Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state‐space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat‐derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra‐annual phenological variation to a model based on Landsat‐derived normalized difference vegetation index (NDVI). The best‐performing model accounted for inter‐ and intra‐annual noise in spectral reflectance and translated NDVI to canopy height via Landsat–lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state‐space models to quantify ecological dynamics from time series of space‐borne imagery. State‐space models also provide a statistical approach well‐suited to fusing high‐resolution data, such as airborne lidar, with lower‐resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.
Remote sensing imagery can provide critical information on the magnitude and extent of damage caused by forest pests and pathogens. However, monitoring short‐term changes in deciduous forest condition caused by defoliating insects is challenging and requires approaches that directly account for seasonal vegetation dynamics. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016–2018 gypsy moth (
- Award ID(s):
- 1832210
- NSF-PAR ID:
- 10448144
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Remote Sensing in Ecology and Conservation
- Volume:
- 7
- Issue:
- 4
- ISSN:
- 2056-3485
- Page Range / eLocation ID:
- p. 592-609
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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