LCLUC 2017 Webinar Series

The 2017 NASA LCLUC Webinar Series feature LCLUC projects focusing on detection and monitoring of land-cover and land-use changes. 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.

The Spring Series features LCLUC projects in mountainous areas.


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.


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.


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

Displaying Presenter:

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.

Displaying Presenter:

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.


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 stan dardized 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 substra tes 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 consis tent, 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 obse rved 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) cen sus 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.



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

Ms. Catherine Nakalembe is a Doctoral Candidate at the University of Maryland and is the LCLUC Program Assistant.