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Nothofagus alessandrii, categorized as Endangered on the IUCN Red List, is an endemic, deciduous tree species of the coastal range of central Chile. We assessed the effects of fire severity, invasion by the exotic fire-prone Pinus radiata, and land-cover composition and configuration of the landscape on the resilience of fragments of N. alessandrii after a mega-fire in 2017. We used remote sensing data to estimate land-use classes and cover, fire severity and invasion cover of P. radiata. We monitored forest composition and structure and post-fire responses of N. alessandrii forests in situ for 2 years after the mega-fire. In the coastal Maule region wildfires have been favoured by intense drought and widespread exotic pine plantations, increasing the ability of fire-adapted invasive species to colonize native forest remnants. Over 85% of N. alessandrii forests were moderately or severely burnt. The propagation and severity of fire was probably amplified by the exotic pines located along the edges of, or inside, the N. alessandrii fragments and the highly flammable pine plantations surrounding these fragments (> 60% of land use is pine plantations). Pinus radiata, a fire-adapted pioneer species, showed strong post-fire recruitment within the N. alessandrii fragments, especially those severely burnt. Positive feedback between climate change (i.e. droughts and heat waves), wildfires and pine invasions is driving N. alessandrii forests into an undesirable and probably irreversible state (i.e. a landscape trap). A large-scale restoration programme to design a diverse and less flammable landscape is needed to avoid the loss of these highly threatened forest ecosystems.

Invasive plant species can pose major threats to biodiversity, ecosystem functioning and services. Satellite based remote sensing has evolved as an important technology to spatially map the occurrence of invasive species in space and time. With the new era of the Sentinel missions, Synthetic Aperture Radar (SAR) and multispectral data are now freely available and repeatedly acquired on a high spatial and temporal resolution for the entire globe. However, the high potential of such sensors for automatic mapping procedures cannot be fully harnessed without sufficient and appropriate reference data for model calibration. Reference data are commonly acquired in field surveys, which however, are often relatively expensive and affected by sampling and observer bias. Moreover, a direct transferability to the remote sensing perspective and scale is difficult. Accordingly, we firstly assess the potential of Unmanned Aerial Vehicles (UAV) for semi-automatic reference data acquisition on species cover of three woody invasive species Pinus radiata, Ulex europaeus and Acacia dealbata occurring in Chile. Secondly, we test the upscaling of the estimated species cover to the spatial scale of Sentinel-1 and Sentinel-2. The proposed workflow includes the visual sampling of respective canopies in UAV orthomosaics and the subsequent spatial extrapolations using MaxEnt with spectral (RGB, Hyperspectral), textural (2D) and canopy structural (3D) predictors derived from UAV-based photogrammetry. These UAV-based maps are then used to train random forest models with multitemporal Sentinel-1 and Sentinel-2 data to map the invasive species cover on large spatial scales. Our results show that the semi-automatic UAV-based mapping of the three invasive species results in accurate predictions. Depending on the predictor combination, the correlation was 0.70, 0.77 and 0.90 for Pinus radiatia, Ulex europaeus, Acacia dealbata, respectively. Among the three species, we observed clear differences in the model performance between the tested photogrammetric predictors and their combinations (spectral, 2D texture or 3D structure). For scaling up the UAV-based estimates to the satellite-scale, the Sentinel-2 data (multispectral) were more important than Sentinel-1 data (SAR). An independent validation revealed that the R2 of the upscaling accounted for 0.78 or higher for all species and RMSE lower than 12%. Our results hence demonstrate that UAV-based reference data acquisitions are a promising alternative to traditional field surveys if the target species are directly identifiable in the UAV data.

1 Vegetation attributes derived from species and plant functional types (PFTs) directly or indirectly drive the carbon (C) cycle in peatlands. However, anthropogenic-based disturbances may alter petland soil-plant interactions and their ability to sequester carbon. Likewise, it is unclear how the soil-plant linkages among different soil C decomposition-based pools and plant attributes vary under disturbance conditions. 2 We aimed to assess how anthropogenic disturbances affect the relationships between aboveground vegetation attributes and belowground C pools in a peatland located in Northern Patagonia, Chile. We further evaluated if attributes derived from PFTs are better suited to predict soil C pools than attributes derived from species. We used structural equation modeling and regression analyses to explore these differences. 3 We found that undisturbed peatland has more soil-plant significant relationships between soil C pools and vegetation attributes, yielding higher predictive accuracies than disturbed areas. The species-based attributes yielded consistently better results predicting soil C pools than PFT-based attributes. However, PFT-based information showed significant interactions with the highly-decomposed C pools in the undisturbed peatland. Likewise, plant height and diversity were only significant with C pools in the undisturbed peatland. 4 We observed that water-logged plant communities have different soil-plant interactions than dryer communities. These differences were observed in both areas but were higher in the disturbed peatland, making it impossible to find meaningful soil-plant relationships across vegetation types and taxa. 5 Our results highlight the importance of accounting for disturbance or management when linking vegetation attributes to soil C pools in peatlands. This implies that up-to-date extensive monitoring of peatland disturbances is needed to accurately monitor soil C attributes at the regional level using vegetation as proxies. We also need to aggregate species into specific plant functional types that hold these soil-plant interactions across landscapes, regions, and disturbances to generalize the soil-plant relationships accurately.

Anthropogenic-based disturbances may alter peatland soil-plant causal associations and their ability to sequester carbon. Likewise, it is unclear how the vegetation attributes are linked with different soil C decomposition-based pools (i.e., live moss, debris, and poorly- to highly-decomposed peat) under grassing and harvesting conditions. Therefore, we aimed to assess the relationships between aboveground vegetation attributes and belowground C pools in a Northern Patagonian peatland of

In the Maipo watershed, situated in central Chile, mining activities are impacting high altitude Andean wetlands through the consumption and exploitation of water and land. As wetlands are vulnerable and particularly susceptible to changes of water supply, alterations and modifications in the hydrological regime have direct effects on their ecophysiological condition and vegetation cover. The aim of this study was to evaluate the potential of Worldview-2 and Sentinel-2 sensors to identify and map Andean wetlands through the use of the one-class classifier Bias support vector machines (BSVM), and then to estimate soil moisture content of the identified wetlands during snow-free summer using partial least square regression. The results obtained in this research showed that the combination of remote sensing data and a small sample of ground reference measurements enables to map Andean high altitude wetlands with high accuracies. BSVM was capable to classify the meadow areas with an overall accuracy of over ∼78% for both sensors. Our results also indicate that it is feasible to map surface soil moisture with optical remote sensing data and simple regression approaches in the examined environment. Surface soil moisture estimates reached r2 values of up to 0.58, and normalized mean square errors of 19% using Sentinel-2 data, while Worldview-2 estimates resulted in non-satisfying results. The presented approach is particularly valuable for monitoring high-mountain wetland areas with limited accessibility such as in the Andes.

Livestock rearing is the most important agricultural activity in global drylands, making forage supply an essential ecosystem service (ES). Most drylands are expected to experience increasing levels of climatic aridity and land-use pressure in the future. As few studies account for combined effects of these global change drivers, we still have a limited understanding of how these drivers jointly shape forage supply. Here, the concept of social-ecological systems (SESs) is useful, as it helps to formalize the complex interrelationships of drivers. Taking advantage of steep gradients of climatic aridity and land-use pressure in West Africa, a crossed space-for-time substitution was applied to capture combined effects of climate and land-use change on forage supply. We have operationalized the SES concept via structural equation modelling, and analysed how drivers directly or indirectly affected forage quantity, quality and their integrated proxy (metabolisable energy yield). Results demonstrate that contemporary dryland SESs are mainly controlled by land-use, which has often been used as a proxy for other variables, such as climatic aridity. Aridity was also directly linked to a higher risk of vegetation degradation, indicating that future drylands will be less resilient to grazing pressures. The importance of land-use drivers for ES provision implies that sustainable grazing management could potentially mitigate detrimental climate change effects. However, model effects mediated by intermediate variables, such as aridity, short-term vegetation dynamics, and weather fluctuations, make it extremely difficult to predict climate change effects on ESs. Integrating structural equation modelling into the well-defined SES concept is thus highly useful to disentangle complex interdependencies of global change drivers in dryland rangelands, and to analyze drivers’ direct and indirect effects on ESs. Our novel approach can thus foster a deeper understanding of patterns and mechanisms driving ecosystem service supply in drylands, which is essential for establishing sustainable management under conditions of global change.

Forest ecosystems are recognized for their large capacity to store carbon (C) in their aboveground and belowground biomass and soil pools. While the distribution of C among ecosystem pools has been extensively studied, less is known about nitrogen (N) and phosphorus (P) pools and how these stocks relate to each other. There is also a need to understand how biotic and abiotic ecosystem properties drive the magnitude and distribution of C-N-P stocks. We studied a temperate rainforest in southern South America to answer the following questions: 1) how are C-N-P total stocks distributed among the different ecosystem pools?, 2) how do C:N, C:P and N:P ratios vary among ecosystem pools?, and 3) which are the main biotic and abiotic drivers of C-N-P stocks? We established 33 circular plots to estimate C, N, and P stocks in different pools (i.e. trees, epiphytes, understory, necromass, leaf litter, and soil) and a set of biotic (e.g., tree density and richness) and abiotic variables (e.g., air temperature, humidity and soil depth). We used structural equation modeling to identify the relative importance of environmental drivers on C-N-P stocks. We found that total ecosystem stocks (mean ± SE) were 1062 ± 58 Mg C ha−1, 28.8 ± 1.5 Mg N ha−1, and 347 ± 12.5 kg P ha−1. The soil was the largest ecosystem pool, containing 68%, 92%, and 73% of the total C, N, and P stocks, respectively. Compared to representative temperate forests, the soil of this forest contains the largest concentrations and stocks of C and N. The low P stock and wide soil C:P and N:P ratios suggest that P may be limiting forest productivity. The ecosystem C-N-P stocks were mainly driven by abiotic properties measured in the study area, however for N stocks, variables such as plant diversity and canopy openness were also relevant. Our results provide evidence about the importance not only of understanding the differences in C, N, and P stocks but also of the factors that drive such differences. This is key to inform conservation policies related to preserving old-growth forests in southern South America, which indeed are facing a rapid land-use change process.

Grasslands are one of the ecosystems that have been strongly affected by anthropogenic impacts. The state-of-the-art in monitoring changes in grassland species composition is to conduct repeated plot-based vegetation surveys that assess the occurrence and cover of plants. These plot-based surveys are typically limited to comparably small areas and the quality of the cover estimates depends strongly on the experience and performance of the surveyors. Here, we investigate the possibility of a semi-automated, image-based method for cover estimates, by analyzing the applicability of very high spatial resolution hyperspectral data to classify grassland species at the level of individuals. This individual-oriented approach is seen as an alternative to community-oriented remote sensing depicting canopy reflectance as the total of mixed species reflectance. An AISA + imaging spectrometer mounted on a scaffold was used to scan 1 m 2 grassland plots and assess the impact of four sources of variation on the predicted species cover: (1) the spatial resolution of the scans, (2) complexity, i.e. species number and structural diversity, (3) the species cover and (4) the share of functional types (graminoids and forbs). Classifications were conducted using a support vector machine classification with a linear kernel, obtaining a median Kappa of ~ 0.8. Species cover estimations reached median r 2 and root mean square errors (RMSE) of ~ 0.6 and ~ 6.2% respectively. We found that the spatial resolution and diversity level (mainly structural diversity) were the most important sources of variation affecting the performance of the proposed approach. A spatial resolution below 1 cm produced relatively good models for estimating species-specific coverages (r 2 = ~ 0.6; RMSE = ~ 7.5%) while predictions using pixel sizes over that threshold failed in this individual-oriented approach (r 2 = ~ 0.17; RMSE = ~ 20.7%). Areas with low inter-species overlap were better suited than areas with frequent inter-species overlap. We conclude that the application of very high resolution hyperspectral remote sensing in environments with low structural heterogeneity is suited for individual-oriented mapping of grassland plant species.

Question Do spatial gradients of plant strategies correspond to patterns of plant traits obtained from a physically based model and hyperspectral imagery? It has been shown before that reflectance can be used to map plant strategies according to the established CSR scheme. So far, these approaches were based on empirical links and lacked transferability. Therefore, we test if physically-based derivations of plant traits may help in finding gradients in traits that are linked to strategies. Location A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. Methods Spatial distributions of plant traits were modelled by adopting an inversion of the PROSAIL radiative transfer model on airborne hyperspectral imagery. The traits are derived from reflectance without making use of field data but only of known links between reflectance and traits. We tested whether previously found patterns in CSR plant strategies were related to the modelled traits. Results The results confirm close relationships between modelled plant traits and C, S and R strategies that were previously found in the field. The modelled plant traits explained different dimensions of the CSR-space. Leaf Area Index (LAI) and the reciprocal of Specific Leaf Area appeared to be good candidates for reproducing CSR scores as community traits using remote sensing. LAI has not been used in previous studies to allocate plant strategies. Conclusions Combining RTMs and the CSR model is a promising approach for establishing a robust link between airborne or spaceborne imagery and plant functioning. The demonstrated potential to map traits with close relation to CSR gradients using only our understanding of the relation between traits and reflectance is a step forward towards an operational use of the CSR model in remote sensing. This article is protected by copyright. All rights reserved.

Biodiversity is considered to be an essential element of the Earth system, driving important ecosystem services. However, the conservation of biodiversity in a quickly changing world is a challenging task which requires cost-efficient and precise monitoring systems. In the present study, the suitability of airborne discrete-return LiDAR data for the mapping of vascular plant species richness within a Sub-Mediterranean second growth native forest ecosystem was examined. The vascular plant richness of four different layers (total, tree, shrub and herb richness) was modeled using twelve LiDAR-derived variables. As species richness values are typically count data, the corresponding asymmetry and heteroscedasticity in the error distribution has to be considered. In this context, we compared the suitability of random forest (RF) and a Generalized Linear Model (GLM) with a negative binomial error distribution. Both models were coupled with a feature selection approach to identify the most relevant LiDAR predictors and keep the models parsimonious. The results of RF and GLM agreed that the three most important predictors for all four layers were altitude above sea level, standard deviation of slope and mean canopy height. This was consistent with the preconception of LiDAR's suitability for estimating species richness, which is its capacity to capture three types of information: micro-topographical, macro-topographical and canopy structural. Generalized Linear Models showed higher performances (r2: 0.66, 0.50, 0.52, 0.50; nRMSE: 16.29%, 19.08%, 17.89%, 21.31% for total, tree, shrub and herb richness respectively) than RF (r2: 0.55, 0.33, 0.45, 0.46; nRMSE: 18.30%, 21.90%, 18.95%, 21.00% for total, tree, shrub and herb richness, respectively). Furthermore, the results of the best GLM were more parsimonious (three predictors) and less biased than the best RF models (twelve predictors). We think that this is due to the mentioned non-symmetric error distribution of the species richness values, which RF is unable to properly capture. From an ecological perspective, the predicted patterns agreed well with the known vegetation composition of the area. We found especially high species numbers at low elevations and along riversides. In these areas, overlapping distributions of thermopile sclerophyllos species, water demanding Valdivian evergreen species and species growing in Nothofagus obliqua forests occur. The three main conclusions of the study are: 1) appropriate model selection is crucial when working with biodiversity count data; 2) the application of RF for data with non-symmetric error distributions is questionable; and 3) structural and topographic information derived from LiDAR data is useful for predicting local plant species richness.

The aerial biomass of Pinus radiata plantations in the Region del Maule, Chile, was estimated from linear models using databases of LiDAR and multispectral LANDSAT ETM+. Six descriptive height variables were obtained from the LiDAR point cloud; the 25%, 50%, 75%, 95% and 100% percentiles and the mean height. Two variables associated with the density of points were also obtained, which relate the returns between fixed weighted intervals calculated as a function of the observed biomass. For multispectral variables we used NDVI, corrected NVDI (NDVIc) and the “Tasseled Cap”components brilliance, greenness and humidity. The results showed coefficients of determination (R2) between 0.801 and 0.814, with errors between 36.07 and 36.11 ton ha-1 for the models generated using height percentiles, and R2 from 0.807 to 0.823 with errors between 36.06 and 36.84 ton ha-1 for transformed LiDAR data. Finally, the stepwise model using all available variables had R2 of 0.821-0.835 with errors of 34.28 - 36.31ton ha-1.Key words:ALS, forest above ground biomass, point cloud density, LiDAR, NDVIc.

Peatlands are key reservoirs of belowground carbon (C) and their monitoring is important to assess the rapid changes in the C cycle caused by climate change and direct anthropogenic impacts. Frequently, information of peatland area and vegetation type estimated by remote sensing has been used along with soil measurements and allometric functions to estimate belowground C stocks. Despite the accuracy of such approaches, there is still the need to find mappable proxies that enhance predictions with remote sensing data while reducing field and laboratory efforts. Therefore, we assessed the use of aboveground vegetation attributes as proxies to predict peatland belowground C stocks. First, the ecological relations between remotely detectable vegetation attributes (i.e. vegetation height, aboveground biomass, species richness and floristic composition of vascular plants) and belowground C stocks were obtained using structural equation modeling (SEM). SEM was formulated using expert knowledge and trained and validated using in-situ information. Second, the SEM latent vectors were spatially mapped using random forests regressions with UAV-based hyperspectral and structural information. Finally, this enabled us to map belowground C stocks using the SEM functions parameterized with the random forests derived maps. This SEM approach resulted in higher accuracies than a direct application of a purely data-driven random forests approach with UAV data, with improvements of r 2 from 0.39 to 0.54, normalized RMSE from 31.33% to 20.24% and bias from −0.73 to 0.05. Our case study showed that: (1) vegetation height, species richness and aboveground biomass are good proxies to map peatland belowground C stocks, as they can be estimated using remote sensing data and hold strong relationships with the belowground C gradient; and (2) SEM is facilitates to incorporate theoretical knowledge in empirical modeling approaches.

Peatlands are ecosystems of great relevance, because they have an important number of ecological functions that provide many services to mankind. However, studies focusing on plant diversity, addressed from the remote sensing perspective, are still scarce in these environments. In the present study, predictions of vascular plant richness and diversity were performed in three anthropogenic peatlands on Chiloé Island, Chile, using free satellite data from the sensors OLI, ASTER, and MSI. Also, we compared the suitability of these sensors using two modeling methods: random forest (RF) and the generalized linear model (GLM). As predictors for the empirical models, we used the spectral bands, vegetation indices and textural metrics. Variable importance was estimated using recursive feature elimination (RFE). Fourteen out of the 17 predictors chosen by RFE were textural metrics, demonstrating the importance of the spatial context to predict species richness and diversity. Non-significant differences were found between the algorithms; however, the GLM models often showed slightly better results than the RF. Predictions obtained by the different satellite sensors did not show significant differences; nevertheless, the best models were obtained with ASTER (richness: R2 = 0.62 and %RMSE = 17.2, diversity: R2 = 0.71 and %RMSE = 20.2, obtained with RF and GLM respectively), followed by OLI and MSI. Diversity obtained higher accuracies than richness; nonetheless, accurate predictions were achieved for both, demonstrating the potential of free satellite data for the prediction of relevant community characteristics in anthropogenic peatland ecosystems.