Laboratory for
Climatology and Remote Sensing

Abstract:

Burrowing animals are important ecosystem engineers affecting soil properties, as their burrowing activity leads to the redistribution of nutrients and soil carbon sequestration. The magnitude of these effects depends on the spatial density and depth of such burrows, but a method to derive this type of spatially explicit data is still lacking. In this study, we test the potential of using consumer-oriented UAV RGB imagery to determine the density and depth of holes created by burrowing animals at four study sites along a climate gradient in Chile, by combining UAV data with empirical field plot observations and machine learning techniques. To enhance the limited spectral information in RGB imagery, we derived spatial layers representing vegetation type and height and used landscape textures and diversity to predict hole parameters. Across-site models for hole density generally performed better than those for depth, where the best-performing model was for the invertebrate hole density (R2 = 0.62). The best models at individual study sites were obtained for hole density in the arid climate zone (R2 = 0.75 and 0.68 for invertebrates and vertebrates, respectively). Hole depth models only showed good to fair performance. Regarding predictor importance, the models heavily relied on vegetation height, texture metrics, and diversity indices.

Abstract:

Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund-Potsdam-Jena General Ecosystem Simulator, LPJ-GUESS) to explore the main drivers of community assembly along an elevational gradient.
In the model used here (LPJ-GUESS-NTD, where NTD stands for nutrient-trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake.
The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient-use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient.
Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions.

Abstract:

Fog-driven epiphyte-rich tropical cloud forests were long believed to be restricted to mountainous regions. Recent studies have shown the occurrence of such forest types in the lowlands of French Guiana, where early morning radiation fog was shown to modify the water and energy cycles at the canopy level of the tropical lowland rain forest (Gehrig-Downie et al., 2012; Obregon et al., 2011). Since this newly discovered Tropical Lowland Cloud Forest (TLCF) harbours a unique biodiversity comparable to mountain cloud forests, knowledge of its spatial distribution across tropical lowland forests is of large ecological interest, but so far fully unknown. A prerequisite to detect potential TLCF stands is knowledge on the spatial occurrence of fog/low stratus clouds (FLS) over longer time scales, indicating fog frequency. We address this need on a continental scale by presenting the first spatially-explicit, high-resolution product on nocturnal FLS occurrence over the entire tropical lowland rain forest area of South America. The product is based on a new FLS retrieval scheme that uses brightness temperature (BT) information of night-time satellite images acquired by the Aqua Moderate Imaging Spectror­ adiometer (Aqua MODIS). Since landforms substantially influence small-scale FLS development, we used a dy­ namic threshold technique depending on the spatially variable subpixel information on landform suitability for FLS generation. The product was successfully validated against in-situ visibility measurements combined with a plausibility analysis based on the published but scarce fog observations over the Amazon. The independence of the new algorithm from measured total precipitable water (TPW) is an improvement over existing procedures that are limited to the availability of these ancillary data. The FLS frequency map for the entire tropical lowland forest of South America derived from over 52,000 scenes (18 years from 2003 to 2020 inclusive) indicates that FLS is widespread, though spatially differentiated, throughout the tropical lowland forests of South America, providing suitable environmental conditions for the TLCF. This knowledge on the spatial distribution of potential TLCF is of major importance for conservation efforts. The new FLS scheme is applicable to all tropical lowland forests and can therefore support global conservation efforts of the valuable TLCF ecosystems.

Abstract:

The French Mediterranean island of Corsica is already today confronted with a clear tendency towards water shortage, leading not only to socio-economical, but also to ecological problems. A potential, but not very widespread source of water is the presence of near-ground clouds, mostly fog. In this study, we investigate fog-low stratus (FLS) frequencies in Corsica, derived from a data set of Meteosat Second Generation SEVIRI, whereby a distinction between fog and low stratus is hardly feasible using remote sensing data. The FLS frequency was studied with respect to its interaction with distinct locally-generated wind and its dependence on the planetary boundary layer height (PBLH) obtained by ERA5 reanalysis (the fifth generation of the European Centre for Medium-Range Weather Forecasts, ECMWF). Results show that radiation FLS is formed in coastal areas at sunrise, with low PBLH. On the other hand, in the interior of the island at sunset, a maximum of advection FLS is formed, fostered by locally-generated and related transport of moisture. On the east side of the island, FLS frequency is lower throughout the year due to frequent lee situations. This situation is reinforced by reduced synoptic moisture transport by westerly winds, so that westerly exposed slopes benefit from moisture input by FLS formation.

Abstract:

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems

Abstract:

For climate models that use paleo-environment data to predict future climate change, tree-ring isotope variations are one important archive for the reconstruction of paleo-hydrological conditions. Due to the rather complicated pathway of water, starting from precipitation until its uptake by trees and the final incorporation of its components into tree-ring cellulose, a closer inspection of seasonal variations of tree water uptake is important. In this study, branch and needle samples of two pine species (Pinus pinaster and Pinus nigra subsp. laricio) and several water compartments (precipitation, creek, soil) were sampled over a two-year period and analyzed for the temporal variations of their oxygen and hydrogen stable isotope ratios (δ18O and δ2H) at five sites over an elevation gradient from sea level to around 1600 m a.s.l. on the Mediterranean island of Corsica (France). A new model was established to disentangle temporal relationships of source water uptake of trees. It uses a calculation method that incorporates the two processes mostly expected to affect source water composition: mixing of waters and evaporation. The model results showed that the temporal offset from precipitation to water uptake is not constant and varies with elevation and season. Overall, seasonal source water origin was shown to be dominated by precipitation from autumn and spring. While autumn precipitation was a more important water source for trees growing at mid- (~800–1000 m a.s.l) and high-elevation (~1600 m a.s.l.) sites, trees at coastal sites mostly took up water from late winter and spring. These findings show that predicted decreases in precipitation amounts during the wet season in the Mediterranean can have strong impacts on water availability for pine trees, especially at higher elevations.

Abstract:

Cloud top height (CTH) plays an important role in the Earth’s radiation budget and thus in climate change. However, accurate CTH retrieval based on satellite data remains a challenge when using currently widespread methods based on passive remote sensing satellite data, particularly over the Arctic Ocean. It is well known that using geometric correlation between CTH and cloud-shadow distance along the normal direction of sunlight, cloud-edge top height (CETH) can be determined. However, this cloud-shadow method (CSM) exhibited larger CTH errors due to low-resolution weather satellite data of the past. The aim of the paper is to analyze the effect of higher-resolution images (MODIS with 250-m resolution) on CSM accuracy. We present a method in which CETH is first retrieved using the MODIS data, and to calculate CTH in the center of cloud region by combining the calculated CETH and cloud top brightness temperature using an environmental temperature lapse rate method. We validated 14 cases over the Arctic Ocean by the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) CTH lidar product and another two cases by radiosonde data. CSM revealed better per- formance (mean difference is 0.45 km and standard deviation is 0.92 km) compared to the operational MODIS CTH product (mean difference is 0.78 km and standard deviation is 1.78 km), when validated by the CALIPSO product. When validated by sounding data, CSM also performed better than the MODIS CTH product: the CSM CTH errors are 0.41 and 0.52 km in two sounding cases, while the corresponding MODIS CTH errors are 1.10 and 1.07 km. The CSM technique especially showed better performance for double-deck cloud systems. We conclude that further improvements in CTH accuracy based on CSM can be achieved by mainly using higher- resolution satellite imagery.

Abstract:

A new satellite-based algorithm for rainfall retrieval in high spatio-temporal resolution fo Ecuador is presented. The algorithm relies on the precipitation information from the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) and infrared (IR) data from the Geostationary Operational Environmental Satellite-16 (GOES-16). It wa developed to (i) classify the rainfall area (ii) assign the rainfall rate. In each step, we selected the most important predictors and hyperparameter tuning parameters monthly. Between 19 April 2017 and 30 November 2017, brightness temperature derived from the GOES-16 IR channels and ancillary geo-information were trained with microwave-only IMERG-V06 using random forest (RF). Validation was done against independent microwave-only IMERG-V06 information not used for training. The validation results showed the new rainfall retrieva technique (multispectral) outperforms the IR-only IMERG rainfall product. This offers using the multispectral IR data can improve the retrieval performance compared to single-spectrum IR approaches. The standard verification scored a median Heidke skill score of ~0.6 for the rain area delineation and R between ~0.5 and ~0.62 for the rainfall rate assignment, indicating uncertainties for Andes’s high elevation. Comparison of RF rainfall rates in 2 km2 resolution with daily rain gauge measurements reveals the correlation of R = ~0.33.

Abstract:

Tropical Mountain Forest (TMF) provides important ecological func- tions like evapotranspiration (ET) that supplies moisture and energy to the atmosphere. ET observations are scarce and difficult to accomplish particularly in areas of high heterogeneity where TMF are. Remote sensing (RS) allows to quantify and to determine ET spatial variation at the landscape level. Detail imaginary improves high spatial variability retrieval. Thought the greater detail intro- duces cast shadows by trees which hamper image interpretation. The objective of this study is to characterize ET estimation for the TMF of the southern Ecuadorian Andes by combining meteorologi- cal data with high-resolution satellite images. Shadows from high resolution images were masked out by applying focal statistics. The analysis included two meteorological periods typical of the area; a wet period when rain prevails and a dry period when precipitation is more sporadic. The reference evapotranspiration (ET0) was calcu- lated using the FAO-Penman Montheid method by applying data obtained from an automatic weather station. The enhanced vege- tation index (EVI) was derived from 2 m resolution WorldView2 satellite images. Results showed a lower ET mean value during the wet period: 1.54 mm day−1 compared to 2.37 mm day−1 . Two forest types, differentiated from its structural composition and topogra- phical position (ravine and ridge), marked ET spatial variation. Ravine forest that has a more dense and closed canopy showed higher ET values for both meteorological conditions. A comparison between ET estimations and ET field measurements from a scintillometer device showed a good agreement (coefficient of correlation r = 0.89) that proves the validity of the method. This study demonstrates that the application of high spatial resolution improves ET estimation in TMF especially when shadows are removed. Also, emphasizes the importance of analysing spatial heterogeneity to properly assess ecosystem water flux terms.

Abstract:

Drastic declines in insect populations are a vital concern worldwide. Despite widespread insect monitoring, the significant gaps in the literature must be addressed. Future monitoring techniques must be systematic and global. Advanced technologies and computer solutions are needed. We provide here a review of relevant works to show the high potential for solving the aforementioned problems. Major historical and modern methods of insect monitoring are considered. All major radar solutions are carefully reviewed. Insect monitoring with radar is a well established technique, but it is still a fast-growing topic. The paper provides an updated classification of insect radar sets. Three main groups of insect radar solutions are distinguished: scanning, vertical-looking, and harmonic. Pulsed radar sets are utilized for all three groups, while frequency-modulated continuous-wave (FMCW) systems are applied only for vertical-looking and harmonic insect radar solutions. This work proves the high potential of radar entomology based on the growing research interest, along with the emerging novel setups, compact devices, and data processing approaches. The review exposes promising insect monitoring solutions using compact radar instruments. The proposed compact and resource-effective setups can be very beneficial for systematic insect monitoring.

Abstract:

The formation and development of fog and low stratus clouds (FLS) depend on meteorological and land surface conditions and their interactions with each other. While analyses of temporal and spatial patterns of FLS in Europe exist, the interactions between FLS determinants underlying them have not been studied explicitly and quantitatively at a continental scale yet. In this study, a state-of-the-art machine learning technique is applied to model FLS occurrence over continental Europe, using meteorological and land surface parameters from geostationary satellite and reanalysis data. Spatially explicit model units are created to test for spatial and seasonal differences in model performance and FLS sensitivities to changes in predictors, and effects of different data preprocessing procedures are evaluated. The statistical models show good performance in predicting FLS occurrence during validation, with R2>0.9 especially in winter high pressure situations.The predictive skill of the models seems to be dependent on data availability, data preprocessing, time period, and geographic characteristics. It is shown that atmospheric proxies are more important determinants of FLS presence than surface characteristics, in particular mean sea level pressure, near-surface wind speed and evapotranspiration are crucial, together with FLS occurrence on the previous day. Higher wind speeds, higher land surface temperatures and higher evapotranspiration tend to be negatively related to FLS. Spatial patterns of feature importance show the dominant influence of mean sea level pressure on FLS occurrence throughout the central European domain. When only high pressure situations are considered, wind speed (in the western study region) and evapotranspiration (in the eastern study region) gain importance, highlighting the influence of moisture advection on FLS occurrence in the western parts of the central European domain. This study shows that FLS occurrence can be accurately modeled using machine learning techniques in large spatial domains based on meteorological and land surface predictors. The statistical models used in this study provide a novel analysis tool for investigating empirical relationships in the FLS – land surface system and possibly infer processes.