Wenge Ni-Meister’s Research Projects
Land, Water, Carbon and Climate
The Earth From Above
My research centers on ecosystem-climate interactions. Human induced increases in atmosphere CO2 is often blamed for recent and projected climate change. However human induced land surface change could have an even greater impact on climate by changing the terrestrial ecosystem energy, water and carbon budgets. Quantifying this impact is limited due to our limited understanding of the ecosystem and climate interactions. Meanwhile, it is still not clear how the complex terrestrial ecosys-tem responds to the changed climate. My research seeks answers to theses questions.
I have been working on different aspects of ecosystem-climate interactions throughout my career, with a focus on utilizing remote sensing data with land surface biophysical and ecosystem process models for better understanding and predictions of ecosystem processes. Physical models help re-searchers better understand the physical processes of ecosystem influence on weather and climate and the ecosystem response to climate change. Meanwhile, satellite remote sensing data provides large spatial information on land surface states. This information provides accurate inputs and initialization states to biophysical and ecosystem models and may provide critical information for scientific under-standing of the role of dynamics ecosystem on global climate change. Integrating satellite data with physical models provides the best understanding of land surface states and ecosystem processes under the changed climate, and helps researchers find the best answers for the questions posed above. Over-all, I strive to apply my research results to different application fields to provide more accurate infor-mation about the environment we live and help policy makers make better decisions regarding our en-vironment.
This website contains detailed
information about my research projects. Feel free to contact her to further
discuss any of her research projects at
Current Research Activities
1. Land Surface Biophysical/Ecosystem Dynamic Modeling
2. Remote Sensing
3. Data Assimilation
5. Research Outreach/Education
1. Land Surface Biophysical/Ecosystem Dynamic Modeling
Light is essential to ecosystem biophysical and biogeochemical processes and land surface hydrological processes. The radiation regime above, within, and below the forest canopy plays a crucial role for photosynthesis, carbon accumulation, tree growth and snowmelt. The complex architecture of forests results in strong landscape heterogeneity with respect to radiation at many spatial scales. I modified and implemented a Geometric Optical and Radiative Transfer (GORT) canopy radiative transfer model to account for the impact of the complex vegetation structure on radiation absorption by vegetation elements and on photosynthesis during my PhD work and have published the research results in prestigious journals (Ni and Woodcock, 2000; Ni et al. 1997; Davis et al. 1997; Hardy et al. 1997; and Li et al, 1996a+b).
Shortly after I arrived at Hunter, I recognized that the vegetation canopy models embedded within global climate models used in climate change research do not correctly represent the interaction between the vegetation and solar radiation, which drives their biogeophysical functions. Thus, I’ve continued working with the canopy radiative transfer model I developed during my PhD to simplify its computational demands while preserving core physical processes and to implement it into a global climate model for climate-ecosystem interaction studies. In this work, I collaborate with several scientists from NASA and Harvard University to implement my model in the NASA Goddard Institute Space Science (GISS) and Goddard Modeling and Assimilation Office (GMAO) global climate models. This collaborative research is funded by NASA.
• Ent: A model for terrestrial ecosystem-climate interactions for seasonal to century time scales through coupled water, carbon, and nitrogen dynamics (NASA, 2006-2009).
--The goal of this project is to develop a dynamic global terrestrial ecosystem model that can be coupled with armopheric general circulation models (GCMs). ENT will be capable of predicting the fast time scale fluxes of water, carbon, nitrogen and energy between the land-surface and the atmosphere and the resulting diurnal surface fluxes, seasonal and inter-annual vegetation growth, and decadal to century scale alterations in vegetation structure and soil carbon and nitrogen. Radiative transfer, biophysics, biogeochemistry, and ecological dynamics will be integrated in a consistent, prognostic, process-based manner, in a way that is both biologically realistic and computationally efficient, and suitable for two-way coupling and parallel computing in GCMs. The model is designed to span the goals of Goddard, GISS, and the NASA Astrobiology Institute, and can be used in conjunction with both the GMAO modeling system to allow assimilation of satellite data and with the GISS GCM for long-term climate studies. Ent will be coupled with atmospheric GCMs for studies on climate variability, global change, vegetation-climate feedbacks.
• The Effect of Subgrid Variability of Snow Cover in Vegetated Regions on Land-Atmosphere Interactions (NASA, 2001-2005).
General Circulation Model (GCM)-subgrid variability of snow cover has a significant impact on land-atmosphere interactions due to the high albedo, low thermal conductivity, and melting processes of snow. In highly vegetated mid- and high-latitude regions, where the presence snow is often found below and between plant canopies, Land surface heterogeneity is even more complex and it is difficult to predict its influence on the atmosphere. A better land surface parameterization scheme to account for subgrid variability in vegetated regions is necessary for studying land-atmosphere interactions in regional and global climate and weather prediction models.
We were funded to develop a hybrid model to characterize GCM-subgrid variability of snow cover in vegetated regions and to use this hybrid scheme to study the impact of subgrid land surface heterogeneity on land surface processes, surface hydrology, and the planetary boundary layer (PBL) development during winter-spring seasons. The hybrid scheme is developed by incorporating a well-developed canopy radiation model (GORT) to characterize the variations of radiation regime at forest stand or smaller scales, into a macro-scale process-based land surface model (VIC). This hybrid scheme is being coupled with a non-local PBL model. The final goal is to develop an improved coupled land-atmosphere model for snow-covered and vegetated regions to study the interrelationships and feedbacks among clouds, precipitation, boundary layer and land surface processes. We will use this coupled land-atmosphere model to study the influences of snow cover heterogeneity in vegetated regions on land surface hydrological processes, the surface energy and water balances, and atmospheric dynamics such as the turbulent boundary layer development, cloud and precipitation patterns.
2. Remote Sensing
Observations of vegetation through remote sensing may provide critical information for natural resource management, and scientific understanding of the role of forests on global climate change. To better retrieve vegetation information from remote sensing data, I have applied the GORT canopy radiation model to better understand how land surface vegetation structure parameters are linked with multi-spectral and multiangular optical remote sensing data, and how vegetation structure parameters are linked with the spatial patterns of remote sensing imageries (Ni et al. 1999a+b; Ni and Li, 2000; Ni and Jupp, 2000, Geiger and Ni, 20003 and Meister et al. 2003). I have used the GORT model to explore the relationship between canopy structure parameters and carbon biomass and vegetation lidar remote sensing data (Ni-Meister et al., 2001). My studies provide a theoretical basis to explore the biophysical and biogeochemical processes and carbon cycle of terrestrial ecosystems from multi-spectral, multi-angular and multi-spatial passive and active remote sensing imageries.
Vegetation structure characteristics and biomass are the key inputs for ecosystem biogeochemical models. One exciting result from my canopy radiation model development is being able to use the canopy radiative transfer GORT model to develop an inversion scheme to extract vegetation structural characteristics and above ground biomass from vegetation lidar and multi-angular remote sensing data. Currently we are developing an inversion scheme to invert GORT to extract land surface vegetation structure characteristics and above ground biomass from lidar and mulitangular remote sensing data (Ni-Meister et al. 2006b and Ni-Meister 2006, manuscripts in preparation). Together with research scientists from Boston University and Australia CSIRO, we are recently funded by NASA to use GORT model to further integrate a ground and space lidar data to map vegetation structure characteristics. We are modifying GORT model to integrate ground and airborne lidar data (click here to read more details).
• Retrieval of Vegetation Structure and Carbon Balance Parameters Using Ground Based Lidar and Scaling to Airborne and Spaceborne Lidar Sensors (NASA/Boston University, 2006-2009).
– To combine a ground- and spaced-based lidar and physical model to map global vegetation structure and biomass for carbon inventory and carbon balance modeling.
Accurate estimates of above-ground biomass and information on the functioning of forests as they interact in the carbon cycle are crucial to understanding the Northern American terrestrial car-bon sink and determining the impact of natural and anthropogenic disturbance on the carbon budget. Such data and their spatially averaged values are key in-puts to carbon ecosystem models. Their temporal change also indicates the impact of disturbance on carbon cycle dynamics and its future impact on the carbon store.
We develop methods for large-area mapping of forest biomass parameters through a combination of downward- and upward- looking lidar and multiangle multispectral optical imagery. Using a consistent approach based on the Geo-Optical Radiative Transfer (GORT) model, we will merge theory describing the scattering of light by 3-dimensional plant canopies of leaves, branches, and trunks with observations using down-looking canopy lidar, upward-looking ground lidar, and multiple view angle (MVA) optical data at spatial scales ranging from centimeters to kilometers. Our goal is to retrieve such forest structure parameters as canopy height, canopy cover, crown diameter, crown areal density (crowns per hectare), and foliage area volume density over large areas from the spaceborne Geoscience Laser Altimeter System (GLAS) acquired by (ICESat) profiles and MVA (MISR) imagery. These, in turn, will drive estimates of green and woody biomass as continuous spatial fields at subkilometer scales. The tools we develop will provide the ability to inventory and monitor forest green and woody biomass, our work will also advance monitoring regional biomass and change as particularly relevant to the carbon cycle.
3, Data Assimilation -- Integration of Models and Remote Sensing Data
• Optimal Land Initialization for Seasonal Climate Predictions through integrating remote sensing data and land surface models (NASA, 2002-2005).
The goal is to develop a scheme to integrate a land surface process model and satellite derived soil moisture data to enhance our current seasonal to interannual climate prediction ability.
Land surface states influence the atmosphere through the exchanges of energy and moisture over a variety of time scales. Accurate initialization of land surface states in fully-coupled climate system models is critical for seasonal-to-interannual climatological and hydrological predictions. Remote sensing provides a technically consistent means to monitor land surface states at large regional to global scales.
We applied and improved the Kalman filter-based data assimilation strategy to integrate remotely sensed soil moisture data with a catchment-based land surface processes model (CLSM) being used in the current NASA Goddard Modeling and Assimilation Office (GMAO) GCM to improve the estimate of land surface soil moisture states.
The goal is to extract urban land use information for modeling study on the impacts of urban land use in NYC on climate and environment for better urban management and planning and the urban climate modeling study.
Urban development is the most rapid transformation taking place in term of land use and land cover change in metropolitan and urban areas. These changes directly impact the local climate (microclimate) and the environment, with consequent impacts on health and quality-of-life of their urban environment.
With recent development of remote sensing technology, particularly with current very high spatial/temporal resolution satellites, satellite remote sensing technology provides us a consistent means to measure detailed urban land use structure and vegetation cover at high spatial and temporal scales, as well as information on climate variables such as surface temperature, precipitation and on air quality such as aerosol. This information will be very valuable information for urban management and planning and for the modeling study on anthropogenic activity on climate and environment.
map urban land use in
The goal is to assess the potential use of
The Environmental Protection Agency (EPA) estimates that over 20,000 bodies of water throughout the country are too polluted to meet water quality standards (USGAO, 2000). EPA has developed and uses the BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) modeling system to assess point source and non-point source pollution for use by regional, state and local agencies making watershed and water quality studies. The linked HSPF (Hydrologic Simulation Program-Fortran) uses sparse meteorology station and rain gauge data to simulate runoff for nonpoint source pollution loading for a watershed.
NASA’s Land Information System (LIS) is a real-time and retrospective hourly, land surface simulation system for providing outputs of land surface water and energy conditions and land surface parameters based on sophisticated land surface models and satellite and ground-based observations. NASA LIS model outputs – evaportranspiration together with precipitation can provide more accurate water availability inputs to the EPA– HSPF water quality non-point source decision support tool.
primary emphasis is to use NASA products to provide better forcing functions
(e.g., precipitation, evaporation, etc.) to improve the performance of the HSPF
water quality assessment tool. We concentrate our efforts to both precipitation
and ET for six watersheds in Chesapeake Bay, being used by the NASA-Goddard
5. Research Outreach/Education
• Integrated Learning of Urban Environment -- Inspiring the next generation of Earth explorers
The goal of this project is to create opportunities for CUNY undergraduate to explore the use of remote sensing technology to understand Earth system sciences and to gain more hands-on research experience in Earth System Science. (NASA, 2006-2007).
NASA satellite data and analysis results produce large Earth system geospatial information that spans the full spectrum of spatial and temporal scales. It is crucial for underrepresented students on CUNY campuses to have opportunities to be exposed to and to explore cutting edge NASA technology and advanced research, and to stimulate their motivation to remain in the STEM pathway. Together with CCNY, we are developing a new initiative to provide more opportunities for students to explore NASA research results. We collect many cutting-edge research results on Earth sciences using new satellite techniques and bring them to our remote sensing classrooms to inspire student's interests in Earth system sciences. We also create research opportunities for undergraduate students to gain more hands-on research experience in Earth System Science. The present work will contribute to the goal of drawing more minorities into the physical sciences and to fulfilling a national need for the training of new scientists.
Last updated: 2006, Nov