4.7 Article

Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States

Journal

REMOTE SENSING
Volume 14, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/rs14102320

Keywords

VIIRS-NOAA 20; GEDI Ecosystem LiDAR; vegetation 3D structure; random forest regression models; airborne discrete return LiDAR; accuracy assessment

Funding

  1. NOAA NESDIS JPSS program [NA19NES4320002]
  2. NASA's Terrestrial Ecology, Carbon Cycle Sciences, and Land Cover and Land Use Change Programs [NNX14AD89G, NNX15AE79G, NNX14AM39G]
  3. NASA [80NSSC18K0833]
  4. US Forest Service [21JV11221638133]
  5. University of Maryland [21JV11221638133]
  6. NOAA at the University of Maryland/ESSIC [NA14NES4320003]
  7. NASA [685408, 804993, NNX14AD89G, NNX14AM39G, 677886, NNX15AE79G] Funding Source: Federal RePORTER

Ask authors/readers for more resources

This study aims to achieve consistent and spatially explicit annual monitoring of forest structure in the conterminous United States (CONUS) using NASA's GEDI waveform LiDAR and VIIRS data, as well as high-resolution images. The accuracy of the annual maps was assessed by comparing them with airborne laser scanning (ALS) data, and the results showed high accuracy in canopy height and percent canopy cover. These methods contribute to the multi-decadal analysis of forest structure and its dynamics using satellite observations from sensors such as VIIRS.
Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA's spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013-2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and more subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available