Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

Abstract

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 Landsatderived 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.

Remotely sensed vegetation greening along a restoration gradient of a tropical forest, Kibale National Park, Uganda

“…Both NDVI and EVI are low in areas with sparse vegetation cover (deserts), intermediate in shrublands and savannas (with high seasonal variation in grassland ecosystems), and reach the highest values in broadleaved forests (Huete et al, 2011). In the tropics, vegetation indices have been previously used for land‐cover classification and to detect land‐cover dynamics (Hartter et al, 2011; Setiawan et al, 2014; Tucker et al, 1985; Vijith & Dodge‐Wan, 2020; Wanyama et al, 2020), map forest disturbances (Murillo‐Sandoval et al, 2017), predict forest resilience to drought (Verbesselt et al, 2016), monitor natural succession (Caughlin et al, 2021), estimate large‐scale patterns in biomass (Anaya et al, 2009) or primary production (Sjöström et al, 2011), and detect seasonal phenological rhythms and photosynthetic capacity (Brando et al, 2010; Valtonen et al, 2013; Xiao et al, 2006). For example, in the Amazon, the first phase of forest regrowth can be detected as an increase in NDVI (Steininger, 1996).…”

Predicting resilience and stability of early second‐growth forests

“…Productivity— We used the normalized difference vegetation index (NDVI) as an operational variable to measure productivity in early second‐growth forests over time (Caughlin et al., 2020). Using the NDVI to monitor forest recovery is sometimes questionable due to its saturation behavior of the early successional stage, which can potentially introduce bias into the analysis (Huete et al., 2002).…”

“…However, saturation may not be problematic as forests regrow take years to reach biomass concentrations in the saturated range (de Almeida et al., 2020). Forecast models show that saturation occurs after approximately 30 years of forest succession when NDVI approaches the asymptote (Caughlin et al., 2020). Furthermore, NDVI has been commonly used as a proxy of primary productivity and biomass, in which correlations are confirmed by field sample data (Huete et al., 2002; Nemani et al., 2003; Pettorelli et al., 2005; Saatchi et al., 2007; Verbesselt et al., 2016).…”

Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes

“…We recommend that future research incorporate hyperspectral imaging to measure plant functional diversity on regenerating landslides (Asner et al, 2017;Millan & Sanchez-Azofeifa, 2018) to complement our measures of forest height and structure. Multi-spectral imagery could also be used to detect early regrowth (Caughlin et al, 2021;Jayathunga et al, 2019), further constraining estimates of biomass accumulation rates and uncertainty in TMF carbon stocks (Paulick et al, 2017;Spracklen & Righelato, 2014).…”