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

Quantitative analysis of the links between forest structure and land surface albedo on a global scale

Forests are critical in regulating climate by altering the Earth's surface albedo. Therefore, there is an urgent need to enhance our knowledge about the effects of forest structure on albedo. Here, we present a global assessment of the links between forest structure and albedo at a 1-km spatial resolution using generalized additive models (GAMs). We used remotely sensed data to obtain variables representing forest structure, including forest density, leaf area index, and tree cover, during the peak growing season in 2005 with pure forest pixels that cover similar to 7% of the Earth's surface. Furthermore, we estimated black-sky albedo at a solar zenith angle of 38 degrees using the most recent collection of the moderate resolution imaging spectroradiometer (MODIS; version 6) at shortwave, near-infrared, and visible spectral regions. In addition, for the first time, we mapped the magnitude of the relationship between forest structure and albedo at each pixel with a 0.5-degree spatial resolution. Our results suggested that forest structure may modulate albedo in most of the sub-biomes. The response of shortwave albedo was always positive to the leaf area index and negative to the tree cover (except for deciduous broadleaf forests in mediterranean and temperate regions), while the response to forest density varied across space in 2005. The spatial map affirmed that the links between forest structure and albedo vary over geographical locations. In sum, our study emphasized the importance of forest structure in the surface albedo regulation. This paper provides the first spatially explicit evidence of the magnitude of relationships between forest structure and albedo on a global scale.Peer reviewe

Remotely sensed monitoring of land surface albedo and ecosystem dynamics

Follow dissertation remotely from here on 4.12.2020 12:00 – 16:00: https://aalto.zoom.us/j/61998840053The earth is under unprecedented pressure, which is reflected in rapid ecosystem changes around the globe. Over just the past three decades, the earth has lost over 178 million hectares of its forests. The rapidly growing evidence of the loss of resilience in ecosystems (i.e., recovery from disturbances slows down) due to climate change has become a global concern. Our limited knowledge of ecosystem dynamics and their key parameters, such as albedo, has also hindered our ability to manage ecosystems appropriately. The main aim of this dissertation is to contribute to elucidating ecosystem dynamics by exploiting remotely sensed satellite data. Additionally, this dissertation aims to explore the dynamics of albedo (reflectivity) in response to forest structure (forest density, tree cover, and leaf area index) variations and forest disturbances (fire and drought). To address specific research questions discussed throughout this dissertation, various study sites extending from local to global scales are considered. The results showed that using an appropriate ecosystem state variable that can represent the state of an ecosystem makes it is possible to achieve a reliable and timely evaluation of wetlands or forests state change. To that end, a new remotely sensed index called the modified vegetation water ratio (MVWR) was developed which improved the ability to understand the dynamics of wetlands. A new approach was also developed based on incorporating local spatial autocorrelation. The efficiency of this approach in measuring the state of drought-affected forests was demonstrated. To provide a convenient implementation of the presented approaches, a new R package, "stew", was developed. Investigating the dynamics of forest albedo on disturbances revealed that precipitation and burn severity only weakly explained the temporal dynamics of albedo. In contrast, it was shown that the number of fire events and the leaf area index strongly explained temporal albedo dynamics. According to the results, although fires could lead to abrupt decreases in the temporal dynamics of albedo, droughts caused abrupt increases in the temporal dynamics of albedo. Finally, a global-scale study was conducted to explore the links between forest structure and albedo during the peak growing season. The results demonstrated that forest structure might significantly explain albedo in most of the forests around the world. It was also found that the response of the shortwave albedo (300–5000 nm) to the variations of the leaf area index was always positive. The first map representing the links between forest structure and albedo was provided which highlights the importance of the role of forests in modulating albedo on a global scale. It is expected that the implications of the results of this dissertation contribute to future climate mitigation plans, the monitoring of ecosystem dynamics, and sustainable development in general

A robust approach and analytical tool for identifying early warning signals of forest mortality

Forests are facing unprecedented stressors, evidenced by increases in the rate of forest mortality. Characterizing the state of forest ecosystems and their responses to disturbances remains a complex and crucial task. Existing methodologies have rarely been evaluated in real-world ecosystems due to challenges such as limitations in data availability and analytical techniques. To address these gaps, this study employs remotely sensed spatio-temporal data to identify early warning signals of forest mortality using satellite imagery. Utilizing local spatial autocorrelation methods, specifically local Geary's c and local Moran's I, a robust approach that yielded consistent results across multiple study sites is developed. This approach successfully generated early warning signals based on time-series analysis of local spatial autocorrelation metrics, providing up to a two-year advance notice of impending forest mortality events. The results demonstrated that the proposed approach could outperform previous techniques in reliably generating early warning signals of forest mortality, as shown by significant trend analysis. Additionally, a new R software package, “stew”, is introduced that is designed to facilitate user-friendly spatio-temporal analysis of ecosystem state changes. In summary, this study corroborates the potential of spatio-temporal indicators as valuable tools for predicting climate-induced forest mortality up to two years in advance

Can land-use and land-cover change explain reduced resilience in forests?

Generating signals of reduced resilience in ecosystems is crucial for conservation and management endeavors. However, the practical implications of such systems are still limited due to the lack of high-frequency data and uncertainties associated with predicting complex systems such as ecosystems. This study aims to investigate the potential of time series analysis of remote sensing data in detecting signals of reduced resilience in mangrove forest ecosystems. Using time series analysis of remote sensing images, the resilience of mangrove forests was explored across two distinct study sites. One site (Qeshm Island) has been adversely affected by land-use and land-cover changes, while the other (Gabrik) serves as a reference ecosystem. The study uses data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to quantify three remotely sensed indices: the Normalized Difference Vegetation Index (NDVI), the Modified Normalized Difference Water Index (MNDWI), and the Modified Vegetation Water Ratio (MVWR). In addition, Landsat data has been used to explore temporal alterations in land-use and land-cover change. To identify signals of reduced resilience, trend analyses of indicators such as autocorrelation (acf (1)) and standard deviation (SD) are applied. The findings revealed a notable decrease in resilience, signaled by significant upward trends in NDVI statistical metrics for Qeshm Island (Kendall’s τ of acf (1): 0.50 and SD: 0.90), contrasting with the pattern observed in Gabrik (Kendall’s τ of acf (1): −0.19 and SD: −0.19). These results align with our expectations derived from previous studies. Despite MNDWI significantly indicating reduced resilience in Qeshm Island (Kendall’s τ of acf (1): 0.86 and SD: 0.90), it also signaled decreased resilience in Gabrik (Kendall’s τ of acf (1): 0.79 and SD: 0.90). Moreover, MVWR failed to indicate signals of reduced resilience in both sites, specifically in Qeshm (Kendall’s τ of acf (1): −0.10 and SD: −0.07) and in Gabrik (Kendall’s τ of acf (1): −0.72 and SD: −0.12). These findings may be explained through quantitative analyses of land-use and land-cover change. While Qeshm Island and Gabrik share similarities in climate, geography, and annual rainfall, the analysis of land-use and land-cover change revealed significant differences between the two study areas. Qeshm Island underwent drastic increases in the built-up class by a 64.40% change between 1996 and 2014, whereas the built-up class expanded modestly by a 4.04% change in the Gabrik site. This study contributes to advancing our understanding of ecosystem dynamics. The findings of this study can be integrated with ecosystem management tools to enhance the effectiveness of conservation efforts. This is the first report of the successful application of remote sensing in generating signals of reduced resilience within mangrove forests in the Middle East

Temporal dynamics of albedo and climate in the sparse forests of Zagros

Land surface albedo is an important parameter affecting the climate locally and globally. A synthesis of current studies urgently calls for a better understanding of the impact of climate change on the surface albedo. The Middle East is expected to experience major climatic changes during the coming decades and has already undergone major losses in its vegetation cover. This study explores how climate change related disturbances, such as severe drought and fire events, influence albedo trends in the largest remaining forest area of the Middle East, the Zagros Mountains. We analyzed time series of albedo, Leaf Area Index (LAI), burn severity (dNBR), and the number of fire events all obtained from MODIS satellite images between 2000 and 2016, together with climatic data from 1950 to 2016. The Zagros area is continuously suffering from low precipitation, high temperatures, and evermore-frequent wildfire events. Our large-scale analysis revealed that albedo is linked to precipitation, number of fire events, dNBR,and LAI with the average correlation coefficients of -0.26, -0.50, 0.17, and -0.72, respectively. Using four study sites located in different parts of the Zagros area, we showed disturbances influence albedo differently. Drought condition resulted in a marginal increasing trend in albedo, whereas fire events resulted in a decreasing trend. This article is the first report linking climate change with albedo in Iran. (C) 2019 The Authors. Published by Elsevier B.V.Peer reviewe

Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning

Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Nino Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires.Peer reviewe

Land surface black-sky albedo at a fixed solar zenith angle and its relation to forest structure during peak growing season based on remote sensing data

Satellite data provide the opportunity to explore different land surface properties, such as albedo (reflectivity) and forest structure, for multidisciplinary purposes. We estimated land surface black-sky albedo at shortwave, near-infrared and visible spectral regions at a fixed solar zenith angle (i.e., 38∘) during peak growing season in 2005 on a global scale. In addition, we estimated the links between albedo and forest structure variables including forest density [the number of trees/km2], tree cover [percent], and leaf area index [m2/m2] over pure forest pixels during peak growing season in 2005 on a global scale. We acquired and processed remotely sensed variables from moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite images. This article provides 1) dataset of black-sky albedo at fixed solar zenith angle at a 1-km spatial resolution, 2) comparison between black-sky albedos at fixed solar zenith angle and local noon at a 1-km spatial resolution that are grouped based on forest types with the classes of evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, mixed and woody savannah forests, and also the major biome zones including boreal, mediterranean, temperate and tropical region. 3) the links between black-sky albedo at fixed solar zenith angle and forest structure using generalized additive models at a 0.5-degree spatial resolution during peak growing season in 2005. The pre-processing steps to enhance the accuracy of these datasets include: (1) identifying pure forest pixels, (2) excluding high slope pixels and those covered partially by water in the albedo product using high spatial resolution water (i.e., 30-m spatial resolution) and slope (i.e., 90-m spatial resolution) masks, and (3) using the most recent collection (collection 6) of MODIS satellite images. More details and interpretations of these datasets can be found in Alibakhshi et al. (2020) [1].Peer reviewe