Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI1310,1720,730: R2 = 0.76, [0.64,0.86]; RMSE (%) = 9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.This research has been funded by the BIOSPEC project “Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change” (http://www.lineas.cchs.csic.es/biospec) (CGL2008-02301/CLI, Ministry of Science and Innovation) and the FLUXPEC project “Monitoring changes in water and carbon fluxes from remote and proximal sensing in a Mediterranean dehesa ecosystem” (http://www.lineas.cchs.csic.es/fluxpec) (CGL2012-34383, Ministry of Economy and Competitiveness). Pacheco-Labrador, J. was sponsored by a JAE-Predoc grant (CSIC)Peer reviewe

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Multi-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands

This paper presents a method for individual tree crown extraction and characterisation from a canopy surface model (CSM). The method is based on a conventional algorithm used for localising LM on a smoothed version of the CSM and subsequently for modelling the tree crowns around each maximum at the plot level. The novelty of the approach lies in the introduction of controls on both the degree of CSM filtering and the shape of elliptic crowns, in addition to a multi-filtering level crown fusion approach to balance omission and commission errors. The algorithm derives the total tree height and the mean crown diameter from the elliptic tree crowns generated. The method was tested and validated on a mountainous forested area mainly covered by mature and even-aged black pine (Pinus nigra ssp. nigra [Arn.]) stands. Mean stem detection per plot, using this method, was 73.97%. Algorithm performance was affected slightly by both stand density and heterogeneity (i.e. tree diameter classes' distribution). The total tree height and the mean crown diameter were estimated with root mean squared error values of 1.83 m and 1.48 m respectively. Tree heights were slightly underestimated in flat areas and overestimated on slopes. The average crown diameter was underestimated by 17.46% on average. (C) 2011 Elsevier B.V. All rights reserved

Airborne laser bathymetry for documentation of submerged archaeological sites in shallow water

Knowledge of underwater topography is essential to the understanding of the organisation and distribution of archaeological sites along and in water bodies. Special attention has to be paid to intertidal and inshore zones where, due to sea-level rise, coastlines have changed and many former coastal sites are now submerged in shallow water. Mapping the detailed inshore topography is therefore important to reconstruct former coastlines, identify sunken archaeological structures and locate potential former harbour sites. However, until recently archaeology has lacked suitable methods to provide the required topographical data of shallow underwater bodies. Our research shows that airborne topo-bathymetric laser scanner systems are able to measure surfaces above and below the water table over large areas in high detail using very short and narrow green laser pulses, even revealing sunken archaeological structures in shallow water. Using an airborne laser scanner operating at a wavelength in the green visible spectrum (532 nm) two case study areas in different environmental settings (Kolone, Croatia, with clear sea water; Lake Keutschach, Austria, with turbid water) were scanned. In both cases, a digital model of the underwater topography with a planimetric resolution of a few decimeters was measured. While in the clear waters of Kolone penetration depth was up to 11 meters, turbid Lake Keutschach allowed only to document the upper 1.6 meters of its underwater topography. Our results demonstrate the potential of this technique to map submerged archaeological structures over large areas in high detail providing the possibility for systematic, large scale archaeological investigation of this environment

Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana

The Shuttle Radar Topography Mission (SRTM) has produced the most accurate nearly global elevation dataset to date. Over vegetated areas, the measured SRTM elevations are the result of a complex interaction between radar waves and tree crowns. In this study, waveforms acquired by the Geoscience Laser Altimeter System (GLAS) were combined with SRTM elevations to discriminate the five forest landscape types (LTs) in French Guiana. Two differences were calculated: (1) penetration depth, defined as the GLAS highest elevations minus the SRTM elevations, and (2) the GLAS centroid elevations minus the SRTM elevations. The results show that these differences were similar for the five LTs, and they increased as a function of the GLAS canopy height and of the SRTM roughness index. Next, a Random Forest (RF) classifier was used to analyze the coupling potential of GLAS and SRTM in the discrimination of forest landscape types in French Guiana. The parameters used in the RF classification were the GLAS canopy height, the SRTM roughness index, the difference between the GLAS highest elevations and the SRTM elevations and the difference between the GLAS centroid elevations and the SRTM elevations. Discrimination of the five forest landscape types in French Guiana was possible, with an overall classification accuracy of 81.3% and a kappa coefficient of 0.75. All forest LTs were well classified with an accuracy varying from 78.4% to 97.5%. Finally, differences of near coincident GLAS waveforms, one from the wet season and one from the dry season, were analyzed. The results showed that the open forest LT (LT12), in some locations, contains trees that lose leaves during the dry season. These trees allow LT12 to be easily discriminated from the other LTs that retain their leaves using the following three criteria: (1) difference between the GLAS centroid elevations and the SRTM elevations, (2) ratio of top energy in the wet season to top energy in the dry season, or (3) ratio of ground energy in the wet season to ground energy in the dry season

The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest

The estimation of species composition and species-specific stem volumes are critical components of many forest inventories. The use of airborne laser scanning with multiple spectral channels may prove instrumental for the cost-efficient retrieval of these forest variables. In this study, we scanned a boreal forest using two channels: 532 nm (green) and 1064 nm (near infrared). The data was used in a two-step methodology to (1) classify species, and (2) estimate species-specific stem volume at the level of individual tree crowns. The classification of pines, spruces and broadleaves involved linear discriminant analysis (LDA) and resulted in an overall accuracy of 91.1 % at the level of individual trees. For the estimation of stem volume, we employed species-specific k-nearest neighbors models and evaluated the performance at the plot level for 256 field plots located in central Sweden. This resulted in root-mean-square errors (RMSE) of 36 m3/ha (16 %) for total volume, 40 m3/ha (27 %) for pine volume, 32 m3/ha (48 %) for spruce volume, and 13 m3/ha (87 %) for broadleaf volume. We also simulated the use of a monospectral near infrared (NIR) scanner by excluding features based on the green channel. This resulted in lower overall accuracy for the species classification (86.8 %) and an RMSE of 41 m3/ha (18 %) for the estimation of total stem volume. The largest difference when only the NIR channel was used was the difficulty to accurately identify broadleaves and estimate broadleaf stem volume. When excluding the green channel, RMSE for broadleaved volume increased from 13 to 26 m3/ha. The study thus demonstrates the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. In addition, we investigated how tree height influences the results where shorter trees were found to be more difficult to classify correctly

Automatic detection to inventory road slopes using open LiDAR point clouds

The transport infrastructure of a country facilitates the development and growth of its economy and improves the quality of life of its inhabitants. Increasing its resilience to different types of risks to improve performance is becoming more important. In the current context of climate change, natural hazards are more severe and frequent. In this article, we focus on rockfall as a natural hazard for roads that occurs in small areas in the vicinity of natural or cut slopes, causing road safety problems by invading part of the road. This article aims to inventory the slopes along the road, identifying the area of the road which would be invaded in case of a rockfall. A methodology divided into two blocks is proposed. First, for slope detection and inventory, an algorithm is developed based on open LiDAR point clouds analysis. The second block consists of estimating the invaded road area if a rockfall occurs on each of the inventoried slopes, using a combination of RockGIS software and the Monte Carlo method. The methodology was applied in five case studies: three sections on motorways and two sections on national roads. The results obtained for slope detection show higher rates in the case studies analyzing motorways, with a precision of 100%, a recovery rate of greater than 93.4%, and an F1 score of greater than 0.96. The results in the invaded area of the road show that 11 slopes would cause a total cut of the motorway in one of the directions if a rockfall occurs. These results are useful for infrastructure managers to remotely obtain an inventory of road slopes and know which of them would affect road safety. Also, the results can serve as input for the Intelligent Transportation System and allow the exchange of information under the Building Information Model approach.Ministerio de Ciencia, Innovación y Universidades | Ref. PID2019-108816RB-I00Ministerio de Ciencia, Innovación y Universidades | Ref. PRE2020-096222European Commission | Ref. H2020, n. 95533

Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds

Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at [email protected]