Laboratory for
Climatology and Remote Sensing

Abstract:

Pastoralism is the main land use in the humid tropical Andes of South America. Wide areas of mountain rainforest have been cleared for gaining pastureland. Due to the lack of indigenous useful grasses in the pristine forests, mainly exotic grass species have been used for establishing the pastures. In the Ecuadorian Andes, Axonopus compressus, Melinis minutiflora, Pennisetum clandestinum and Holcus lanatus are common pasture grass species. Their preference for certain microsites resulted in a mosaic of different pasture types, which reflect the differing ecological conditions on the undulating terrain of the mountain slopes. During the last decades, however, another exotic grass species, Setaria sphacelata has widely been introduced which, because of its fast growth on some of the sites could successfully suppress the formerly dominant plant species. With respect to the changing microclimate and cattle stocking rates the present study explored, whether planting Setaria is the best option for the common low-input type of pasture farming in these tropical mountains. In a study over twenty years, the development of four main pasture types, dominated by the above-mentioned grass species was investigated in areas with and without Setaria, and their topographical occurrence on the sloping terrain was analyzed. On forty-eight plots a pairwise (with or without Setaria) comparison of species composition and diversity, biomass production, forage quality and soil properties was performed. Although Setaria grows faster than the other grass species, its productivity was only higher on flat terrain. The nutritive value of the Setaria plots was at best equivalent to that of the former pastures, while species richness was consistently lower. Our results suggest the maintenance of a terrain-adapted diversification of the pastures and in particular the use of Setaria only on flat terrain.

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:

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:

In remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain Radars (MRR) in Cuenca, Ecuador, and Huaraz, Peru, from February 2017 to January 2019. This data allowed for a detailed study on the temporal precipitation characteristics, such as event occurrences and durations at these two locations. Our results showed that the majority of precipitation events had durations of less than 3 h. In Huaraz, precipitation has a distinct annual and diurnal cycle where precipitation in the rainy season occurred predominantly in the afternoon. These annual and diurnal cycles were less pronounced at the site in Cuenca, especially due to increased nocturnal precipitation events compared to Huaraz. Furthermore, we used a fuzzy logic classification of fall velocities and rainfall intensities to distinguish different precipitation types. This classification showed that nightly precipitation at both locations was predominantly stratiform, whereas (thermally induced) convection occurred almost exclusively during the daytime hours

Abstract:

Despite many eorts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RFmodel was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coecient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on dierent rainfall events (correlation coecient up to 0.83; slope = 1.04). The results are promising and unveil a dierent approach to overcome the high attenuation issues inherent to X-band radars.

Abstract:

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two dierent sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and dierent vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two dierent methods. The firstmethod used the RGB images and applied the structure formotion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha 1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized dierence vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation eect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Abstract:

The effects of an increasing moisture on trees of the tropical species-rich mountain rain forest in the South Ecuadorian Andes was investigated, using the daily total water consumption (TWC) and the instantaneous water use ef?ciency (WUE, ratio of photosynthetic CO2 uptake per water loss by transpiration) as eco- physiological indicators. Two canopy and one sub-canopy tree species, (Vismia tomentosa, Clusiaceae, an as of yet unknown Lauracee, and Spirotheca rosea, Bombacaceae) were the experimental objects. Seasonal changes as well as a long-term (18 months) trend of increasing precipitation caused an inverse reaction of the TWC of the trees. Because of a rather unlimited water supply to the trees from a permanently high water content of the soil, transpiration followed mainly the atmospheric demand of water vapor, and increasing moisture hence reduced water loss by transpiration. It was hypothesized that in spite of the reduction in transpiratory water loss photosynthetic carbon acquisition would be not or less affected due to an increase in water use ef?ciency. Concomitant measurements of photosynthetic net CO2 uptake showed the expected increase of WUE in V. tomentosa and S. rosea, but no clear reaction of the Lauracee. Accompanying measurements of stem extension growth con?rmed an undiminished growth of V. tomen- tosa and S. rosea but showed also suspended growth of the Lauracee during the wettest months. While TWC can be continuously monitored with the heat dissipation technique, WUE is determined by leaf porometry in campaigns for which access to the canopy is required. Simultaneous recordings of the gas exchange of leaves at 4 different positions in the crown of one of the experimental trees (V. tomentosa) showed the usability of the trait WUE in combination with the total daily water consumption as indicator set for assessing the response of trees to a subtly changing climate. However, not all tree species appear as likewise useful indicator trees

Abstract:

Land-use and climate change are major threats to biodiversity and ecosystem functions. Most of the current biodiversity monitoring systems are based on periodic records of the populations of a set of threatened or popular ‘?agship’ indicator species. In contrast to the abundance-based monitoring of species, also speci?c indicators of processes and functional interactions in an ecosystem may become targets of a more functional monitoring which can unveil early responses of an ecosystem to environmental changes at different spatial and temporal scales. The contributions of this Special Issue present such functional indicators for assessing and predicting responses to environmental changes of ecosystem functions in a hotspot of tropical biodiversity.

Abstract:

This paper focuses on the analysis of precipita- tion patterns, using a Local Area Weather Radar to collect information about the precipitation distribution in an Andean region of southern Ecuador (cities of Loja, Zamora and Catamayo). 54 representative events were selected to develop daily precipitation maps and to obtain their rele- vant characteristics, which were related to the topography and the season. The results showed that a strong correlation between the areas covered by precipitation (RA coef?cient) and the season exists. In general, humid air masses come from the east (Amazon Basin), but during the main rainy season (December to April), humidity also frequently enters the study region from the west (Paci?c Ocean). The rainy season is characterized by convective precipitation, associated with higher evaporation rates during austral summer. The relatively dry season is formed between May and November, but considerable precipitation amounts are registered, too, due to advective moisture transport from the Amazon Basin, a result of the predominant tropical easterlies carrying the humidity up the eastern escarpment of the Andes, generally following the natural course of the drainage systems.