. From a global synthesis of urban vegetation change, we quantify the role of social-economic development and climate change in regulating urban vegetation growth, and the contrasting imprint on cities of developed and developing countries. [ABSTRACT FROM AUTHOR]

Structure-based led optimisation of orally active reversible Methionine Aminopeptidase-2 (MetAP-2) inhibitors utilising a 'molecular budget' medicinal chemistry strategy is described. The key physicochemical parameters of target molecules (cLogP, molecular size and H-bond donor count) were monitored through straightforward and intuitive use of atom count and distribution. The balance between structure-based design and an awareness of the physicochemical properties of the compounds synthesised enabled the rapid identification of a potent molecule with good oral pharmacokinetic (PK) characteristics by making fewer, higher quality compounds. The resulting candidate quality molecule was validated in a mechanistic cellular assay and a rodent secondary immunisation model. [ABSTRACT FROM AUTHOR]

• We quantify large scale afforestation projects impact on forest spatial patterns. • Remote sensing time series is used to analyze the effects of restoration projects. • Clear footprints were observed on the forest landscape of the Loess Plateau. • Core and fragmented forests greatly increased following afforestation projects. • Analysis of spatial pattern dynamics offers a new view on forest ecosystems. The spatial pattern of forests impacts on biodiversity, stability and sustainability of forest ecosystems. Afforestation and reforestation projects have massively increased forested areas on the Chinese Loess Plateau, however, the spatial pattern of the new and old forests, as well as their interaction, remains unknown. Here we study the spatiotemporal dynamics of old and new forests for the period 2001–2016, and found that 84.21% of the old forests existed throughout the study period. Moreover, core forests (defined as a forest area which is surrounded by other forest areas) significantly increased (2585 km2 yr−1, in total 39, 597 km2). Two ecological restoration projects have left clear footprints on the forest landscape of the Loess Plateau: (1) The Natural Forest Conservation Project, aiming at expanding old forest, has resulted in the establishment of considerable areas of new forest surrounding old forest. Consequently, this has promoted new core forest areas to emerge. (2) The Grain for Green Project has mainly caused a fragmented landscape of forest islets which gradually connect to core forest areas. The general increase in core forest areas can be considered an ecological improvement, and the assessment method presented here may guide stakeholders in measuring the success of forest restoration activities that goes beyond a classical quantification of forest cover. [ABSTRACT FROM AUTHOR]

• Missing context features in the retrieval cue cause the revelation effect. • A decrease in false alarm and hit rates for low-frequency materials is predicted. • An increase in false alarm and hit rates for high-frequency materials is predicted. • A formal model has been successfully fitted to a series of three new experiments. Interrupting a sequence of episodic recognition decisions by a problem-solving task will change the hit and false alarm rate for the following item in a recognition test (Watkins & Peynircioglu, 1990). The mechanisms of this revelation effect have not yet been understood completely. We offer a new explanation based on the global matching model MINERVA 2 (Hintzman, 1984, 1986, 1988). The main mechanism in our approach is that the interrupting problem-solving task eliminates some context features in the retrieval cue for the next recognition decision. Assuming a constant decision criterion, this shifts the means of the underlying familiarity distributions and produces a revelation effect. The means of the familiarity distributions decrease for low-frequency stimuli but can shift to more positive values for high-frequency stimuli. We show how this approach explains established empirical findings. We also test new predictions within three experiments. The first two experiments show that the revelation effect disappears if context features are made more available at test. The third experiment confirms the prediction that the revelation effect increases as a function of pre-experimental frequency. Overall, our approach explains findings that have been difficult to explain so far, provides a framework for new predictions, and shows connections to other memory paradigms via the underlying model. [ABSTRACT FROM AUTHOR]

Remote sensing-derived cropland products have depicted the location and extent of agricultural lands with an ever increasing accuracy. However, limited attention has been devoted to distinguishing between actively cropped fields and fallowed fields within agricultural lands, and in particular so in grass fallow systems of semi-arid areas. In the Sahel, one of the largest dryland regions worldwide, crop-fallow rotation practices are widely used for soil fertility regeneration. Yet, little is known about the extent of fallow fields since fallow is not explicitly differentiated within the cropland class in any existing remote sensing-based land use/cover maps, regardless of the spatial scale. With a 10 m spatial resolution and a 5-day revisit frequency, Sentinel-2 satellite imagery made it possible to disentangle agricultural land into cropped and fallow fields, facilitated by Google Earth Engine (GEE) for big data handling. Here we produce the first Sahelian fallow field map at a 10 m resolution for the baseline year 2017, accomplished by designing a remote sensing driven protocol for generating reference data for mapping over large areas. Based on the 2015 Copernicus Dynamic Land Cover map at 100 m resolution, the extent of fallow fields in the cropland class is estimated to be 63% (403,617 km2) for the Sahel in 2017. Similar results are obtained for five contemporary cropland products, with fallow fields occupying 57–62% of the cropland area. Yet, it is noted that the total estimated area coverage depends on the quality of the different cropland products. The share of cropped fields within the Copernicus cropland area is found to be higher in the arid regions (200–300 mm rainfall) as compared to the semi-arid regions (300–600 mm rainfall). The woody cover fraction within cropped and fallow fields is found to have a reversed pattern between arid (higher woody cover in cropped fields) and semi-arid (higher woody cover in fallow fields) regions. The method developed, using cloud-based Earth Observation (EO) data and computation on the GEE platform, is expected to be reproducible for mapping the extent of fallow fields across global croplands. Future applications based on multi-year time series is expected to improve our understanding of crop-fallow rotation dynamics in grass fallow systems being key in teasing apart how cropland intensification and expansion affect environmental variables, such as soil fertility, crop yields and local livelihoods in low-income regions such as the Sahel. The mapping result can be visualized via a web viewer (https://buwuyou.users.earthengine.app/view/fallowinsahel). • Mapping of the land use class "fallow field" was developed. • An RS-based approach for creating large-scale reference data was designed. • A Sahel-scale crop/fallow field map was produced at a 10 m resolution for 2017. • Fallow fields, remarkably occupy 60 ± 3% of Sahelian cropland areas. • Shares of crop/fallow fields were analyzed using rainfall and woody cover. [ABSTRACT FROM AUTHOR]

Woody vegetation is a central component of savanna ecosystems providing ecosystem services for local livelihoods. Accurate monitoring of woody vegetation in savannas is therefore desirable, yet large scale mapping approaches rely on relatively coarse spatial resolution satellite data, which cannot directly capture the scattered nature of savanna trees. Studies at regional scale thus estimate the fractional cover of woody plants for a given area, whereas binary tree/no-tree estimates are restricted to the use of very high-resolution (VHR) images at local scales. With the launch of the Sentinel satellite systems (Sentinel-1 and Sentinel-2), the spatial resolution of images approaches the size of medium/large tree crowns, providing the opportunity to map the presence/absence of tree canopies, rather than the fraction of woody cover or forested areas. Here, we used a support vector machine (SVM) to classify the presence/absence of woody canopies from Sentinel-1 and Sentinel-2 data at a 10-m spatial resolution for the entire African Sahel. Training samples for the SVM classifier were collected from VHR images provided by Google Earth and Sentinel satellite data were processed in Google Earth Engine. Accuracy assessment was performed based on independent VHR images, showing an overall accuracy of 93% (71% and 98% for producer's accuracy of woody and non-woody pixels, 91% and 93% for user's accuracy of woody and non-woody pixels) when combining Sentinel-1 and Sentinel-2 data (overall accuracy of 89% using Sentinel-1 only and 91% using Sentinel-2 only). The combined use proved to perform significantly better (p < 0.05) than the single use of any of the two. A comparison with existing tree cover maps (by aggregating presence/absence of tree canopies into fractional cover) showed noticeable differences, reflecting the need for new woody cover products adapted to the nature of savanna ecosystems. The Sentinel woody canopy map was able to reproduce the general pattern of scattered woody canopies, but generally overestimated the woody coverage (11.37 ± 26.13% (mean ± sd) when aggregating to 250 m resolution) due to the 10 × 10-m spatial resolution which exceeds the crown size of a typical savanna tree. The cloud-based Sentinel-1 and Sentinel-2 analysis presented is a step towards large scale mapping of woody canopy (tree/no-tree) in savannas. Ultimately, such direct assessment of woody canopy areas will allow monitoring of temporal dynamics of woody canopies in future studies as Sentinel time-series expands to multiple years. • Mapping of woody vegetation in savannas was advanced using Copernicus data in GEE. • Woody canopies were mapped superseding conventional fractional cover estimates. • Mapping was done by Sentinel-1 & −2 temporal signatures (10-m resolution) using SVM. • A map of Sahel showed an overall accuracy of 93%, but overestimated woody coverage. • A comparison with existing tree cover maps showed the added value of this approach. [ABSTRACT FROM AUTHOR]

Sacred forests are increasingly disappearing due to increasing land pressure and a decline in cultural values. Protecting the remaining sacred forests plays a crucial role in preserving biodiversity. The existence of remaining old forests often related to local people and their culture, but this relationship has rarely been quantified at large regional scales. This study analyzes the relationship between old forest and ethnic minorities based on the location of the old forest at a high spatial resolution (2 m). We found a significantly positive correlation (p < 0.01) between the proportion of ethnic minority population and old forest patch number, area, aggregation, and maximum patch area. However, there was no correlation with the connectivity of the old forest (p = 0.14). We further show that both environmental and anthropogenic factors are important for the distribution of old forests. Hydrothermal conditions contribute to the growth of forests, and local ethnic customs and the corresponding ecological wisdom contributes to the preservation of old forest remnants (r = 0.12, p < 0.05). Our findings highlight the significance of social dimensions for the conservation of old forests. We encourage that forest management should consider the role of indigenous people and their cultural wisdom for a better conservation and restoration of degraded ecosystems. • We find a positive correlation between the presence of old forests and the distribution of ethnic minorities. • Hydrothermal conditions contribute to the growth of forests, and traditional knowledge contributes to the preservation of old forests. • The ecological wisdom of local people can improve the conservation of old trees. [ABSTRACT FROM AUTHOR]

The sahelian rainfall regime is characterized by a strong spatial as well as intra- and inter-annual variability. The satellite based African Rainfall Climatology Version 2 (ARC2) daily gridded rainfall estimates with a 0.1° × 0.1° spatial resolution provides the possibility for in-depth studies of seasonal changes over a 33-year period (1983–2015). Here we analyze rainfall regime variables that require daily observations: onset, cessation, and length of the wet season; seasonal rainfall amount; number of rainy days; intensity and frequency of rainfall events; number, length, and cumulative duration of dry spells. Rain gauge stations and MSWEP (Multi-Source Weighted-Ensemble Precipitation) data were used to evaluate the agreement of rainfall variables in both space and time, and trends were analyzed. Overall, ARC2 rainfall variables reliably show the spatio-temporal dynamics of seasonal rainfall over 33 years when compared to gauge and MSWEP data. However, a higher frequency of low rainfall events (<10 mm day −1 ) is found for satellite estimates as compared to gauge data, which also causes disagreements between satellite and gauge based variables due to sensitivity to the number of days with observations (frequency, intensity, and dry spell characteristics). Most rainfall variables (both ARC2 and gauge data) show negative anomalies (except for onset of rainy season) from 1983 until the end of the 1990s, from which anomalies become mostly positive and inter-annual variability is higher. ARC2 data show a strong increase in seasonal rainfall, wet season length (caused by both earlier onset and a late end), number of rainy days, and high rainfall events (>20 mm day −1 ) for the western/central Sahel over the period of analysis, whereas the opposite trend characterizes the eastern part of the Sahel. [ABSTRACT FROM AUTHOR]

Human-transformed agricultural and aquacultural ponds, collectively referred to as "dike-pond systems", play a crucial role in ensuring food security but also contribute to the widespread loss of natural wetlands. However, spatially and temporally explicit patterns of dike-pond systems have not been thoroughly documented globally. Here, we map the distribution of dike-pond systems over both global inland and coastal regions during three periods (1984 to 2000, 2001 to 2010, 2011 to 2020) using 30-m resolution satellite observations. Results show that the total area of dike-pond systems was 132,886 km2 globally, with about 2/3 contributed from inland wetlands. From 1984 to 2020, the net gain of inland dike-pond systems (26,385 km2 or 92.7%) was 1.6 times that of coastal wetlands (16,371 km2 or 83.3%), due primarily to reduced coastal dike-pond systems in the last decade. We identified significant contributions of dike-pond systems to fishery production, and further revealed occurrences of dike-pond systems within protected areas. Our study highlights the importance of prioritizing the conservation and management of more intensively inland dike-pond systems, and our global synthesis provides a critical benchmark for assessing potential consequences of dike-pond system and for future restoration efforts. • From 1984 to 2020, about 132,886 km2 of land was transformed into dike-pond systems. • The rise of inland dike-pond systems outpaced that of coastal ones from 1984 to 2020. • 6% of inland and 14% of coastal dike-pond systems are within protected areas recently. [ABSTRACT FROM AUTHOR]

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery 0.6 m from the USDA-NAIP program. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with corresponding RGB-NIR NAIP images collected in 2020. We evaluated the performance of the deep-learning model using 42 independent 1 km 2 areas across various forest types and landscape variations in California. Our predictions of tree heights exhibited a mean error of 2.9 m and showed relatively low systematic bias across the entire range of tree heights present in California. In 2020, trees taller than 5 m covered ∼ 19.3% of California. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models. The approach we used allowed for the reconstruction of the three-dimensional structure of individual trees as observed from nadir-looking optical airborne imagery, suggesting a relatively robust estimation and mapping capability, even in the presence of image distortion. These findings demonstrate the potential of large-scale mapping and monitoring of tree height, as well as potential biomass estimation, using NAIP imagery. • Sub-meter California tree canopy height map (mean average error of 2.9 m) • Tree height predicted with LiDAR-Informed U-Net and sub-meter (0.6 m) aerial images. • Validated with canopy height models from LiDAR in 42 sites of 1 × 1 km. • 3D tree's structure recovery from a nadir view independent of sensor view-angle • Reproducible in countries with aerial VHR image national coverage and LiDAR flights [ABSTRACT FROM AUTHOR]

Vegetation dynamics in the African continent play an important role in the global terrestrial carbon cycle. Above-ground biomass carbon (AGC) stocks in Africa are sensitive to drought, fires and anthropogenic disturbances, and can be increased from forest restoration and tree plantation. However, there are large uncertainties in estimating changes that have occurred in AGC stocks in Africa over the past decades. Here, we used a microwave remote sensing-based vegetation index named Vegetation Optical Depth produced from X-band observations by INRAE Bordeaux (IB X-VOD) to describe the AGC dynamics in Africa covering recent decades. From 2003 to 2021, African AGC showed a net increase at a rate of +0.06 [+0.04, +0.07] PgC·yr−1 (the range represents the minimum and maximum AGC changes estimated by four calibrations), resulting from a large carbon gain of +0.55 [+0.46, +0.60] PgC·yr−1 during the first decade of the twenty-first century (period 1: 2003–2010) and a much weaker increase of +0.05 [+0.04, +0.07] PgC·yr−1 over the recent decade (period 2: 2013–2021). AGC gains were mainly found in non-forest woody areas, which contributed the most to the AGC changes during 2003–2021. Rainforests showed a minor AGC loss of −0.02 [−0.03, −0.02] PgC·yr−1, which emphasizes the need for forest conservation in Africa. Relationships between the AGC changes and potential forcing climate or anthropogenic variables suggested that human-induced deforestation and water stress (especially the vapor pressure deficit (VPD)) are the most important variables explaining the spatial and temporal AGC variations, respectively. For areas of rainforests, we identified a strong relationship between AGC and VPD (negative), soil moisture (positive) and radiation (positive). For areas of sparse vegetation (mainly located in drylands), AGC changes are largely dominated by changes in the soil water conditions. This study presents a new dataset for monitoring AGC dynamics at a continental scale over recent decades being independent of optical observations, quantifying the impacts of anthropogenic pressure and water stress on aboveground biomass carbon changes. • IB X-VOD was used to investigate AGC dynamics in Africa. • Africa experienced an overall AGC increase from 2003 to 2021. • Woodlands showed a pronounced AGC gain, while rainforests showed a minor AGC loss. • Human disturbances & water stress drove AGC changes in space and time, respectively. [ABSTRACT FROM AUTHOR]

Changes in vegetation phenology are among the most sensitive biological responses to global change. While land surface phenological changes in the Northern Hemisphere have been extensively studied from the widely used long-term AVHRR (Advanced Very High Resolution Radiometer) data, current knowledge on land surface phenological trends and the associated drivers remains uncertain for the tropics. This uncertainty is partly due to the well-known challenges of applying satellite-derived vegetation indices from the optical domain in areas prone to frequent cloud cover. The long-term vegetation optical depth (VOD) product from satellite passive microwaves features less sensitivity to atmospheric perturbations and measures different vegetation traits and functioning as compared to optical sensors. VOD thereby provides an independent and complementary data source for studying land surface phenology and here we performed a combined analysis of the VOD and AVHRR NDVI (Normalized Difference Vegetation Index) datasets for the dry tropics (25°N to 25°S) during 1992–2012. We find a general delay in the VOD derived start of season (SOS) and end of season (EOS) as compared to NDVI derived metrics, however with clear differences among land cover and continents. Pixels characterized by significant phenological trends (P < 0.05) account for up to 20% of the study area for each phenological metric of NDVI and VOD, with large spatial difference between the two sensor systems. About 50% of the pixels studied show significant phenological changes in either VOD or NDVI metrics. Drivers of phenological changes were assessed for pixels of high agreement between VOD and NDVI phenological metrics (serving as a means of reducing noise-related uncertainty). We find rainfall variability and woody vegetation change to be the main forcing variables of phenological trends for most of the dry tropical biomes, while fire events and land cover change are recognized as second-order drivers. Taken together, our study provides new insights on land surface phenological changes and the associated drivers in the dry tropics, as based on the complementary long-term data sources of VOD and NDVI, sensitive to changes in vegetation water content and greenness, respectively. • Trends of land surface phenology are analyzed based on VOD and AVHRR NDVI. • VOD and NDVI metrics show different trends across the dry tropics. • Around 50% of the dry tropics show significant phenological trends. • Potential drivers of the observed phenological trends are examined. [ABSTRACT FROM AUTHOR]

Tropical rainforests from the Brazilian Amazon are frequently degraded by logging, fire, edge effects and minor unpaved roads. However, mapping the extent of degradation remains challenging because of the lack of frequent high-spatial resolution satellite observations, occlusion of understory disturbances, quick recovery of leafy vegetation, and limitations of conventional reflectance-based remote sensing techniques. Here, we introduce a new approach to map forest degradation caused by logging, fire, and road construction based on deep learning (DL), henceforth called DL-DEGRAD, using very high spatial (4.77 m) and bi-annual to monthly temporal resolution of the Planet NICFI imagery. We applied DL-DEGRAD model over forests of the state of Mato Grosso in Brazil to map forest degradation with attributions from 2016 to 2021 at six-month intervals. A total of 73,744 images (256 × 256 pixels in size) were visually interpreted and manually labeled with three semantic classes (logging, fire, and roads) to train/validate a U-Net model. We predicted the three classes over the study area for all dates, producing accumulated degradation maps biannually. Estimates of accuracy and areas of degradation were performed using a probability design-based stratified random sampling approach (n = 2678 samples) and compared it with existing operational data products at the state level. DL-DEGRAD performed significantly better than all other data products in mapping logging activities (F 1 -score = 68.9) and forest fire (F 1 -score = 75.6) when compared with the Brazil's national maps (SIMEX, DETER, MapBiomas Fire) and global products (UMD-GFC, TMF, FireCCI, FireGFL, GABAM, MCD64). Pixel-based spatial comparison of degradation areas showed the highest agreement with DETER and SIMEX as Brazil official data products derived from visual interpretation of Landsat imagery. The U-Net model applied to NICFI data performed as closely to a trained human delineation of logged and burned forests, suggesting the methodology can readily scale up the mapping and monitoring of degraded forests at national to regional scales. Over the state of Mato Grosso, the combined effects of logging and fire are degrading the remaining intact forests at an average rate of 8443 km2 year−1 from 2017 to 2021. In 2020, a record degradation area of 13,294 km2 was estimated from DL-DEGRAD, which was two times the areas of deforestation. • Mapping forest degradation is a major gap for carbon emissions in tropical forests. • Here we train a deep learning model using Planet NICFI data to map degradation. • The U-Net model segments degraded forests due to logging, fire, and roads. • Our maps show the highest overlap with official Brazilian data from DETER and SIMEX. • This novel approach provides mapping and attribution of tropical forest degradation. [ABSTRACT FROM AUTHOR]

Abstract We present a method for remote sensing based monitoring of changes in dryland ecosystem functioning based on the assumption that an altered vegetation rainfall relationship (VRR) indicates changes in vegetation biophysical processes, potentially leading to changes in ecosystem functioning. We describe the VRR through a linear regression between integrated rainfall and vegetation productivity (using NDVI as a proxy) within a combined spatio-temporal window, sequentially moved over the study area and along the temporal axis of a time series. The trend in the slope values derived from such a sequential linear regression, termed SeRGS, thus represents a measure of change in the VRR. Scenarios of land degradation, defined here as a reduction in biological productivity, which may be caused by either climatic or anthropogenic factors are simulated for the period 1970–2016 from CRU rainfall and modelled NDVI data to test and evaluate the performance of the SeRGS method in detecting degradation, and compare it against the well-known RESTREND method. We found that SeRGS showed (1) overall more pronounced trends and higher significance levels (p ≤ 0.01) in detecting degradation events and (2) an improved statistical basis for the calculation of trends in the VRR (expressed by high coefficients of determination throughout the period of analysis), which was found to increase the validity of the results produced. Through the implementation of the temporal moving window the effect of inter-annual rainfall variability on vegetation productivity was effectively reduced, thereby enabling a more exact and reliable identification of the timing of degradation events (e.g. start, maximum and end of degradation) by using a time series breakpoint analysis (BFAST). Finally, the SeRGS method was applied using real data for Senegal (seasonally integrated MODIS NDVI and CHIRPS rainfall data 2000–2016) and we discuss patterns and trends. This study provides the theoretical basis for an improved assessment of changes in dryland ecosystem functioning, which is of relevance to land degradation monitoring targeting loss of vegetation productivity. Highlights • An improved method for monitoring changes in dryland ecosystem functioning • Interpretation of vegetation/rainfall sequential linear regression slopes (SeRGS) • Performance evaluation from simulated land degradation (vegetation loss) scenarios • SeRGS facilitates accurate breakpoint detection of ecosystem functioning changes. • SeRGS showcase for Senegal and discussion on operability across time/space/sensors [ABSTRACT FROM AUTHOR]

Within the past decades, the karst region in the Guangxi province in southwest China has been in the focus of large scale ecological restoration projects. In this study, we adapt the pressure-state-response (PSR) framework (which includes measures for human pressure, the current state of the ecosystem and the human response) and propose a remote sensing based ecosystem health (ESH) index (0–1; 250 m spatial resolution), evaluating the pressure and state of the fragile karst ecosystem. We further apply inventory data of recovery actions (restoration areas at county level resolution) as response to ongoing degradation to test the impact of ecological restoration on the ESH. Our analysis was conducted for the years 2000, 2010 and 2016, and the results showed that 73% of the study area experienced an increase in the ESH (from 2000 to 2016) which was related to the improvements in vegetation vigor, organization, resilience, ecosystem service provisioning which offset a deterioration in fragmentation and population density. From 2000 to 2016, areas of increase in ESH were slightly larger in karst than in non-karst (37.5% and 35.1%, respectively), but also larger areas of decrease in ESH were observed in karst as compared to non-karst (16% and 11%, respectively). The results further showed that the share of areas with a high ESH (greater than 0.7) had increased by ∼3% (from 67.16% to 70.21%) during the 17 years period. At county level, we found a clear relationship between increases in ESH and ecological restoration areas ( r  = 0.58 , p  = 0.004), with a high recovery rate (ratio between areas with increased ESH and restoration areas in a county) in karst than in non-karst. We conclude that restoration projects have caused a large scale transformation of farmland into forested areas (∼5500 km 2 ), which has caused a general improvement in ecosystem parameters related to ESH. [ABSTRACT FROM AUTHOR]

Vegetation optical depth (VOD) from satellite passive microwave sensors has enabled monitoring of aboveground biomass carbon dynamics by building a relationship with static carbon maps over space and then applying this relationship to VOD time series. However, uncertainty in this relationship arises from changes in water stress, as VOD is mainly determined by vegetation water content, which varies at diurnal to interannual scales, and depends on changes in both biomass and relative moisture content. Here, we studied the reliability of using VOD from various microwave frequencies and temporal aggregation methods for estimating decadal biomass carbon dynamics at the global scale. We used the VOD diurnal variations to represent the magnitude of vegetation water content buffering caused by climatic variations for a constant amount of dry biomass carbon. This magnitude of VOD diurnal variations was then used to evaluate the likelihood of VOD decadal variations in reflecting decadal dry biomass carbon changes. We found that SMOS-IC L-VOD and LPDR X-VOD can be reliably used to estimate decadal carbon dynamics for 76.7% and 69.9% of the global vegetated land surface, respectively, yet cautious use is warranted for some areas such as the eastern Amazon rainforest. Moreover, the annual VOD aggregated from the 95% percentile of the nighttime VOD retrievals was proved to be the most suitable parameter for estimating decadal biomass carbon dynamics among the temporal aggregation methods. Finally, we validated the use of annual VOD for estimating interannual carbon dynamics by comparing VOD changes between adjacent years against eddy covariance estimations of gross primary production from flux sites over several land cover classes across the globe. Despite the large difference in spatial scales between them, the positive correlation obtained supports the capability of satellite VOD in quantifying interannual carbon dynamics. • A new method to evaluate VOD reliability for estimating decadal carbon dynamics. • Comparing various approaches for obtaining annual VOD from daily observations. • Development of global VOD reliability maps for estimating decadal carbon dynamics. • Evaluation of VOD-based carbon interannual dynamics using eddy covariance measurements. [ABSTRACT FROM AUTHOR]

Australia experienced multi-year drought and record high temperatures, and massive forest fires occurred across the southeast in 2019 and early 2020. In the fire-affected forest areas, understory and often tree canopies were burned, and in-situ observations in late 2020 reported rapid vegetation recovery, including grasses, shrubs, and tree canopies from burned-but-not-dead eucalyptus trees. Considering the strong fire resilience and resistance of eucalyptus trees and above-average rainfall in 2020, we assessed how much and how quickly vegetation structure and biomass changed from loss to post-fire and drought recovery in 2020 for all forest areas in Australia. Here, we analyzed space-borne optical, thermal, and microwave images to assess changes in the structure and function of vegetation using four vegetation indices (VIs), leaf area index (LAI), solar-induced chlorophyll fluorescence (SIF), gross primary production (GPP), and aboveground biomass (AGB). We found that all eight variables show large losses in 2019, driven by fires and climate (drought and high temperature), but large gains in 2020, resulting from the high resilience of most trees to fire and rapid growth of understory vegetation under wet condition in 2020. In 2019, the forest area has an AGB loss of 0.20 Pg C, which is ~15% of the pre-fire AGB. Attribution analyses showed that both fire and climate (prior and co-occurring severe drought and record high temperatures) are responsible for the AGB loss in 2019, approximately 0.09 Pg C (fire) and 0.11 Pg C (climate), respectively. In 2020, the forest area has a total AGB gain of 0.26 Pg C, composed of 0.22 Pg C from fire-affected forest area and 0.04 Pg C from fire-unaffected forest area. Fire-adapted Eucalyptus forests and above-average annual precipitation in 2020 brought by a moderate La Niña drove the recovery of vegetation cover, productivity, and AGB. The results from this study shows the potential of multiple sensors for monitoring and assessing the impacts of fire and climate on the forest areas in Australia and their post-fire recovery. • Assess forest canopy, productivity, and aboveground biomass changes in Australia. • Optical, thermal, and microwave satellite datasets were used in analyses together. • Large loss in forest canopy, productivity and biomass due to fire and climate. • Fast recovery in forest canopy, productivity and aboveground biomass in 2020. • Fire and climate have similar contribution to forest aboveground biomass loss. [ABSTRACT FROM AUTHOR]

Abstract: 1-(1,3,5-Triazin-yl)piperidine-4-carboxamide inhibitors of soluble epoxide hydrolase were identified from high through-put screening using encoded library technology. The triazine heterocycle proved to be a critical functional group, essential for high potency and P450 selectivity. Phenyl group substitution was important for reducing clearance, and establishing good oral exposure. Based on this lead optimization work, 1-[4-methyl-6-(methylamino)-1,3,5-triazin-2-yl]-N-{[[4-bromo-2-(trifluoromethoxy)]-phenyl]methyl}-4-piperidinecarboxamide (27) was identified as a useful tool compound for in vivo investigation. Robust effects on a serum biomarker, 9, 10-epoxyoctadec-12(Z)-enoic acid (the epoxide derived from linoleic acid) were observed, which provided evidence of robust in vivo target engagement and the suitability of 27 as a tool compound for study in various disease models. [Copyright &y& Elsevier]

The Tibetan Plateau is the highest and largest plateau in the world, hosting unique alpine grassland and having a much higher snow cover than any other region at the same latitude, thus representing a "climate change hot-spot". Land surface phenology characterizes the timing of vegetation seasonality at the per-pixel level using remote sensing systems. The impact of seasonal snow cover variations on land surface phenology has drawn much attention; however, there is still no consensus on how the remote sensing estimated start of season (SOS) is biased by the presence of preseason snow cover. Here, we analyzed SOS assessments from time series of satellite derived vegetation indices and solar-induced chlorophyll fluorescence (SIF) during 2003–2016 for the Tibetan Plateau. We evaluated satellite-based SOS with field observations and gross primary production (GPP) from eddy covariance for both snow-free and snow covered sites. SOS derived from SIF was highly correlated with field data (R2 = 0.83) and also the normalized difference phenology index (NDPI) performed well for both snow free (R2 = 0.77) and snow covered sites (R2 = 0.73). On the contrary, normalized difference vegetation index (NDVI) correlates only weakly with field data (R2 = 0.35 for snow free and R2 = 0.15 for snow covered sites). We further found that an earlier end of the snow season caused an earlier estimate of SOS for the Tibetan Plateau from NDVI as compared to NDPI. Our research therefore adds new evidence to the ongoing debate supporting the view that the claimed advance in land surface SOS over the Tibetan Plateau is an artifact from snow cover changes. These findings improve our understanding of the impact of snow on land surface phenology in alpine ecosystems, which can further improve remote sensing based land surface phenology assessments in snow-influenced ecosystems. Unlabelled Image • Four satellite indicators are tested to estimate spring phenology trends over the Tibetan Plateau. • The confounding effect of snow cover is evaluated. • The Normalized Difference Phenology Index provides best results for snow conditions. • Normalized Difference Vegetation Index SOS trends are biased by snow trends. [ABSTRACT FROM AUTHOR]

The vegetation optical depth (VOD), a vegetation index retrieved from passive or active microwave remote sensing systems, is related to the intensity of microwave extinction effects within the vegetation canopy layer. This index is only marginally impacted by effects from atmosphere, clouds and sun illumination, and thus increasingly used for ecological applications at large scales. Newly released VOD products show different abilities in monitoring vegetation features, depending on the algorithm used and the satellite frequency. VOD is increasingly sensitive to the upper vegetation layer as the frequency increases (from L-, C- to X-band), offering different capacities to monitor seasonal changes of the leafy and/or woody vegetation components, vegetation water status and aboveground biomass. This study evaluated nine recently developed/reprocessed VOD products from the AMSR2, SMOS and SMAP space-borne instruments for monitoring structural vegetation features related to phenology, height and aboveground biomass. For monitoring the seasonality of green vegetation (herbaceous and woody foliage), we found that X-VOD products, particularly from the LPDR-retrieval algorithm, outperformed the other VOD products in regions that are not densely vegetated, where they showed higher temporal correlation values with optical vegetation indices (VIs). However, LPDR X-VOD time series failed to detect changes in VOD after rainfall events whereas most other VOD products could do so, and overall daily variations are less pronounced in LPDR X-VOD. Results show that the reprocessed VODCA C- and X-VOD have almost comparable performance and VODCA C-VOD correlates better with VIs than other C-VOD products. Low frequency L-VOD, particularly the new version (V2) of SMOS-IC, show a higher temporal correlation with VIs, similar to C-VOD, in medium-densely vegetated biomes such as savannas (R ~ 0.70) than for other short vegetation types. Because the L-VOD indices are more sensitive to the non-green vegetation components (trunks and branches) than higher frequency products, they are well-correlated with aboveground biomass: (R ~ 0.91) across space between predicted and observed values for both SMOS-IC V2 and SMAP MT-DCA. However, when compared with forest canopy height, results at L-band are not systematically better than C- and X-VOD products. This revealed specific VOD retrieval issues for some ecosystems, e.g., boreal regions. It is expected that these findings can contribute to algorithm refinements, product enhancements and further developing the use of VOD for monitoring above-ground vegetation biomass, vegetation dynamics and phenology. • The performance of nine VOD products at X-, C- and L-band are inter-compared. • X-VOD, particularly LPDR, performs best for monitoring short vegetation seasonality. • L-VOD, particularly SMOS-IC V2 and SMAP MT-DCA, performs best for predicting biomass. • Multiple-frequency VODs have complementary capabilities in monitoring vegetation. [ABSTRACT FROM AUTHOR]