Introduction

Data products distributed by NASA's 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. Two of those applications published between July and September 2020 are highlighted below.

Vegetation Change in Mount Elgon

In the study "Persistent vegetation greening and browning trends related to natural and human activities in the Mount Elgon ecosystem" published in Remote Sensing, Wanyama and others (2020) use Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetative index (NDVI) and land cover data to monitor long-term greening and browning patterns in East Africa’s Mount Elgon ecosystem.

Science Objectives

Expanding populations lead to a higher demand for food and natural resources, which drive agricultural and other land cover/land use changes (LCLUC) in many regions of the world. In regions with high interannual climate variability and a dependence on rainfed agriculture, this can be a recipe for severe food insecurity. There is a need to better understand the long term impacts of climate change and shorter term impacts of anthropogenic activities (i.e. LCLUC) on vegetation trends (greening/browning) to inform policy decisions that can help conserve the environment and improve communities that depend on it. The Mt. Elgon ecosystem (MEE) straddles the border between Uganda and Kenya and was recently studied by Wanyama and others (2020) to characterize greening/browning trends in vegetation on a multi-temporal scale from 2001–2018.

Instruments Used

The researchers investigated spatio-temporal patterns in NDVI data from the Terra MODIS Vegetation Indices 250 meter (m) 16-day composited product (MOD13Q1.006) at multi-temporal scales (10-day, 16-day, and seasonal) using an integration of Mann-Kendall, Sen’s slope, and the Breaks for Additive Season and Trend (bfast) algorithms. They included precipitation data from the Climate Hazards Group Infrared Precipitation with Stations data product to disentangle human-driven vs. natural vegetation changes. Combined Terra and Aqua MODIS Annual Land Cover Type (MCD12Q1.006) data from 2018 was also used to classify land cover types in the region. The MODIS NDVI data were extracted using NASA’s Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). The authors describe performing a quality assessment of the MODIS NDVI data using the quality layer to exclude poor quality pixels from the analysis.

Major Findings

After applying the integrated Mann-Kendall, Sen’s slope, and bfast algorithms on the time series, the authors found that vegetation in the MEE is highly variable, with greening and browning being observed at all time scales. Using multiple algorithms helped detect different changes. The Mann-Kendall and Sen’s slope approach revealed major changes related to forest gain/loss, while the bfast approach observed more subtle changes, including vegetation degradation. The authors found that certain changes could be attributed to natural (precipitation) and anthropogenic (particularly vegetation to cropland LCLUC) factors. This work characterizes MEE vegetation dynamics, which can inform policy decisions that will both help conserve the environment and protect the livelihoods of the communities dependent on this region for agriculture.

References

Publication Reference

Wanyama, D., Moore, N.J., and Dahlin, K.M., 2020, Persistent vegetation greening and browning trends related to natural and human activities in the Mount Elgon ecosystem: Remote Sensing, v. 12, no. 13, art. no. 2113. doi:10.3390/rs12132113.

Image References

AppEEARS Team, 2020, Application for Extracting and Exploring Analysis Ready Samples (AppEEARS), Ver. 2.48: NASA EOSDIS Land Processes DAAC, accessed November 4, 2020, at https://appeears.earthdatacloud.nasa.gov/.

Didan, K., 2015, MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006: NASA EOSDIS Land Processes DAAC, accessed October 28, 2020, at doi:10.5067/MODIS/MOD13Q1.006.

Fuel Moisture in Fire-Prone Shrublands

In the study "Investigating Live Fuel Moisture Content estimation in fire-prone shrubland from remote sensing using empirical modelling and RTM simulations" published in Remote Sensing, Marion and others (2020) use MODIS and Sentinel-2 data to estimate live fuel moisture for wildfire risk modeling in Central Spain.

Science Objectives

Fuel characteristics such as vegetation moisture content substantially impact wildfire activity. Live Fuel Moisture Content (LFMC) is a key indicator of the flammability of vegetation, fire behavior, severity, and is an important parameter of wildfire models. Accurately estimating LFMC improves operational fire management activities for prevention and suppression of wildfire in addition to emergency responses. A common strategy for assessing wildfire risk is to launch field campaigns that survey fire-prone plant species (indicator species); however, these campaigns are expensive and time-consuming. Marino and others (2020) investigated the potential for using remotely sensed data to estimate LFMC for large-scale fire risk assessments in the Madrid region in Central Spain.

Instruments Used

The researchers utilized the Combined Terra and Aqua MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) Daily L3 Global 500m product (MCD43A4.006), Terra MODIS Surface Reflectance L2G 500m/1000m product (MOD09GA.006), and Sentinel-2 Multi-Spectral Instrument (MSI) top of the atmosphere reflectance to calibrate models for the estimation of LFMC. Spectral indices (SI), including NDVI, Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII), and Normalized Difference Water Index (NDWI) were derived using the MODIS and Sentinel-2 data and used to calibrate the models. Reference data for the study area was gathered from 143 field samples where Cistus ladanifer, an indicator species, was used to determine the LFMC (the percentage of water content of vegetation on a dry-weight basis).

Major Findings

Multiple types of empirical models were leveraged, including linear regression (LR), multivariate linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs), to estimate LFMC from the derived SI. LFMC is also estimated using a radiative transfer model (RTM), and LFMC estimations from empirical and physically-based approaches were compared. LFMC derived from field observations were usually highest in the spring season and lowest in the summer season. Besides the seasonal differences, annual variations were also observed, potentially due to different weather conditions (temperature and precipitation). The results showed a high correlation between most of the remote sensing-derived SI and LFMC data. GAM had the best model fit while NLR had better results in model validation. The remotely sensed approaches generally overestimated the minimum LFMC; however, the authors believe their methods are promising, cost-effective, and provide valuable information for operational wildfire risk management.

References

Publication Reference

Marino, E., Yebra, M., Guillén-Climent, M., Algeet, N., Tomé, J.L., Madrigal, J., Guijarro, M., and Hernando, C., 2020, Investigating Live Fuel Moisture Content estimation in fire-prone shrubland from remote sensing using empirical modelling and RTM simulations: Remote Sensing, v. 12, no. 14, art. no. 2251. doi:10.3390/rs12142251.