LCLUC 2017 Webinar Series

The 2017 Webinar Series features 3 series: LCLUC projects in mountainous areas, MuSLI projects, and LCLUC synthesis projects.

Mountainous areas projects

The 2017 LCLUC projects in mountainous areas Series feature LCLUC projects focusing on detection and monitoring of land-cover and land-use changes and impacts in various regional landscapes, including environmental conflicts as well as community based forest efforts. This research contributes towards providing critical scientific information about LCLUC and the consequences of land-cover and land-use change on environmental goods and services and the management of natural resources. 


Multi-source Land Imaging (MuSLI) projects

Multi-source Land Imaging (MuSLI) projects promote and enhance the use of multiple sensor data at high-to-moderate spatial resolution allowing for more clear-sky observations to study landscape changes over the globe and develop continental and global scale higher-level products. The Landsat and ESA Sentinel programs are both providing users free data access. The availability of two Landsats, two Sentinel-2, and two sentinel-1 satellites all currently in space opens a new era for dense time series research at 10-30m spatial resolution for studying forest, agriculture, and urban processes with observations every 2-3 days, provided the sky is clear. The MuSLI team under the LCLUC program is focused on developing innovative approaches with the most recent team formed in 2017. Activities are coordinated and combined with the newest Landsat Science Team just formed by the USGS in December 2017.


LCLUC synthesis projects

The Fall 2017 LCLUC webinar series features our LCLUC synthesis projects that aim to generate critical syntheses producing new, emergent insights and focus on integration of existing results rather than development of new data or models. Synthesis projects include a social or economic science component as an integral portion of the study, including the use of already existing survey data.


LCLUC in mountainous regions

Tuesday March 28, 2017 Time: 2:00 PM EST (1:00 PM CST, 11:00 AM PST)



Dr. Volker Radeloff
University of Wisconsin, Madison

Land Use Change in the Caucasus Mountains Due to Ethnic Differences, National Policies, and Armed Conflicts.

Mapping land use change in mountainous regions is challenging because steep topography alters the apparent surface reflectance in satellite images, resulting in classification errors. However, the launch of Landsat 8 offers new opportunities for topographic correction though, because OLI’s 16-bit data is more sensitive in areas affected by cast shadows. Here we compare Landsat images with and without topographic correction in the Caucasus Mountains and demonstrate the classification gains that result from topographic correction, especially in steep terrain. Our result demonstrate that topographic correction of Landsat imagery is possible and important when analyzing land use change in mountainous regions.


Jamon Van Den Hoek
Oregon State University

Twenty-Five Years of Community Forestry: Mapping Forest Dynamics in the Middle Hills of Nepal

We will present initial results of our LCLUC-funded research on mapping annual forest dynamics in the Middle Hills of Nepal from 1990-2015. Nepal is a challenging region for forest cover change mapping in part due to the regularity of cloud cover and the country's extreme topographic relief that result in variable solar illumination and shading. To mitigate these conditions, our mapping approach includes a rigorous evaluation of terrain correction approaches, a disturbance detection methodology that leverages the full Landsat time series, and a Google Earth Engine-based image analysis framework. In this presentation, we will provide an overview of our methods, illustrate the spatial distribution of hotspots of forest cover growth and loss across Nepal, and discuss potential socio-economic drivers of forest cover change such as the spread of Nepal's community forests and remittance income borne from foreign labor migration.

speaker_pic Presenter:

Andrew Hansen
Montana State University

Downscaling IPCC Land Use Scenarios for Global Change Adaptation Planning in Mountainous Environments

The Intergovernmental Panel on Climate Change (IPCC) uses scenario development as its major vehicle for visualizing potential future conditions, their consequences, and adaptation options. Unfortunately, initial efforts to downscale IPCC socioeconomic scenarios to levels relevant for policy have not adequately represented land cover and land use change (LCLUC) in mountain landscapes. Exurban development, the fastest growing land use type in these landscapes, is either not resolved or projected accurately. Modeling of rural LCLUC can be improved through new remote-sensing techniques for detecting the fine-grained development typical in mountains and better understanding of the context-dependency of drivers of LCLUC. Our goal is to project LCLUC under IPCC scenarios across northwestern US mountain landscapes and to apply the results to enhance vulnerability assessments of biodiversity to future global change. This presentation will examine rates of spatial patterns and rates of change in exurban development and test hypotheses on the relative and context-dependent influence of proximity to cities and markets, natural resources, natural amenities, and climate change.


Tuesday May 16, 2017 Time: 2:00 PM EST (1:00 PM CST, 11:00 AM PST)



Dr. Geoff Henebry
South Dakota State University

How Environmental Change in Central Asian Highlands Impacts High Elevation Communities.

Prior to the Soviet era, highlanders in Central Asia practiced vertical transhumance in raising livestock—sheep and goats—for wool, meat, milk, and hides. Collectivization disrupted this practice with multiple external subsidies. Since 1991 montane agro-pastoralism has been disrupted by withdrawal of external subsides and introduction of a market economy. Moreover, montane agropastoralism is highly vulnerable to environmental change. Our project evaluates four aspects of environmental change in human settlements and associated pasturelands in representative areas in the Kyrgyz Republic during the satellite era and projected changes into the middle of the 21st century to assess impacts on these highland communities and the pastures upon which they depend. The four aspects of environmental change are (1) changes in the thermal regime, including growing season timing and extremes, (2) changes in the moisture regime, including peak precipitation timing and onset and duration of snow cover, (3) changes in socio-economic conditions, including impacts of globalization through labor migration and remittance income, and (4) changes in land cover, land use, and land condition, including alterations in terrain. To date we have been focusing on highland communities in four rayons in the Kyrgyz Republic: At-Bashy and Naryn in Naryn oblast, and Alay and Chong-Alay in Osh oblast. We have developed a novel approach to characterizing seasonal land surface dynamics in mountainous terrain through blending Landsat surface reflectance data and MODIS land surface temperature and snow cover products with 30 m DEM data. Modeling the land surface phenology with a simple quadratic model yields two phenological metrics: Peak Height (PH) of NDVI and Thermal time to Peak (TTP). Phenometric lapse rates—change in phenometrics as a function of elevation—enable us to characterize land surface phenology and snow cover seasonality in highland pastures using the thermal time metrics growing degree-days and frost degree-days, respectively, calculated from MODIS land surface temperature data. Of particular interest are the influences of snow cover melt date and snow cover duration on subsequent metrics of land surface phenology—peak height and thermal time to peak—as modulated by terrain (elevation, slope, and aspect).


Dr. Giorgos Mountrakis
State University of New York

Management of Social-Ecological Grazing Systems in the Altai Mountain Transboundary Zone.

How broad - scale factors impinge on local decision making and translate into land use change is not well understood. This is particularly true of the vast rangelands of the Altai Mountains in central Asia where grazing was and remains the dominant form of agriculture and land use. Critical questions remain about what communities, donors, and policymakers can do to promote desirable co - management outcomes in grazing systems of this ecologically similar region but politically complex region which remains in upheaval following the collapse of the former Soviet Union and the heavy subsidies it once provided to herding societies. Our study will take a nested approach that first contrasts long - term, broad - scale vegetation dynamics for the same high montane grasslands occupied by Kazakh peoples herding livestock across four countries with strikingly different political systems Mongolia, Russia, Kazakhstan and China (regional modeling). We integrate remotely sensed data on recent LCLUC with semi - structured interviews of local herders at the local level among grazing areas in a transboundary region shared between Mongolia and Russia along the Sailyugem Range (local modeling). This presentation will share findings on: i) correlation of MODIS NDVI with ground vegetation cover from our field survey, ii) linkages between traditional ecological knowledge on forage quality, as expressed via herder interviews, and satellite observations, and iii) LCLUC classification advancements related to per-pixel accuracy estimation and the effects on landscape heterogeneity.


MuSLI projects

videoWebinar Recording

Monday June 12, 2017 Time: 2:00 PM EST (1:00 PM CST, 11:00 AM PST)


Dr. Chengquan Huang
University of Maryland

Towards Near Daily Monitoring of Inundated Areas Over North America Through Multi-Source Fusion of Optical and Radar Data
Inundated areas, including lakes, streams, some wetlands, as well as episodically flooded areas, play important roles in many Earth system processes and provide a broad range of ecosystem services. In the meantime, they are being lost at alarming rates. However, present knowledge of the spatial and temporal dynamics of terrestrial inundation is limited. Existing surface water maps often disagree on the distribution and extent of relatively stable water bodies, and wetlands and other episodically inundated areas that are more difficult to map are among the least accurate classes in many land cover products. Further, no existing national to global scale products provide near daily, sub - hectare details on terrestrial inundation, which are critical for fully characterizing the dynamics of many inundated areas. When completed in 2017, the constellations of the European Space Agency's (ESA) Sentinel - 1 and - 2 together with Landsat - 8 will, for the first time, provide near daily global datasets at sub - hectare spatial resolutions. The primary goal of this study is to utilize this constellation of satellites to develop and demonstrate improved capability to monitor terrestrial inundation. We will develop automated algorithms suitable for inundation monitoring at the global scale using Landsat - 8/Sentinel - 2 (L8S2) optical data and Sentinel - 1 (S1) SAR data. These algorithms will be calibrated and tested extensively over study areas selected from different biomes, and will be used to generate near daily inundation products for temperate, subtropical, and tropical North America, including the United States and southern Canada. According to current launch schedules, we expect to have the data necessary to generate these products for one full year (~2017 - 2018) through this project. Delays in the launch of one or more of these systems will result in less than near daily coverage but will not impede the overall project. This study responds to the LCLUC NRA by maximizing "the utility of current and near - future remote sensing capabilities" to study terrestrial inundation, a highly dynamic phenomenon that needs to be characterized at sub - hectare resolutions on a near daily basis. It provides an "efficient use and seamless combination" of L8S2 optical data and S1 SAR data for understanding global inundation dynamics. Being fully automated, the developed algorithms can be implemented in an operational system to generate global, long - term inundation records. The products derived through this study will represent multi - order improvements over existing knowledge. This study will help develop techniques to rapidly incorporate NASA - ISRO's future NISAR data into an operational inundation monitoring framework, and will benefit multiple ongoing US federal efforts, including NASA's Arctic - Boreal Vulnerability Experiment, USGS's National Water Census (, EPA's efforts to clarify the definition of Waters of the U S under the Clean Water Act, and NOAA's Coastal Change Analysis Program.


Dr. Joseph Sexton
University of Maryland

Multi-Source Imaging of Time-Serial Tree and Water Cover at Continental to Global Scales​

We propose to develop a seamless and consistent, moderate - (i.e., sub - hectare) resolution database of percent - tree and water cover on a global, epoch al basis in 2000, 2005, and 2010 and continentally at annual frequency from 2010 to 2015. Globally, we will refine our existing Landsat - based maps of tree and water cover in 2000 and 2005, and we will extend these data with a global layer for 2010. Further , we will estimate tree and water cover annually from 2010 to 2015 across North and South America. All estimates will be accompanied by per - pixel estimates of uncertainty. To do so, we will generalize our proven multi - source fusion algorithms and apply the m to a combination of Landsat, ALOS - PALSAR, Sentinel - 2, and other data sources. This research will be partnered with the ESA - funded GLOBBIOMASS project (C. Schmullius, PI), which will map biomass regionally and globally in 2000, 2005, and 2010 epochs based on our maps of tree cover.


videoWebinar Recording

Thursday August 10, 2017 Time: 2:00 PM EDT (1:00 PM CDT, 11:00 AM PDT)



Dr. Christopher Small
Columbia University

Multi-source Imaging of Infrastructure and Urban Growth using Landsat, Sentinel, and SRTM

The Landsat program provides more than three decades of decameter resolution multispectral observations of the growth and evolution of human settlements and development worldwide. While these changes are often easy to observe visually, accurate repeatable quantification at Landsat's resolution has proven elusive. In part, this is a consequence of the multi-scale heterogeneity and diversity of settlements worldwide. Efforts to map settlement extent are also confounded by the lac k of a single, physically - based, definition of what constitutes urban, suburban, peri-urban and other types of settlement. We attempt to resolve both of these challenges by characterizing built environments in terms of their distinctive physical properties. This can be accomplished by combining multi - temporal optical reflectance with synthetic aperture radar backscatter measurements to identify combinations of physical properties that distinguish built environments from other types of land cover. Three well - known examples include an abundance of impervious surface, persistent deep shadow between buildings and high density of corner reflectors at meter to decameter scales. At optical wavelengths, spectral properties of land cover can be represented using standardized spectral endmember fractions to represent combinations of the most spectrally and functionally distinct components of land cover soil and impervious substrates, vegetation, water and shadow. The spectral similarity of soils and impervious substrates that makes thematic classifications error prone can be resolved by using multi-season composites of spectral endmembers to distinguish spectrally stable impervious substrates from temporally variable soil reflectance resulting from seasonal changes in moisture content (thus albedo) and fractional vegetation cover. By representing the diversity of anthropogenic land use as a continuous mosaic of land cover it is possible to quantify the wide variety of human settlements in a way that is physically consistent, repeatable and scalable. We propose to develop and test algorithms to combine multi-season Landsat and Sentinel-2 optical multispectral imagery with SRTM and Sentinel - 1 C - band radar backscatter imagery to produce a continuous Infrastructure Index (II) to identify and map changes in the extent of anthropogenic built environments (e.g. urban, suburban, exurban, peri-urban) worldwide between 2000 and 2015. Rather than attempting to map specific features associated with built environments (e.g. impervious surfaces, buildings, roads), we will characterize the combined optical and microwave response of a wide range of built environments to identify the physical properties associated with these features (e.g. spectral stability, persistent shadow, anisotropic backscatter intensity). We will then use the most persistent of these properties to derive an index incorporating multiple characteristics measured by both optical and microwave sensors. The index will be calibrated using the full range of properties observed in a set of ~20 test sites spanning urban - rural gradients worldwide and vicariously validated using high spatial resolution (1-4 m) imagery and the DLR 8 m urban footprint product. As an independent comparison, we will use high resolution (sub-km) census enumerations circa 2000 and 2010 to map changes in population density associated with the mapped changes in the infrastructure index at test sites in the USA, Brazil, Portugal, Malawi, South Africa and Sri Lanka.



Dr. Mark Friedl
Boston University

Multi-source Imaging of Seasonal Dynamics in Land Surface Phenology

Land surface phenology, including not only the timing of phenophase transitions but also the entire seasonal cycle of surface reflectance and vegetation indices, is important for a wide range of applications including ecosystem and agro-ecosystem modeling, monitoring the response of terrestrial ecosystems to climate variability and extreme events, crop-type discrimination, and mapping land cover, land use, and land cover change. While methods to monitor and map phenology from coarse spatial resolution instruments such as MODIS are now relatively mature, the spatial resolution of these instruments is inadequate for many applications, especially where land use and land cover vary at scales of 10's of meters. To address this need, algorithms to map phenology at moderate spatial resolution (~30 m ground resolution) using data from Landsat have recently been developed. However, the 16-day repeat cycle of Landsat presents significant challenges for monitoring seasonal variation in land surface properties in regions where changes are rapid or where cloud cover reduces the frequency of clear-sky views. The ESA/EU Sentinel-2 satellites, which will provide moderate spatial resolution data at 5-day revisit frequency near the equator and 2-3 day revisit frequency in the mid-latitudes, will alleviate this constraint in many parts of the world. Further, by combining data from Sentinel-2 and Landsat, it should become possible to monitor large areas of the Earth's land surface at frequencies that were previously not possible. The goal of the research described in this proposal is exploit the combined observational capabilities of Landsat and Sentinel-2 to develop the algorithmic, methodological, and computational basis for moderate spatial resolution monitoring of land surface phenology. Specifically, we propose to develop algorithms that will use a combination of Landsat and Sentinel-2 data to: (1) quantify the timing and magnitude of land surface phenology events ("phenometrics") at 30-m spatial resolution, and (2) generate gap-filled time series of spectral vegetation indices that characterize the entire seasonal cycle of land surface phenology at fixed time steps. To help achieve these goals, we propose to collaborate with Prof. Lars Eklundh at Lund University in Sweden, who developed the widely used TIMESAT algorithm for estimating phenology and who is currently funded by the Swedish Space Agency to adopt TIMESAT for use with Sentinel-2. Results from this research will provide the foundation for operational production of multi-sensor land surface phenology data products at moderate spatial resolution. Further, by implementing our algorithms in TIMESAT, the proposed research will provide flexible tools that can be exploited by the user community for location-and application-specific needs.

Tuesday, September 19th, 2017 Time: 2:00 PM EST (1:00 PM CST, 11:00 AM PST)


Dr. Nathan Torbick
Applied Geosolutions LLC

Operational algorithms and products for near-real time maps of rice extent and growth stage using multi-source remote sensing

Rice is one of the most important crops globally for food production, supporting livelihoods, and its role in the Earth system. Rice agriculture faces major challenges in the coming decade due to increasing resource pressures, severe weather and climate change, population growth and shifting diets, and economic development. The overall goal of this proposed project is to develop integrated, seamless algorithms and operationalize multisource SAR and optical products for near real time maps of rice extent, rice calendar, and rice crop growth stage to support food security and land use decision making in South Asia. We propose to fuse moderate resolution, operational Synthetic Aperture Radar platforms (ALOS - 2, Sentinel - 1, RISAT - 1) with Landsat 8 OLI and Senintel-2 for mapping land use and characterizing rice agricultural conditions at 30m at national scales using a Classification And Regression Tree framework. Operationalize multisource algorithms to provide weekly maps of: near real time rice extent, rice cropping calendar and growth (phenological) stage and track risk or deviation from normal as an indicator for crop failure. We will conduct technology transfer with partners in developing regions using an open and transparent approach and grow institutional capacity to support multi - source land monitoring and food security in coordination with GEOGLAM and AsiaRice. The Group on Earth Observations (GEO) is a coordinated effort to build the Global Earth Observation System of Systems (GEOSS). GOESS is the leading framework to integrate Earth Observations and geospatial mapping tools to support Global Agricultural Monitoring (GLAM). NASA is a strong supporter of GEO and G OESS and, in particular, the Agricultural task force (GEOGLAM). The next phase of the GEOGLAM plan is to begin to scale up pilot applications to have national and global monitoring tools within he next decade (fig 2). GEOGLAM was tasked to coordinate satellite monitoring observation systems in different regions of the world in order to enhance crop production projections and support the Agricultural Market Information System (AMIS) and Crop Monitor. The objective of the Crop Monitor is to provide an international and transparent multi - source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. This activity covers the four primary crop types (wheat, maize, rice, and soy) within the main agricultural producing regions. Currently, S. Asia has several gaps that are not well represented in GEOGLAM and this proposal will help fill those gaps.


Speaker Bio


Dr. Matthew Hansen
University of Maryland

Integrating Landsat 7, 8, and Sentinel-2 in improving crop type identification and area estimation

Identification of crop type and areal extent is a challenge, made difficult by the variety of cropping systems, including crop types, management practices, and field sizes. The goal of this project is to evaluate the integrated use of Landsat an d Sentinel 2 data in quantifying cultivated area by major commodity crop type. The first evaluation objective is correct identification of crop type. MODIS data, due to its high image cadence, are appropriate for and have been extensively used for mapping crop. Using MODIS as a high temporal reference, an assessment of combined Landsat and Sentinel 2 observations in identifying crop type will be performed. For any given crop type, its areal extent is required in estimating production. RapidEye data represent a high temporal, high spatial resolution imaging capability over limited areas. RapidEye data will be used to evaluate area estimation of selected crop types and fine - scale agricultural landscapes using combined Landsat and Sentinel 2 data. Results will inform users of the potential value of Landsat and Sentinel 2 data to identify and map the extent of key commodity crops for a variety of landscapes, including wheat, corn and soybean.


LCLUC synthesis projects

videoWebinar Recording

Thursday November 2nd, 2017 Time: 2:00 PM EDT (1:00 PM CDT, 11:00 AM PDT)


Dr. Karen Seto
Yale University

Synthesis of LCLUC studies on urbanization: State of the art, gaps in knowledge, and new directions for remote sensing science

This synthesis project aims to formulate an assessment of the patterns, drivers, and outcomes of global urban LCLUC from 1972 to 2014 by synthesizing existing remote sensing research and published studies from around the world. We aim to assess how the myriad urban remote sensing studies contribute to advancing fundamental and theoretical knowledge of urbanization, sustainability, and the functioning of the Earth system. This synthesis project will examine five key research questions. Question 1. What are the existing and available remotely sensed datasets and analyses on urban LCLUC? Question 2. What are the available change detection algorithms to characterize urban LCLUC and can we develop best practices to guide which change detection algorithms to apply across different geographies, conditions, and applications? Question 3. What are spatial patterns of urban LCLUC and how do they vary across place, time, and economic development levels? Question 4. What are the socioeconomic and policy drivers of urban LCLUC across different world regions, stages of economic development, and land use histories? Question 5. What are the effects of urban LCLUC on other land uses and land covers?



Dr. Peilei Fan
Michigan State University

Urbanization and sustainability under global climate change and transitional economies: synthesis from southeat, east, and north Asia

Transitional economies in Southeast, East, and North Asia (SENA), including Cambodia, Laos PDR, Myanmar, Vietnam, China, Mongolia, and the Asian part of Russia (Siberia), have experienced liberalization, macroeconomic stabilization, restructuring and privatization, and legal and institutional reforms over the past three decades. These countries constitute a region that is significant in both natural and socioeconomic dimensions. Covering a land area of 25.4 million km2, they had a population of 1.54 billion in 2010 and a GDP of $4.91 trillion in 2012. Coupled with the rapid economic development is the urbanization at various but mostly tenacious speeds, which exert tremendous pressure on social, economic, and environmental sustainability, especially under the increasingly visible climate change. Building upon our previous research on urban systems in the region, rich databases of collaborators, and diverse experience and expertise of team members, we set our objective toward synthesizing the data and knowledge on urban sustainability to the socioeconomic transformation and changing climate in transitional economies in SENA. We propose four specific hypotheses to link key socioeconomic and biophysical drivers, especially institutional mechanism unique in transitional economies and global climate change, for the spatiotemporal changes of urbanization and urban sustainability in these countries. We will perform three tasks: Data Integration: We will construct a comprehensive database of LCLUC, socioeconomics, and environmental variables for the 17 cities at multiple spatial and temporal scales from a variety of sources. Data gaps will be identified and a limited effort will be made to collect ground, RS, socioeconomic, and environmental data that are missing, but are critical for Tasks 2 and 3. Knowledge Synthesis: We will first construct quantitative indices for spatial, human, and natural systems of 17 cities. We will perform statistical and modeling analyses to quantify the interactions and feedbacks, thus answering our research questions and to test the hypotheses based on integrated the database, thus generating new knowledge of the co-evolution of LCLUCs, human systems, and natural systems for the urban environments in transitional economies. Forecast Synthesis: We will model and predict the changes of the urban LCLUC, human, and natural systems beyond 2016 with sound scenarios of climate and land cover changes, populations, economic growth, and possible planning and policies. Two workshops will be held in the region to assist us in gathering expert opinions from policymakers and local collaborators on plausible scenarios and to exchange ideas with a larger and broader academic and policy community. This synthesis will not only provide a solid base for further research and education on urbanization and sustainability in the SENA region through the integrated spatial, socioeconomic, and environmental database, but will also contribute to our knowledge on driving forces from human and natural perspectives for urban LCLUC and ecosystems of other regions, especially on those under the mounting pressure of global change and the unique institutional factors of transitioning economies. The project has direct policy implications for cities in transitional economies as it will assist us moving toward urban sustainability under future climate change and growth conditions.


videoWebinar Recording

Tuesday November 28th, 2017 Time: 2:00 PM EST (1:00 PM CST, 11:00 AM PST)


Dr. Daniel Brown
University of Michigan

Large-scale land transactions as drivers of land cover change in sub-Saharan Africa

This project will synthesize available remotely sensed and other information, complemented with targeted new data collection, to investigate the impacts of recent large-scale land transactions. Ours will be the first project to undertake a systematic, quantitative analysis of the impacts from large-scale changes in land tenure on land-cover change and livelihoods, to investigate both the multiple drivers of and the patterns of interactions among these outcomes, and to do so through a rigorous, statistical matching-based causal inference approach. We will make use of existing satellite-based land-cover products and survey-based socioeconomic data in and around the locations of recent large-scale land acquisitions in three countries in Sub-Saharan Africa. 



Dr. Ariane de Bremond
University of Maryland

The global land rush: a socio-environmental synthesis

This project conducts an integrated global synthesis of large-scale land acquisitions (LSLAs), a growing phenomenon in the global South as governments and transnational investors seek to secure access to land in developing countries to produce food, bio-fuels, and non-agricultural commodities. Distant connections between land systems are not new, but rising evidence indicates that such cross-scaled telecoupled socio-economic and environmental interactions as a result of LSLAs have grown stronger, with more rapid feedbacks. The overarching question motivating our research is, What are the processes through which telecoupled LSLAs do or do not result in LCLUC globally, and with what consequences?


videoWebinar Recording

Thursday December 7th, 2017 Time: 2:00 PM EDT (1:00 PM CDT, 11:00 AM PDT)


Dr. Stephen Walsh
University of North Carolina

Synthesis of drivers, patterns, and trajectories of LCLUC in island ecosystems

(1) Primary Island Sites (Hawaiian Islands, Galapagos Islands, Puerto Rico) will be characterized using an assembled social-ecological data set, including, population censuses, tourism data, household surveys, environmental data, local & community infrastructure data, and a blended satellite image stack populated by LANDSAT & MODIS imagery, but also SENTINEL, ASTER, HYPERION & ADVANCED LAND IMAGER data. Existing image archives will be consulted for all available imagery, including the USGS Global Visualization Viewer, USGS EarthExplorer, and NASA Earth Exchange web portals. The nominal periods of study are 1990, 1995, 2000, 2005, 2010 & 2015.

(2) Published information will be distilled from LCLUC case studies for Primary & Secondary Island Sites (Fiji, Azores, Canary Islands, Madagascar, Seychelles, Tahiti) and for islands more generally. LCLUC classification schemes (e.g., USGS National Land Cover Database & NOAA C-CAP Program) will be examined and a suite of LCLUC classes selected that best represent island ecosystems, particularly, those that suggest a transition from natural to human systems (e.g., forest to urban & built-up) and from human to natural systems (e.g., reforestation of abandoned land). Our classification focus will be on urban & built up (low, medium, high intensity), forest (deciduous, evergreen, mixed), cultivated crops, pasture, agroforestry, grasslands, wetlands, open water, beaches, shrub/scrub, and barren. High spatial resolution imagery, e.g., Worldview-2, QuickBird, Google Earth Pro & Google Earth Engine images, will be used for calibration & validation. Using our defined LCLU classes, a suite of change-detections will be generated that represent from-to transitions, with a focus on (1) intensification of urban & built-up, (2) rural to urban, (3) deforestation & reforestation, (4) agricultural extensification & land abandonment, (5) transitions among cultivated crops, pasture & agroforestry, and (6) coastal & interior island development and the transition of beaches, wetlands, forest, shrub/scrub, and open water. We will also track the fragmentation patterns and changes in vegetation & environmental indices. Further, we will construct LCLUC trajectories using derived sequences, focusing on the timing, magnitude, and stability/dynamism of LCLUC relative to the six transitions listed above.

(3) Findings will be synthesized based mainly on models developed for the Primary Sites, informed through statistical functions that link variables and rates of LCLUC documented in the literature. We will develop a Dynamic Systems Model that is sufficiently robust and capable of capturing the main social-ecological variations and dynamics of the drivers of LCLUC on the Primary Sites. We will then test the Model to see how well it represents the variation in the Primary Sites, including performing sensitivity analysis to assess model performance. The Dynamic Systems Model will also be tested through what if scenarios of change. (4) We will expand the degree to which the model can be applied in the Secondary Sites by compiling population censuses, tourism data, household surveys, environmental data, and fused satellite assets to assess LCLUC patterns and the drivers of change. We will secure archival satellite image data as done for our Primary Sites. We will apply the Dynamic Systems Model to the Secondary Sites with necessary modifications and test model performance using sensitivity analysis. (5) As further demonstration of the generalizability of our Dynamic Systems Model, we will develop and test our models using MODIS imagery and globally available & gridded population and socio-economic data maintained by (a) CIESEN, Columbia University, the GRUMP databases, (b) LandScan Global Data Set, Oak Ridge National Laboratory, (c) Global data sets including the Pacific Climate Information System, NASAs Earth Observing System Data & Information System, NOAAs Coastal Change Analysis Program.


Dr. Valerie Thomas
Virginia Tech

Regionally Specific Drivers of Land-Use Transitions and Future Scenarios: A Synthesis Considering the Land Management Influence in the Southeastern US

Land-use and land-cover change are a significant factor in regional and global carbon cycles. Further, land cover dynamics play an important role in the ecological and economic resilience of the landscape to future conditions. Predicting the influence of land-use and land-cover change on global carbon cycling, food and fiber production, and climate requires gridded inputs defining the land-use and land-cover change (Taylor et al. 2009). These gridded land-use inputs describe how the area of the region in different land-use and land-cover classes change over time (Hurtt et al. 2006). In global models, different land-use and land-cover classes are often simulated as a fraction of each model grid-cell, thus allow for sub-grid scale variation in land-use. Two major weaknesses in the regional to global simulations are 1) a lack of regionally specific drivers of land-use transitions and regionally specific scenarios of future land-use, and 2) an explicit consideration of management practices on the carbon cycle and land cover dynamics. Recent work by Hansen et al. (2013) shows a number of regions on the global forested landscape that are experiencing significant changes (mostly forest loss). The southeastern United States stands out as a unique region in terms of land change dynamics. The region is highly productive and largely forested, but with an anthropogenic dominance in the ecosystem. In the southeastern US private owners (from individuals to corporations) control a vast majority of lands (about 90%) and economic factors dominate their decisions. Existing land use studies explicitly address the influence of returns to alternative uses in determining land use choices (Hardie et al. 2000, Lubowski et al. 2002, Wear 2011). We argue that a limiting feature of previous studies has been the treatment of secondary forests as a single land use, in effect lumping passively managed or unmanaged forests with those that are intensively managed. As planted and intensively managed forests have expanded in the southeastern US and now account for a majority of harvests, it seems clear that these managed forests are distinct land uses with very different costs, benefits and service flows when compared with naturally regenerated and unmanaged forests. We propose a synthesis project that integrates four major projects, decades of research on land use and forest management in the Southeastern United States, and NASA remote sensing products (Landsat) and algorithms to develop a regionally specific land-use transition matrix that considers the economic structure of land management and land use decisions under varying scenarios. This matrix will be incorporated into the Global Land-Use Model (GLM) to generate new Land-Use Harmonization datasets, paving the way for future integration of regionally specific land-use decisions into global climate projections. Deliverables for the project include: 1) Landsat-based classification and transitions that include managed forest lands for the Southeast, 2) an integrated assessment of socio- economic drivers of land-use transitions in a management-driven region, 3) regionallyrefined land-use transition matrix derived from an economic conceptual framework that considers management, and 4) harmonization of the regional results with the GLM. Our overall framework could be modified and applied elsewhere to develop regionally appropriate matrices that could feed into the global products.





Dr. Garik Gutman
Manager LCLUC Landsat Program Scientist

Summary: Dr. Garik Gutman is Program Manager for the NASA Land-Cover/Land-Use Change (LCLUC) Program. His current research interests include the use of remote sensing for detecting changes in land cover and land use, and analyzing the impacts of these changes on climate, environment and society. His NASA research program helps to develop the underpinning science and promotes scientific international cooperation through supporting the development of regional science networks over the globe under the GOFC-GOLD international program.



Catherine Nakalembe
LCLUC Program Assistant

Catherine Nakalembe is a Research Assistant Professor at the University of Maryland.