Introduction

Data products from the Land Processes Distributed Active Archive Center (LP DAAC) are used in many different applications. They play an important role in modeling, help to detect changes to the landscape, and are a way to assess ecosystem variables, to name a few. Three of those applications, published between July and September 2016, are highlighted below.

Landslide Detection in the French Alps

This study combines aerial imagery and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data to track landslide movement and deformation over time in glacio-lacustrine deposits.

Science Objectives

This study, described in the paper "A new technique for the detection of large scale landslides in glacio-lacustrine deposits using image correlation based upon aerial imagery—A case study from the French Alps" published in the International Journal of Applied Earth Observation and Geoinformation, looked at the Harmaliere landslide in the French Alps. In March 1981, the Harmaliere landslide occurred near the village of Sinard in the Drac Valley of the French Alps, shown in the image above. Fernandez and Whitworth’s (2016) study uses software to correlate aerial images, and then uses those images to detect changes over time in the landslide area.

Instruments Used

Researchers used aerial photography from 2001 to 2003, ASTER Global Digital Elevation Model (GDEM) Version 2, and Global Positioning System (GPS) ground control data. The authors chose to use ASTER GDEM data to correct for high relief related distortions and topographic effects the software found while matching aerial images.

Major Findings

Fernandez and Whitworth (2016) report movement and deformation, particularly in the western flank of the landslide, which had not been previously identified. Their results can potentially show patterns for future movement in the Harmaliere landslide area. The authors state that the method used in their study can also be applied to other areas that have been captured with aerial imagery for a long time span and contain good ground control data.

References

Publication Reference

Fernandez, P., and Whitworth M., 2016, A new technique for the detection of large scale landslides in glacio-lacustrine deposits using image correlation based upon aerial imagery—A case study from the French Alps: International Journal of Applied Earth Observation and Geoinformation, v. 52, p. 1–11. doi:10.1016/j.jag.2016.05.002

Image References

Granule IDs

• ASTGTM2_N45E005_dem
• ASTGTM2_N44E005_dem

DOI

10.5067/ASTER/ASTGTM.002

Mapping Floral Resources for Beekeepers

Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data powers a web-based tool that tracks eucalyptus flowering patterns to help Australian beekeepers manage hive placement.

Science Objectives

This paper, "A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data" published in Environmental Modelling & Software, describes a free web-based application, BeeBox, for beekeepers and bee enthusiasts in the Australian states of Victoria, New South Wales, Queensland, and South Australia. BeeBox provides users with preprocessed Terra MODIS enhanced vegetation index (EVI) (MOD13Q1) data to show growing and flowering patterns of eucalyptus trees, a major source of food for bees. Information from this application can be used to predict where and when flowering events will occur, which can aid beekeepers in determining where to place their hives.

Instruments Used

Arundel and others (2016) use Terra MODIS data from February 2000 to present day as their main dataset due to MODIS' consistent coverage. The authors specifically chose to use the 16-day composited data because it contains less cloud coverage as well as atmospheric contamination and requires less storage for their application. BeeBox allows beekeepers to identify a specific area of interest and then provides them with averaged EVI information of the area through time.

Major Findings

The authors conducted a case study to demonstrate the application by using the Terra MODIS EVI layer to observe flowering patterns of the Eucalyptus tricarpa (Red Ironbark) species. The authors found there was a strong flowering pattern and honey production during 2000, 2004, and 2006. Conversely, they observed weaker incidents in 2002 and 2003. Their results correlate with the flowering interval that Red Ironbark typically experiences. In the future the authors see the BeeBox expanding to provide global coverage and also to provide information for other industries.

References

Publication Reference

Arundel, J., Winter, S., Gui, G., and Keatley, M., 2016, A web-based application for beekeepers to visualise patterns of growth in floral resources using MODIS data: Environmental Modelling & Software, v. 83, p. 116–125. doi:10.1016/j.envsoft.2016.05.010

Image References

Granule IDs

• MOD13Q1.A2016241.h29v12.006.2016263150734
• MOD13Q1.A2016241.h30v12.006.2016263150730

DOI

10.5067/MODIS/MOD13Q1.006

Boundary source:

Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects

Monitoring Mangrove Health in the Sundarbans

MODIS time series products are used to evaluate forest canopy, productivity, and biomass trends in the world's largest mangrove ecosystem.

Science Objectives

The world’s largest contiguous mangrove forest is the Sundarbans forest, located along the coast of Bangladesh and India. While portions of the Sundarbans are a National Park, a wildlife sanctuary, and a forest reserve, the area is still under constant threat from both environmental forces and anthropogenic activities. In their paper "Examining the ecosystem health and sustainability of the world's largest mangrove forest using multi-temporal MODIS products" published in Science of the Total Environment, Ishtiaque and others (2016) are using four Terra MODIS land data products distributed by LP DAAC, and a MODIS evapotranspiration product, as parameters to observe ecological changes in the Sundarbans forest from 2000 to 2010.

Instruments Used

The authors use Terra MODIS Yearly Vegetation Continuous Fields data (MOD44B), shown in the image above, to identify the percentage of tree cover (PTC) in the forest. The authors use several other Terra MODIS data products as well. To observe the biomass richness of the forest the authors use EVI from the monthly vegetation indices product (MOD13A3). The yearly Net Primary Production (NPP) (MOD17A3) is used to observe the net amount of carbon added to the plant biomass in the forest. The Leaf Area Index (LAI) data product (MOD15A2) is used to measure the amount of canopy foliage in the forest. The authors observe the Sundarbans overall using these parameters, and they also create annual image stacks of each product to perform a pixel-based time-series trend analysis of each product.

Major Findings

Based on these Terra MODIS data parameters the authors find that overall the Sundarbans are degrading at a slow, but steady rate, with decreases in PTC, EVI, NPP and LAI. They find the eastern side of the forest is experiencing higher rates of degradation, with certain parcels degrading more rapidly than the rest of the forest. Their study agrees with a previous study that used radar imagery in the area. The authors state that results from this study can be used to aid management agencies and policy makers in providing better forest management in the future to help protect the Sundarbans. The authors also believe that the parameters used in this study can be used to study and monitor the health of other forest ecosystems around the world.

References

Publication Reference

Ishtiaque, A., Myint, S., and Wang, C., 2016, Examining the ecosystem health and sustainability of the world's largest mangrove forest using multi-temporal MODIS products: Science of the Total Environment, v. 569–570, p. 1241–1254. doi:10.1016/j.scitotenv.2016.06.200

Image References

Granule ID

• MOD44B.A2010065.h26v06.051.2014313033848

Boundary Source

Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects

Author's note: This paper used MOD17A3 data, which has since been replaced by MOD17A3H and MOD15A2, which has since been replaced by MOD15A2H.