JERS-1 SAR image mosaics of the North American Boreal forests: seasonal mosaics and natural resource applications
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Boreal Forest mapping synthetic aperture radar↗
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Remote sensing and statistical analysis of the effects of hurricane María on the forests of Puerto Rico
Widely recognized as one of the worst natural disaster in Puerto Rico's history, hurricane María made landfall on September 20, 2017 in southeast Puerto Rico as a high-end category 4 hurricane on the Saffir-Simpson scale causing widespread destruction, fatalities and forest disturbance. This study focused on hurricane María's effect on Puerto Rico's forests as well as the effect of landform and forest characteristics on observed disturbance patterns. In this work, we used Google Earth Engine (GEE) to assess the severity of forest disturbance using a disturbance metric based on Landsat 8 satellite data composites with pre and post-hurricane María. Forest structure, tree phenology characteristics, and landforms were obtained from satellite data products, including digital elevation model and global forest canopy height. Our analyses showed that forest structure, and characteristics such as forest age and forest type affected patterns of forest disturbance. Among forest types, highest disturbance values were found in sierra palm, transitional, and tall cloud forests; seasonal evergreen forests with coconut palm; and mangrove forests. For landforms, greatest disturbance metrics was found at high elevations, steeper slopes, and windward surfaces. As expected, high levels of disturbance were also found close to the hurricane track, with disturbance less severe as hurricane María moved inland. Results demonstrated that forest and landform characteristics accounted for 34% of the variation in spatial forest spectral disturbance patterns. This study demonstrated an informative regional approach, combining remote sensing with statistical analyses to investigate factors that result in variability in hurricane effects on forest ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Climate Warming Alters Nutrient Storage in Seasonally Dry Forests: Insights From a 2,300 m Elevation Gradient
Understanding potential response of forest carbon (C) and nutrient storage to warming is important for climate mitigation policies. Unfortunately, those responses are difficult to predict in seasonally dry forests, in part, because ecosystem processes are highly sensitive to both changes in temperature and precipitation. We investigated how warming might alter stocks of C, nitrogen (N), and phosphorus (P) in vegetation and the entire regolith (soil + weathered bedrock or “saprock”) using a space-for-time substitution along a bioclimatic gradient in the Sierra Nevada, California. The pine-oak and mixed-conifer forests between 1,160–2,015 m elevation have more optimal climates (not too dry or hot) for ecosystem productivity, soil weathering, and cycling of essential elements than the oak savannah (405 m) and subalpine forest (2,700 m). We found decreases in overstory vegetation nutrient stocks with decreasing elevation because of enhanced water limitation and greater occurrence of disturbances. Stocks of C, N, and P in the entire regolith peaked at the pine-oak and mixed-conifer forests across the bioclimatic gradient, driven by thicker regolith profiles and greater nutrient input rates. These observations suggest long-term warming will decrease ecosystem nutrient storage at the warmer, transitional pine-oak zone, but will increase nutrient storage at the colder, subalpine zone. Assuming steady-state conditions, we found the mean residence time of ecosystem C decreased with projected rising air temperatures and increased following a major drought event across the bioclimatic gradient. Our study emphasizes potentially elevation-dependent changes in nutrient storage and C persistence with warming in seasonally dry forests.
54 ENVIRONMENTAL SCIENCES↗
Root Characteristics Vary with Depth Across Four Lowland Seasonal Tropical Forests
Fine roots are key to ecosystem-scale nutrient, carbon (C), and water cycling, yet our understanding of fine root trait variation within and among tropical forests, one of Earth’s most C-rich ecosystems, is limited. We characterized root biomass, morphology, nutrient content, and arbuscular mycorrhizal fungal (AMF) colonization to 1.2 m depths across four distinct lowland Panamanian forests, and related root characteristics to soil C stocks. We hypothesized that: (H1) Fine root characteristics vary consistently with depth across seasonal tropical forests, with deeper roots exhibiting more exploratory traits, such as for deep water acquisition; (H2) fine root characteristics vary among tropical forests mainly in surface soils, where resource availability also varies. Here we found consistent variation with depth across the four forests, including decreased root biomass, root tissue density, and AMF, and increased specific root length. Among the forests, there was variation in some fine root characteristics, including greater surface root biomass and lower SRL in the wettest forest, and smaller fine root diameter in the driest forest. We also found that root characteristics were related to total soil C stocks, which were positively related to root biomass and negatively related to specific root length. These results indicate emergent properties of root variation with depth across tropical forests, and show site-scale variation in surface root characteristics. Future work could explore the flexibility in root characteristics under changing conditions such as drought.
13C NMR↗
Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought
In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.
54 ENVIRONMENTAL SCIENCES↗
Radiocarbon of deep fine roots across four in lowland seasonal tropical forests in Panama
Objectives:Fine roots are key to ecosystem-scale nutrient, carbon (C), and water cycling, but our understanding of fine root traits variation within and among tropical forests, one of Earth’s most C-rich ecosystems, is limited. In 2022 and 2023, we aimed to explore differences in deep root characteristics among four lowland tropical forests in Panama, which vary in fertility and mean annual precipitation. We measured radiocarbon content (fraction modern [FM] and Δ14C) and δ13C of live fine roots at depths greater than 80 cm, up to 120 cm. The goal was to understand how deep root characteristics differ across these sites.Datasets included:The datasets provided include .csv and .xlsx files for radiocarbon content (fraction modern [FM] and Δ14C) and δ13C of live fine roots at depths greater than 80 cm, up to 120 cm collected from late 2017 to 2018 across four different forests. Additionally, a .kml file is provided with coordinates for all 32 plots included in the study across four forests (n = 8 plots per site). This dataset serves as baseline data before a throughfall exclusion experiment, Panama Rainforest Changes with Experimental Drying (PARCHED), was implemented. No special software is needed to open these files.
54 ENVIRONMENTAL SCIENCES↗
Plant functional types broadly describe water use strategies in the Caatinga, a seasonally dry tropical forest in northeast Brazil
In seasonally dry tropical forests, plant functional type can be classified as deciduous low wood density, deciduous high wood density, or evergreen high wood density species. While deciduousness is often associated with drought-avoidance and low wood density is often associated with tissue water storage, the degree to which these functional types may correspond to diverging and unique water use strategies has not been extensively tested. We examined (a) tolerance to water stress, measured by predawn and mid-day leaf water potential; (b) water use efficiency, measured via foliar δ 13 C; and (c) access to soil water, measured via stem water δ 18 O. We found that deciduous low wood density species maintain high leaf water potential and low water use efficiency. Deciduous high wood density species have lower leaf water potential and variable water use efficiency. Both groups rely on shallow soil water. Evergreen high wood density species have low leaf water potential, higher water use efficiency, and access alternative water sources. These findings indicate that deciduous low wood density species are drought avoiders, with a specialized strategy for storing root and stem water. Deciduous high wood density species are moderately drought tolerant, and evergreen high wood density species are the most drought tolerant group. Our results broadly support the plant functional type framework as a way to understand water use strategies, but also highlight species-level differences.
54 ENVIRONMENTAL SCIENCES↗
Increasing plant water stress and decreasing summer streamflow in response to a warmer and wetter climate in seasonally snow-covered forests
Not provided.
Environmental Sciences & Ecology↗
Amazon Rain Forest Classification Using J-ERS-1 SAR Data
The Amazon rain forest is a region of the earth that is undergoing rapid change. Man-made disturbance, such as clear cutting for agriculture or mining, is altering the rain forest ecosystem. For many parts of the rain forest, seasonal changes from the wet to the dry season are also significant. Changes in the seasonal cycle of flooding and draining can cause significant alterations in the forest ecosystem.Because much of the Amazon basin is regularly covered by thick clouds, optical and infrared coverage from the LANDSAT and SPOT satellites is sporadic. Imaging radar offers a much better potential for regular monitoring of changes in this region. In particular, the J-ERS-1 satellite carries an L-band HH SAR system, which via an on-board tape recorder, can collect data from almost anywhere on the globe at any time of year.In this paper, we show how J-ERS-1 radar images can be used to accurately classify different forest types (i.e., forest, hill forest, flooded forest), disturbed areas such as clear cuts and urban areas, and river courses in the Amazon basin. J-ERS-1 data has also shown significant differences between the dry and wet season, indicating a strong potential for monitoring seasonal change. The algorithm used to classify J-ERS-1 data is a standard maximum-likelihood classifier, using the radar image local mean and standard deviation of texture as input. Rivers and clear cuts are detected using edge detection and region-growing algorithms. Since this classifier is intended to operate successfully on data taken over the entire Amazon, several options are available to enable the user to modify the algorithm to suit a particular image.
J-ERS-1↗
Above-ground net primary productivity in regenerating seasonally dry tropical forest: Contributions of rainfall, forest age and soil
Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modelling vegetation dynamics and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forests which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest. Here We examined above-ground net primary productivity (ANPP) and its components (wood production and litterfall) over 10 years in forest plots that vary in successional age, soil characteristics and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica. We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition. These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that detailed understanding of local site variation in soils and plant community may be required to accurately predict the impact of changing rainfall on forest carbon uptake. Synthesis. We show that in seasonally dry tropical forest, annual rainfall has a positive relationship with the growth of above-ground woody tissues of trees and that droughts lead to significant reductions in above-ground productivity. These results provide evidence for climate change—carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.
54 ENVIRONMENTAL SCIENCES↗
Distribution of Aboveground Live Biomass in the Amazon Basin
The amount and spatial distribution of forest biomass in the Amazon basin is a major source of uncertainty in estimating the flux of carbon released from land-cover and land-use change. Direct measurements of aboveground live biomass (AGLB) are limited to small areas of forest inventory plots and site-specific allometric equations that cannot be readily generalized for the entire basin. Furthermore, there is no spaceborne remote sensing instrument that can measure tropical forest biomass directly. To determine the spatial distribution of forest biomass of the Amazon basin, we report a method based on remote sensing metrics representing various forest structural parameters and environmental variables, and more than 500 plot measurements of forest biomass distributed over the basin. A decision tree approach was used to develop the spatial distribution of AGLB for seven distinct biomass classes of lowland old-growth forests with more than 80% accuracy. AGLB for other vegetation types, such as the woody and herbaceous savanna and secondary forests, was directly estimated with a regression based on satellite data. Results show that AGLB is highest in Central Amazonia and in regions to the east and north, including the Guyanas. Biomass is generally above 300Mgha(sup 1) here except in areas of intense logging or open floodplains. In Western Amazonia, from the lowlands of Peru, Ecuador, and Colombia to the Andean mountains, biomass ranges from 150 to 300Mgha(sup 1). Most transitional and seasonal forests at the southern and northwestern edges of the basin have biomass ranging from 100 to 200Mgha(sup 1). The AGLB distribution has a significant correlation with the length of the dry season. We estimate that the total carbon in forest biomass of the Amazon basin, including the dead and below ground biomass, is 86 PgC with +/- 20% uncertainty.
rainforest↗
Increasing Liana Abundance and Associated Reductions in Tree Growth in Secondary Seasonally Dry Tropical Forest
Lianas are thought to be increasing and altering tree growth and ecosystem productivity in tropical forests, but less research has focused on secondary or seasonally dry tropical forest. We report on an 11-year study of tree growth and liana presence from Guanacaste, Costa Rica, where we measured the diameter growth and liana presence on more than 1,700 trees in regenerating forest of different ages. We find that the proportion of trees without lianas is decreasing and the number of trees with lianas occupying more than 10% of tree’s crowns is increasing. We also find that lianas are affecting the diameter growth of trees. The 11-year average relative growth rates of trees with lianas in more than 10% of the tree’s crown are lower than the relative growth of trees with no lianas or lianas in less than 10% of their crown. Year-to-year, tree relative growth rate is related to annual precipitation and tree diameter. However, trees that were heavily infested with lianas (i.e., with lianas in more than 50% of their crowns) had lower relative growth and a weaker precipitation-growth relationship. This work underscores the value of long-term longitudinal data in secondary forest and adds critical data on dry forest liana abundance change.
54 ENVIRONMENTAL SCIENCES↗
Monitoring 2009 Forest Disturbance Across the Conterminous United States, Based on Near-Real Time and Historical MODIS 250 Meter NDVI Products
This case study shows the promise of computing current season forest disturbance detection products at regional to CONUS scales. Use of the eMODIS expedited product enabled a NRT CONUS forest disturbance detection product, a requirement for an eventual, operational forest threat EWS. The 2009 classification product from this study can be used to quantify the areal extent of forest disturbance across CONUS, although a quantitative accuracy assessment still needs to be completed. However, the results would not include disturbances that occurred after July 27, such as the Station Fire. While not shown here, the project also produced maximum NDVI products for the June 10-July 27 period of each year of the 2000-2009 time frame. These products could be applied to compute forest change products on an annual basis. GIS could then be used to assess disturbance persistence. Such follow-on work could lead to attribution of year in which a disturbance occurred. These products (e.g., Figures 6 and 7) may also be useful for assessing forest change associated with climate change, such as carbon losses from bark beetle-induced forest mortality in the Western United States. Other MODIS phenological products are being assessed for aiding forest monitoring needs of the EWS, including cumulative NDVI products (Figure 10).
Spruce, J.↗
Extrapolating carbon dynamics of seasonally dry tropical forests across geographic scales and into future climates: improving simulation models with empirical observations. Final report
This research focused on seasonally dry tropical forests (SDTFs), a biome that exhibits strong variation in ecological, hydrological and physical characteristics. Climate change is expected to strongly impact the atmospheric environment of SDTFs, and long-term trends in precipitation seasonality have already been detected. The extent to which such changes in seasonal moisture availability will impact SDTF carbon dynamics is highly uncertain, and much of this uncertainty hinges on poorly-understood belowground processes. Thus, the central objectives of this research were to qualitatively and quantitatively describe how belowground processes mediate the response of SDTF carbon dynamics to environmental change. We accomplished these aims through field campaigns in four tropical countries to characterize soil biogeochemistry and plant hydraulic traits, ecosystem-scale field experiments that manipulated both nutrients and rainfall, and simulation modeling.
54 ENVIRONMENTAL SCIENCES↗
Soil emissions of nitric oxide in a seasonally dry tropical forest of Mexico
Soil emissions of NO were measured at the Chamela Biological Station, Mexico, using soil covers and a field apparatus of NO detection based on CrO3 conversion of NO to NO2 and detection of NO2 by chemiluminescence with Luminol. Mean NO fluxes from forest soils ranged from 0.14 to 0.52 ng NO-N/sq cm/hr during the dry season and from 0.73 to 1.27 ng NO-N/sq cm/hr during the wet season. A fertilized floodplain pasture exhibited higher fluxes, but an unfertilized upland pasture, which represents the fastest growing land use in the region, had flux rates similar to the forest sites. Wetting experiments at the end of the dry season caused large pulses of NO flux, equaling 10 percent to 20 percent of the estimated annual NO emissions of 0.5-1.0 kg N/ha from the forest sites. Absence of a forest canopy during the dry season and the first wet season rain probably results in substantial NO(x) export from the forest system that may be important to regional atmospheric chemical processes. Wetting experiments during the wet season and a natural rain event had little or no stimulatory effect on NO flux rates.
Davidson, Eric A.↗
The interplay of labile organic carbon, enzyme activities and microbial communities of two forest soils across seasons
Soil labile organic carbon (LOC) responds rapidly to environmental changes and plays an important role in carbon cycle. In this study, the seasonal fluctuations in LOC, the activities of carbon-cycle related enzymes, and the bacterial and fungal communities were analyzed for soils collected from two forests, namely Betula albosinensis (Ba) and Picea asperata Mast. (Pa), in the Qinling Mountains of China. Results revealed that the seasonal average contents of microbial biomass carbon (MBC), easily oxidized organic carbon (EOC), and dissolved organic carbon (DOC) of Pa forest soil were 13.5%, 30.0% and 15.7% less than those in Ba soil. The seasonal average enzyme activities of β-1,4-glucosidase (βG), and β-1,4-xylosidase (βX) of Ba forest soils were 30.0% and 32.3% higher than those of Pa soil while the enzyme activity of cellobiohydrolase (CBH) was 19.7% lower. Furthermore, the relative abundance of Acidobacteria was significantly higher in summer than in winter, whereas the relative abundance of Bacteroidetes was higher in winter. Regarding the fungal communities, the relative abundance of Basidiomycota was lowest in winter, whereas Ascomycota predominated in the same season. In addition, the soil LOC was significantly positively correlated with the CBH, βG and βX activities. Changes in LOC were significantly correlated with Acidobacteria, Bacteroidetes and Basidiomycota. We conclude that the seasonal fluctuations in forest soil LOC fractions relied on carbon cycle-associated enzymatic activities and microorganisms, which in turn were affected by climatic conditions.
54 ENVIRONMENTAL SCIENCES↗
Budgets of reactive nitrogen, hydrocarbons, and ozone over the Amazon forest during the wet season
The atmospheric composition over the Amazon forest during the wet season is simulated with a one-dimensional photochemical model for the planetary boundary layer (PBL) extending from the ground to 2000-m altitude. The model is constrained and evaluated using observations from the Amazon Boundary Layer Experiment 2B field expedition. Results indicate that only about 20 percent of NO(x) emitted by soils is exported to the atmosphere above the forest canopy. The balance is deposited to vegetation before leaving the canopy layer. The small NO(x) flux that escapes from the canopy is nevertheless sufficient to account for the low NO concentrations observed in the PBL. Soil emission can account for only a portion of NO(y) observed over the forest. Organic nitrates of nonbiogenic origin likely account for the balance of NO(y). Enhancements of CO observed in the PBL appear to reflect direct emission of CO by the forest ecosystem. Concentrations of O3 in the PBL are regulated largely by transport from aloft and deposition to the canopy, with little net influence from photochemistry. Ozone is photochemically produced immediately above the forest where NO concentrations are relatively high, but is photochemically consumed in the upper portion of the PBL.
Jacob, Daniel J.↗
(abstract) Monitoring Seasonal Change in Taiga Forests Using ERS-1 SAR Data
Sensitivity of radar backscatter to the dielectric and geometric character of forested regions suggests significant changes in backscatter are expected with season due to freezing temperatures, snow, wind, leaf fall, and drought. The first European Remote Sensing Satellite, ERS-1, offers a unique opportunity to monitor a complete seasonal cycle for the Alaskan taiga forest ecosystem with synthetic aperture radar. During the 3-day repeat Commissioning Phase of ERS-1, from August 1991to December 1991, ERS-1 SAR data were collected in the region of Manley Hot Springs, Alaska, along the Tanana River, west of Fairbanks. In parallel with the SAR data collection, meteorological data from three weather stations positioned in three forest stands were collected continuously along with in situ measurements of the dielectric and moisture properties of the canopy and of ground cover which were collected during each overflight. The in situ data were collected in floodplain forest stands dominated by balsam poplar, white spruce, and black spruce. These results from the Commissioning Phase as well as preliminary results from the 35-day Repeat Phase will be presented.
characteristics geometric characteristics moisture↗