Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called 'regime shifts' or 'Critical transitions', can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation- water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.Peer reviewe

Hyperspectral remote sensing of foliar nitrogen content

http://www.pnas.orgPeer reviewe

Reply toTownsend et al.: Decoupling contributions from canopy structure and leaf optics is critical for remote sensing leaf biochemistry

Non peer reviewe

Canopy spectral invariants for remote sensing and model applications

The concept of canopy spectral invariants expresses the observation that simple algebraic combinations of leaf and canopy spectral transmittance and reflectance become wavelength independent and determine a small set of canopy structure specific variables. This set includes the canopy interceptance, the recollision and the escape probabilities. These variables specify an accurate relationship between the spectral response of a vegetation canopy to the incident solar radiation at the leaf and the canopy scale and allow for a simple and accurate parameterization for the partitioning of the incoming radiation into canopy transmission, reflection and absorption at any wavelength in the solar spectrum. This paper presents a solid theoretical basis for spectral invariant relationships reported in literature with an emphasis on their accuracies in describing the shortwave radiative properties of the three-dimensional vegetation canopies. The analysis of data on leaf and canopy spectral transmittance and reflectance collected during the international field campaign in Flakaliden, Sweden, June 25–July 4, 2002 supports the proposed theory. The results presented here are essential to both modeling and remote sensing communities because they allow the separation of the structural an

Reply to Ollinger et al.:Remote sensing of leaf nitrogen and emergent ecosystem properties

Various physical, chemical, and physiological processes, including canopy structure, impact surface reflectance. Remote sensing aims to derive ecosystem properties and their functional relationships, given these impacts. Ollinger et al. (1) do not distinguish between the forward and inverse problems in radiative transfer and, hence, misrepresent our results (2). The authors also suggest our conclusions are based on a subset of data from ref. 3, which is not the case

Hidden becomes clear:optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Abstract The water table and its dynamics are one of the key variables that control peatland greenhouse gas exchange. Here, we tested the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over intact, restored (previously forestry-drained), and drained (under agriculture) northern peatlands in Finland, Estonia, Sweden, Canada, and the USA. More specifically, we studied the potential and limitations of OPTRAM using water table data from 2018 through 2021, across 53 northern peatland sites, i.e., covering the largest geographical extent used in OPTRAM studies so far. For this, we calculated OPTRAM based on Sentinel-2 data with the Google Earth Engine cloud platform. First, we found that the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance in peatlands. Second, we revealed that the tree cover density is a major factor controlling the sensitivity of OPTRAM to water table dynamics in peatlands. Tree cover density greater than 50% led to a clear decrease in OPTRAM performance. Finally, we demonstrated that the relationship between water table and OPTRAM often disappears when WT deepens (ranging between 0 to −100 cm, depending on the site location). We identified that the water table where OPTRAM ceases to be sensitive to variations is highly site-specific. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density (below 50%) when the water table varies from shallow to moderately deep. Our study makes significant steps towards the broader implementation of optical remote sensing data for monitoring peatlands subsurface moisture conditions over the northern region