Nima Madani

Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency

A common assumption of remote sensing-based light use efficiency (LUE) models for estimating vegetation gross primary productivity (GPP) is that plants in a biome matrix operate at their photosynthetic capacity under optimal climatic conditions. A prescribed constant biome maximum light use efficiency parameter (LUE max ) defines the maximum photosynthetic carbon conversion rate under these conditions and is a large source of model uncertainty. Here we used tower eddy covariance measurement-based carbon (CO 2 ) fluxes for spatial estimation of optimal LUE (LUE opt ) across North America. LUE opt was estimated at 62 Flux Network sites using tower daily carbon fluxes and meteorology, and satellite observed fractional photosynthetically active radiation from the Moderate Resolution Imaging Spectroradiometer. Ageostatistical model was fitted to 45 flux tower-derived LUE opt data points using independent geospatial environmental variables, including global plant traits, soil moisture, terrain aspect, land cover type, and percent tree cover, and validated at 17 independent tower sites. Estimated LUE opt shows large spatial variability within and among different land cover classes indicated from the sparse tower network. Leaf nitrogen content and soil moisture regime are major factors explaining LUE opt patterns. GPP derived from estimated LUE opt shows significant correlation improvement against tower GPP records (R2 = 76.9%; mean root-mean-square error (RMSE) = 257gCm-2yr-1), relative to alternative GPP estimates derived using biome-specific LUE max constants (R2 = 34.0%; RMSE = 439gCm-2yr-1). GPP determined from the LUE opt map also explains a 49.4% greater proportion of tower GPP variability at the independent validation sites and shows promise for improving understanding of LUE patterns and environmental controls and enhancing regional GPP monitoring from satellite remote sensing.

Predictive modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population.

The SMAP Level 4 Carbon Product for Monitoring Ecosystem Land–Atmosphere CO 2 Exchange

The National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4C) product provides model estimates of the Net Ecosystem CO2 exchange (NEE) incorporating SMAP soil moisture information. The L4C product includes NEE, computed as total ecosystem respiration less gross photosynthesis, at a daily time step posted to a 9-km global grid by plant functional type. Component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics are also included. The L4C model is driven by the SMAP Level 4 Soil Moisture data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 weather analysis, and Moderate Resolution Imaging Spectroradiometer satellite vegetation data. The L4C data record extends from March 31, 2015 to present with ongoing production and 8–12 day latency. Comparisons against concurrent global CO2 eddy flux tower measurements, satellite solar-induced canopy florescence, and other independent observation benchmarks show favorable L4C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies. Model experiments and L4C spatiotemporal variability were analyzed to understand the independent value of soil moisture and SMAP observations relative to other sources of input information. This analysis highlights the potential for microwave observations to inform models where soil moisture strongly controls land CO2 flux variability; however, skill improvement relative to flux towers is not yet discernable within the relatively short validation period. These results indicate that SMAP provides a unique and promising capability for monitoring the linked global terrestrial water and carbon cycles.

Global analysis of bioclimatic controls on ecosystem productivity using satellite observations of solar-induced chlorophyll fluorescence

Ecosystem productivity models rely on regional climatic information to estimate temperature and moisture constraints influencing plant growth. However, the productivity response to these environmental factors is uncertain at the global scale and has largely been defined using limited observations from sparse monitoring sites, including carbon flux towers. Recent studies have shown that satellite observations of Solar-Induced chlorophyll Fluorescence (SIF) are highly correlated with ecosystem Gross Primary Productivity (GPP). Here, we use a relatively long-term global SIF observational record from the Global Ozone Monitoring Experiment-2 (GOME-2) sensors to investigate the relationships between SIF, used as a proxy for GPP, and selected bio-climatic factors constraining plant growth at the global scale. We compared the satellite SIF retrievals with collocated GPP observations from a global network of tower carbon flux monitoring sites and surface meteorological data from model reanalysis, including soil moisture, Vapor Pressure Deficit (VPD), and minimum daily air temperature (Tmin). We found strong correspondence (R2 > 80%) between SIF and GPP monthly climatologies for tower sites characterized by mixed, deciduous broadleaf, evergreen needleleaf forests, and croplands. For other land cover types including savanna, shrubland, and evergreen broadleaf forest, SIF showed significant but higher variability in correlations between sites. In order to analyze temperature and moisture related effects on ecosystem productivity, we divided SIF by photosynthetically active radiation (SIFp) and examined partial correlations between SIFp and the climatic factors across a global range of flux tower sites, and over broader regional and global extents. We found that productivity in arid ecosystems is more strongly controlled by soil water content to an extent that soil moisture explains a higher proportion of the seasonal cycle in productivity than VPD. At the global scale, ecosystem productivity is affected by joint climatic constraint factors so that VPD, Tmin, and soil moisture were significant (p < 0.05) controls over 60, 59, and 35 percent of the global domain, respectively. Our study identifies and confirms dominant climate control factors influencing productivity at the global scale indicated from satellite SIF observations. The results are generally consistent with climate response characteristics indicated from sparse global tower observations, while providing more extensive coverage for verifying and refining global carbon and climate model assumptions and predictions.

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia.

Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (Dromaius novaehollandiae) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m3 m-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia.

A geo-statistical approach to model Asiatic cheetah, onager, gazelle and wild sheep shared niche and distribution in Turan biosphere reserve-Iran

Presence data for four mammals in the Turan Biosphere Reserve in Iran including the Asiatic cheetah (Acinonyx jubatus venaticus), the Persian onager (Equus hemionus onager), the wild sheep (Ovis vignei), and the gazelle (Gazelle Bennettii) were used to analyze and model their potential interaction, facilitation, habitat coverage and niche dimensions. A geostatistical approach using the spatial autocorrelation between the locality points, and their relationship with habitat resources and characteristics with application of remotely sensed maximum enhanced vegetation index (EVI) and surface temperature, elevation, aspect, vegetation cover and soil moisture was used to predict herbivores species niche. The potential suitable habitat of herbivores along with environmental variables was used to model the predator species (cheetah) niche. The model results were tested using fivefold cross validation by area under the curve (AUC) values on set of independent testing data and were compared to more commonly used models of generalized linear model (GLM) and MaxEnt. The results show that cheetahs potential suitable habitat has 61% overlap with wild sheep, 36% with onager, and 30% with gazelle. Onager habitat has 64% overlap with gazelle and 60% the wild sheep. Wild sheep on the hand, shares only 37% of its habitat with gazelle. The most prey and predator interaction exists between cheetahs and wild sheep, while onagers provides facilitation for gazelles and wild sheep by potentially providing extra water sources. Among the implemented modeling techniques, spatial GLM showed better performance over GLM and MaxEnt. We suggest that conservation effort should focus more on maintaining the population of wild sheep and onagers to support other species in the habitat.

Future global productivity will be affected by plant trait response to climate

Plant traits are both responsive to local climate and strong predictors of primary productivity. We hypothesized that future climate change might promote a shift in global plant traits resulting in changes in Gross Primary Productivity (GPP). We characterized the relationship between key plant traits, namely Specific Leaf Area (SLA), height, and seed mass, and local climate and primary productivity. We found that by 2070, tropical and arid ecosystems will be more suitable for plants with relatively lower canopy height, SLA and seed mass, while far northern latitudes will favor woody and taller plants than at present. Using a network of tower eddy covariance CO2 flux measurements and the extrapolated plant trait maps, we estimated the global distribution of annual GPP under current and projected future plant community distribution. We predict that annual GPP in northern biomes (≥45 °N) will increase by 31% (+8.1 ± 0.5 Pg C), but this will be offset by a 17.9% GPP decline in the tropics (−11.8 ± 0.84 Pg C). These findings suggest that regional climate changes will affect plant trait distributions, which may in turn affect global productivity patterns.

Improving Global Gross Primary Productivity Estimates by Computing Optimum Light Use Efficiencies Using Flux Tower Data

In the Light Use Efficiency (LUE) approach of estimating the Gross Primary Productivity (GPP), plant productivity is linearly related to Absorbed Photosynthetically Active Radiation (APAR) assuming that plants absorb and convert solar energy into biomass within a maximum LUE (LUEmax) rate, which is assumed to vary conservatively within a given biome type. However, it has been shown that photosynthetic efficiency can vary within biomes. In this study, we used 149 global CO2 flux towers to derive the Optimum LUE (LUEopt) under prevailing climate conditions for each tower location, stratified according to model training and test sites. Unlike LUEmax, LUEopt varies according to heterogeneous landscape characteristics and species traits. The LUEopt data showed large spatial variability within and between biome types, so that a simple biome classification explained only 29% of LUEopt variability over 95 global tower training sites. The use of explanatory variables in a mixed effect regression model explained 62.2 % of the spatial variability in tower LUEopt data. The resulting regression model was used for global extrapolation of the LUEopt data and GPP estimation. The GPP estimated using the new LUEopt map showed significant improvement relative to global tower data, including a 15% R2 increase and 34% RMSE reduction relative to baseline GPP calculations derived from biome specific LUEmax constants. The new global LUEopt map is expected to improve the performance of LUE based GPP algorithms for better assessment and monitoring of global terrestrial productivity and carbon dynamics.

The SMAP level 4 carbon product for monitoring terrestrial ecosystem-atmosphere CO 2 exchange

The NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon (L4_C) product provides model estimates of Net Ecosystem CO2 exchange (NEE) incorporating SMAP soil moisture information as a primary driver. The L4_C product provides NEE, computed as total respiration less gross photosynthesis, at a daily time step and approximate 14-day latency posted to a 9-km global grid summarized by plant functional type. The L4_C product includes component carbon fluxes, surface soil organic carbon stocks, underlying environmental constraints, and detailed uncertainty metrics. The L4_C model is driven by the SMAP Level 4 Soil Moisture (L4_SM) data assimilation product, with additional inputs from the Goddard Earth Observing System, Version 5 (GEOS-5) weather analysis and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The L4_C data record extends from March 2015 to present with ongoing production. Initial comparisons against global CO2 eddy flux tower measurements, satellite Solar Induced Canopy Florescence (SIF) and other independent observation benchmarks show favorable L4_C performance and accuracy, capturing the dynamic biosphere response to recent weather anomalies and demonstrating the value of SMAP observations for monitoring of global terrestrial water and carbon cycle linkages.

Monitoring ecosystem-atmosphere co 2 exchange respose to recent (2015–2016) climate variability using the smap l4 carbon product

The recent two years have been characterized by contrasting climatic variability across the globe driven by the onset of a strong El Niño event and relaxation back to ENSO neutral conditions. We investigated the global pattern and seasonal cycle of net ecosystem-atmosphere CO2 exchange (NEE) for 2015 and 2016 using satellite observation based estimates from the NASA Soil Moisture Active Passive (SMAP) mission. The SMAP Level 4 Carbon product (L4C) was previously validated using globally-distributed eddy covariance flux tower observations and other independent observations. For this study, we investigated L4C seasonal and annual anomalies for vegetation productivity and NEE relative to baseline carbon flux estimates determined from the L4C model-based historical (2001–2012) climatology. Our results reveal large global carbon flux anomalies associated with ENSO related events. Australia transitioned from slightly anomalous CO2 release under dry conditions in 2015 to a strong CO2 sink in response to record precipitation in 2016. We also find contrasting seasonally-dependent CO2 source/sink anomalies between the borealand temperate-northern latitudes which began in 2015 and persisted through 2016, associated with early spring onset, hot and dry summers, and an El Niño-enhanced temperate monsoon. These results highlight the capability of the SMAP L4C product for continued global monitoring of terrestrial ecosystems, including environmental and drought related impacts on vegetation growth, carbon sink strength and associated ecosystem goods and services.