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Water scarcity and the difference between water supply and consumption is an increasing concern particularly under climatic uncertainty. Costa Rica has historically been at the forefront of environmental legislation; however, the water use framework in Costa Rica, still utilizes a quota style water allocation, which is problematic in extreme drought conditions. This allocation system paired with climate volatility makes it essential to quantify the future water demands compared to the water supply in Costa Rica. We use the Integrated Valuation of Ecosystem Services and Tradeoffs modeling system to quantify the water availability and usage in watersheds in Costa Rica under different climate and land cover change scenarios. We subsequently assess the spatial variability of water shortage and stress across Costa Rica. We find that there is a high spatial difference in water availability which do not match high use basins. In the highest demand areas, over 70 % of the water supply is projected to be consumed under a business-as-usual land cover change and high emission climate scenario. Low emission climate scenarios result in higher water availability, and increased forest regrowth increases water availability. Overall, the Guanacaste Province and the central valley which includes the capital city of San Jose are most at risk for experiencing high or moderate water shortage and stress in the future. Given the risks associated with water shortage and scarcity, it is important to address governance and stakeholder concerns as we move into the future. • There is a mismatch between areas of Costa Rican water supply and water demand. • Water stress and shortage are highest near the capital city of San Jose and on the Pacific side of Costa Rica. • Water usage highly related to urban and agricultural presence in the watershed. • Incentive structures could improve crop and water matching to reduce water stress. • Drought and flood management strategies should be implemented at a regional level. [ABSTRACT FROM AUTHOR]

Despite their ecological and socioeconomic importance, the forested areas of tropical dry ecosystems remain among the most threatened biomes worldwide. There has been limited research on the current extent, fragmentation, and conservation status of tropical dry forests on a global scale. In this study, we used the GLAD UMD Forest Height dataset to provide a high-resolution global assessment of the extent, patch size distribution, and protection levels of Tropical and Subtropical Dry Broadleaf Forests (TBDF) and forested areas of the Tropical and Subtropical Grasslands, Savannas, and Shrublands (TSGSS) by ecoregion and country. We find that despite increasing protection, degradation continues to rise with less than ⅓ of the original TBDF and TSGSS currently forested and over 80 % of those forests currently exposed to edge effects. <1 % of the remaining forest patches are large (>10 km2), and <25 % of the remaining forests are located in conservation or protection zones (TBDF:16 %; TSGSS:21 %), with a heavy protection bias in S.E. Asia. These results indicate high levels of degradation and low conservation leading to potentially severe ecological consequences. Systematic and coordinated efforts are needed to reduce the pressure on this ecosystem and ensure the ongoing provisioning of biodiversity and ecosystem services in these ecoregions. • Less than ⅓ of the original tropical dry forest extent is currently forested. • The remaining tropical dry forest is severely degraded with 20 % in core forest areas. • Less than ⅕ of current forest areas are protected at regional and national levels. • Of protected areas, <10 % are managed by indigenous communities. • There is a geographic mismatch of remaining core forests & highly protected areas. [ABSTRACT FROM AUTHOR]

The region connecting Edmonton and Calgary, the two largest cities in Alberta, contains rich agricultural land and is one of the most rapidly changing areas in the province. There is little legislation to restrict urban sprawl or adequately protect agricultural land or native grasslands, and there has been little research to predict future alteration. The main study objectives are, therefore, to assess historical changes in the Edmonton-Calgary corridor from 1984 to 2013 and simulate the future landscape change to 2022 under potential government intervention scenarios. Satellite imagery from Landsat, used in conjunction with biogeophysical variables, was used to create a history of cover in the Edmonton-Calgary area. This history of the environment can be used as a baseline to project changes into the future. Testing different legislative scenarios under two major branches of modifying rates of change or locations of change can be used to identify effective policies for limiting damage to the environment while still allowing for urban growth. Five scenarios were created for this purpose: (1) business as usual, (2) increased rate of urban expansion, (3) no urban expansion, (4) implementation of greenbelts around urban areas, (5) protection of the best agricultural land. This study finds that over the past 30 years, urban area has nearly doubled in size, targeting predominately farmland, especially due to an increase in rural subdivisions. Each scenario impacts growth differently, however, greenbelts and the no expansion model decease growth the most, while the agricultural protection is comparable to the business as usual scenario. [ABSTRACT FROM AUTHOR]

Rapid acquisition and mapping of normalized difference vegetation index (NDVI) values is essential for a timely evaluation of nitrogen use efficiency (NUE) in sustainable agriculture. The zonal nutrient management techniques for sustainable agriculture require timely and efficient NDVI data collection and analysis capabilities, – a need that is still rarely addressed. However, as with the use of manned aircraft, the primary limitation of established unmanned aerial vehicle (UAV) imaging technologies is the limited time window for large-area data collection. Furthermore, as the spatial resolution increases, the post-processing time generally becomes longer. To overcome problems associated with the need for rapid data acquisition and post-processing, we have developed a UAV-based sensor system called the Accurate and Speed Scanner (AS-Scanner). The data acquisition accuracy and NDVI mapping speed of the system were assessed. According to the results of a ground-level experiment, the mean absolute error (MAE) between the reflectance data of the AS-Scanner and the corresponding data of the ASD FieldSpec 3 spectrometer is less than 1.11%. Furthermore, results of a plot-level experiment suggest that the NDVI values of a raw point cloud from AS-Scanner -based UAV platform (AS-NDVI) and UAV-mounted MicaSense RedEdge camera (REG-NDVI) are highly correlated (0.54 < R2 < 0.57, p < 0.01). In a map-level experiment, the interpolated, 15 m × 15 m pixel size AS-NDVI values were in good agreement with the REG-NDVIs over a 4.01 ha (40100 m2) rice field (0.55 < R2 < 0.65, p < 0.01). The average processing times of the inverse distance weighted (IDW, 0.45 s), kriging linear (KL, 0.73 s), and nearest neighbor (NN, 1.05 s) interpolations of the AS-Scanner data prove that it is possible to efficiently create NDVI maps using data from the sensor. Besides, the effect of the varying solar irradiance values on the AS-NDVIs was analyzed under varied illumination conditions. Moreover, the AS-NDVIs were also used to assess the NUE in a paddy rice field at the panicle stage. The results show that nutrients can be effectively assessed using AS-Scanner data. Despite some disadvantages such as the potential of NDVI value saturation over dense canopies, the AS-Scanner provides a method for quickly obtaining reasonable spatial resolution crop information for agricultural nutrient monitoring and management. This also offers the potential to reduce environmental pressure which may be caused by untimely estimation of NUE. Image 1 • A UAV system with practicality for rapid acquisition and mapping of NDVI was designed. • The reflectance precision of the present system was similar to ASD spectrometer. • The NDVI results are highly correlated to those of the RedEdge camera (R2 > 0.54). • NDVI mapping time was shortened by using the present UAV system. • Beneficial to the nutrient zonal management technology of precision agriculture. [ABSTRACT FROM AUTHOR]

Abstract Tracking the occurrence of deforestation events is an essential task in tropical dry forest (TDF) conservation efforts. Ideally, deforestation monitoring systems would identify a TDF clearing with near real time precision and high spatial detail, and alert park managers and environmental practitioners of illegal forest clearings occurring anywhere in a region of interest. Over the past several years there have been significant advances in the design and application of continuous land cover change mapping algorithms with these capabilities, but no studies have implemented such methods over human dominated TDF environments where small–scale deforestation (<5 ha) is widespread and hard to detect with moderate resolution sensors. The general objective for this research was to evaluate the overall accuracy of the BFASTSpatial R Package for detecting and monitoring small-scale deforestation in four sites located in tropical dry forest landscapes of Mexico and Costa Rica using greenness and moisture spectral indices derived from Landsat time series. Results show a high degree of spatial agreement (90%–94%) between the distribution of TDF clearings occurred during the 2013–2016 period (as indicated by VHR imagery interpretation) and BFASTSpatial outputs. NDMI and NBR2 had the best performance than other indices and this is evidenced by the combined overall, user's and producer's accuracies. In particular, NBR2 were the most accurate predictor of deforestation with an overall accuracy of 94.5%. Our results also imply that monitoring sites at an annual basis is feasible using BFASTSpatial and LTS, but that lower confidence should be given to sub-annual products given significant systematic temporal differences between the BFASTSpatial monthly product and reference data. The possibility of including more clear observations at the spatial resolution of Landsat (30-m) or higher will greatly increase the spatial and temporal accuracies of the method. Given its performance, BFASTSpatial can help monitor hotspots of small-scale TDF loss across Central and North America at little or no cost. Users of the method should have a strong knowledge of the local land use and land cover dynamics and the ecophysiology of vegetation types present in the landscape. This local expertise is necessary for interpreting and validating results as well as communicating its output to decision-makers and stakeholders. Highlights • Detecting TDF loss using BFASTSpatial in R can be achieved with >90% overall accuracy. • Landsat-derived NDMI and NBR2 spectral indices resulted in the highest accuracies. • BFASTSpatial package can be adapted to evaluate dry forest loss at local scales. • Temporal inaccuracies can be improved by increasing cloud free observations. [ABSTRACT FROM AUTHOR]

• The temporal response of TDFs to SST anomalies are assessed. • The impacts of El Niño events on TDFs are assessed by MWCA. • Teleconnection, biophysical characteristics, and seasonality influence the response of TDFs on ENSO. Tropical Dry Forests (TDFs) in the Americas are significantly affected by drought related to El Niño Southern Oscillation (ENSO). The analysis builds upon two drought indices: the MODIS-derived Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) and the Sea Surface Temperature (SST) anomaly in the Niño 3.4 region. Temporal correlation analysis and Moving Window Correlation Analysis (MWCA) were used to explore the long-term and short-term responses at multiple sites. Results illustrate Gross Primary Productivity (GPP) at SRNP-EMSS (Santa Rosa National Park Environmental Monitoring Super Site) and PEMS (Parque Estadual da Mata Seca) in the dry season were negatively impacted by the long-term SST anomaly across multiple ENSO phases (warm, neutral, and cold phases), while there was no long-term impact at CCBR (Chamela-Cuixmala Biosphere Reserve) and TVMWR (Tucabaca Valley Municipal Wildlife Reserve). Findings from a short-term perspective, suggest that the GPP at SRNP-EMSS and CCBR were sensitive to the ENSO warm phase. TDFs GPP at PEMS and TVMWR were resistant to El Niño. Our results reveal that stronger El Niño events may lead to severe droughts in more regions across TDFs, but there is no direct relationship between the intensity of an individual El Niño event and the precipitation condition in each study site. Except for their teleconnections, the biophysical characteristics and seasonality can impact the response of ENSO on the GPP. [ABSTRACT FROM AUTHOR]

• Leaf traits vary from conservative to acquisitive along a successional gradient in a tropical dry forest. • Early and intermediate plant species functionally differ from late species;. • Most of the variance in leaf traits occur at the interspecific level;. • Habitat conditions drive the convergent leaf traits at intra- and interspecific levels during community assembling. The present study aimed to determine how plant functional traits change both at the intra- and interspecific levels along three successional stages (early, intermediate and late) in a Brazilian tropical dry forest (TDF). Plants in early-successional plots are expected to exhibit conservative traits to increase photoprotection and water conservation, whereas late successional plants would invest on acquisitive traits. Sampling was conducted in three plots of 50 × 20 m per successional stage, where plant species composition and structural parameters were previously determined for individuals with DBH ≥ 5 cm. From each of three plots, the five tree species with the highest importance values were selected. This resulted in a total of 13 species from seven families selected for this study. We quantified specific leaf area (SLA), leaf slenderness, polyphenols and chlorophyll contents, and leaf lifespan. Based on leaf traits, we detected two major tree functional species groups: the early-intermediate and the late successional groups. Confirming our expectations, the former was positively associated with drought tolerance traits (polyphenols and slenderness) related to conservative resource use; and the latter was positively associated with acquisitive traits (SLA). This pattern was partially corroborated at the intraspecific level for species that occurred in more than one successional stage. We found a weak phylogenetic signal for the functional grouping (only for polyphenols). The analysis of variance structure across scales indicated that a very small proportion (up to 2.8%) of the general variance in leaf traits occurred at intraspecific level suggesting that, compared to interspecific variations, trait differences among individuals due to phenotypic plasticity are low. Although further studies with a larger number of species are needed, our findings suggest a decoupling between functional and structural/species composition regeneration, contradicting several studies on secondary succession where species composition is regarded as the main indicator of the regeneration of ecological functions. [ABSTRACT FROM AUTHOR]

Accurate estimates of Essential Climate Variables (ECV) such as the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are essential for assessing global carbon balances. ESA's Sentinel-2 (S2) mission with its decametric resolution enables to derive fAPAR information at single forest stand. Validation studies on previously existing satellite-derived fAPAR products have found considerable discrepancies, especially in forest ecosystems that exceed relative discrepancies of 10% (i.e. 0.05 for absolute values) set as target accuracy by the Global Climate Observing System (GCOS). This study presents the validation of S2 fAPAR products using direct radiation measurements of 2017 at three different forests, located in Central Europe (mixed-coniferous forest in temperate mid-latitude), North America (boreal-deciduous forest) and Central America (tropical dry forest, TDF). We measured incoming and transmitted PAR every 10 min synchronously using Wireless Sensor Networks (WSN) and calculated a two-flux fAPAR estimate. We validated the S2 fAPAR product with different instantaneous ground fAPAR estimates (e.g. instantaneous fAPAR at 10:00 and 14:00 local solar time) and daily average fAPAR. We considered uncertainties of ground data, i.e. bias related to the presence of colored autumn leaves and influences of solar zenith angle. Overall, we found high discrepancies between the S2 fAPAR product and ground measurements, indicating that the S2 fAPAR product systematically underestimated (negative values for bias in percent) the ground observations. The highest agreement was observed at the boreal-deciduous forest stand with a bias of −13% (R2 = 0.67). The Central American and European sites reported deviations of −20% (R2 = 0.68) and −25% (R2 = 0.26), respectively. At all sites, we found evidence that particularly the influence of colored leaves during the senescence periods lead to bias of the ground data. Further, the choice of temporal fAPAR estimate, i.e. daily average fAPAR or a certain instantaneous fAPAR estimate, lead to partly different results in the correlation analysis with the S2 fAPAR product. However, considering sources of uncertainties of ground data, we emphasize that only the boreal-deciduous site in Canada fulfilled the accuracy requirements set by the GCOS. In contrast to absolute values, we found strong agreement on phenological changes at all three sites. Specifically, the influence of species composition on seasonal variability of fAPAR across the European mixed-coniferous site was well-represented in the S2 fAPAR product. As for the representation of spatial variability, we found highest agreement at the boreal-deciduous forest stand (BIAS = −22%, R2 = 0.93), whereas spatial variability was least represented at the TDF site (BIAS = 125%, R2 = 0.97). We conclude that the S2 fAPAR product has strong capabilities for assessing temporal variability of fAPAR, but due to low accuracy of absolute values currently limited options to feed global production efficiency models and assess global carbon balances. • Sentinel-2 (S2) fAPAR validated in temporal, boreal and tropical forest ecosystems. • High discrepancies of absolute fAPAR values, ranging from 13 to 25% • Considering uncertainty of in-situ fAPAR, GCOS accuracy target reached at boreal site. • Seasonal dynamics due to species composition well-represented by S2 at temperate site. • Accuracy of fAPAR values and spatial variability related to ecosystem complexity. [ABSTRACT FROM AUTHOR]