Laboratory for
Climatology and Remote Sensing

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:

Tropical montane forests, particularly Andean rainforest, are important ecosystems for regional carbon and water cycles as well as for biological diversity and speciation. Owing to their remoteness, however, ecological key-processes are less understood as in the tropical lowlands. Remote sensing allows modeling of variables related to spatial patterns of carbon stocks and fluxes (e.g., biomass) and ecosystem functioning (e.g., functional leaf traits). However, at a landscape scale most studies conducted so far are based on airborne remote sensing data which is often available only locally and for one time-point. In contrast, multispectral satellites at moderate spectral and spatial resolutions are able to provide spatially continuous and repeated observations. Here, we investigated the effectiveness of Landsat-8 imagery in modeling tropical montane forest biomass, its productivity and selected canopy traits. Topographical, spectral and textural metrics were derived as predictors. To train and validate the models, in-situ data was sampled in 54 permanent plots in forests of southern Ecuador distributed within three study sites at 1000?m, 2000?m and 3000?m a.s.l. We used partial least squares regressions to model and map all response variables. Along the whole elevation gradient biomass and productivity models explained 31%, 43%, 69% and 63% of variance in aboveground biomass, annual wood production, fine litter production and aboveground net primary production, respectively. Regression models of canopy traits measured as community weighted means explained 62%, 78%, 65% and 65% of variance in leaf toughness, specific leaf area, foliar N concentration, and foliar P concentration, respectively. Models at single study sites hardly explained variation in aboveground biomass and the annual wood production indicating that these measures are mainly determined by the change of forest types along with elevation. In contrast, the models of fine litter production and canopy traits explained between 8%–85% in variation depending on the study site. We found spectral metrics, in particular a vegetation index using the red and the green band to provide complementary information to topographical metrics. The model performances for estimating leaf toughness, biochemical canopy traits and related fine litter production all improved when adding spectral information. Our findings therefore revealed that differences in fine litter production and canopy traits in our study area are driven by local changes in vegetation edaphically induced by topography. We conclude that Landsat-derived metrics are useful in modeling fine litter production and biochemical canopy traits, in a topographically and ecologically complex tropical montane forest.

Abstract:

Temperate grasslands play globally an important role, for example, for biodiversity conservation, livestock forage production, and carbon storage. The latter two are primarily controlled by biomass production, which is assumed to decrease with lower amounts and higher variability of precipitation, while increasing air temperature might either foster or suppress biomass production. Additionally, a higher atmospheric CO2 concentration ([CO2]) is supposed to increase biomass productivity either by directly stimulating photosynthesis or indirectly by inducing water savings (CO2 fertilization effect). Consequently, future biomass productivity is controlled by the partially contrasting effects of changing climatic conditions and [CO2], which to date are only marginally understood. This results in high uncertainties of future biomass production and carbon storage estimates. Consequently, this study aims at statistically estimating mid-21st century grassland aboveground biomass (AGB) based on 18 years of data (1998–2015) from a free air carbon enrichment experiment. We found that lower precipitation totals and a higher precipitation variability reduced AGB. Under drier conditions accompanied by increasing air temperature, AGB further decreased. Here AGB under elevated [CO2] was partly even lower compared to AGB under ambient [CO2], probably because elevated [CO2] reduced evaporative cooling of plants, increasing heat stress. This indicates a higher susceptibility of AGB to increased air temperature under future atmospheric [CO2]. Since climate models for Central Europe project increasing air temperature and decreasing total summer precipitation associated with an increasing variability, our results suggest that grassland summer AGB will be reduced in the future, contradicting the widely expected positive yield anomalies from increasing [CO2].

Abstract:

Grünländer der gemäßigten Breiten liefern wichtige Ökosystemdienstleistungen die von der Biomasseproduktion abhängen. Letztere nimmt mit geringeren Niederschlägen und höheren Niederschlagsvariabilitäten ab, während höhere Lufttemperaturen fördernd wirken können. Zusätzlich sollen höhere atmosphärische CO2-Konzentrationen ([CO2]s) die Biomasse durch direkte Stimulation der Photosynthese und Wassereinsparung (CO2-Düngeeffekt) erhöhen. Somit ist die Biomasseproduktivität durch teils gegenläufige Auswirkungen wechselnder klimatischer Bedingungen und [CO2] gesteuert. Die Studie untersucht diese Einflüsse des globalen Wandels auf Grundlage eines Freiluftexperiments zur CO2-Anreicherung (~20% über Umgebungs-[CO2]; 18 J.) um die oberirdische Sommer-Biomasse (AGB) Mitte des 21. Jhdt. statistisch vorherzusagen. Ein informationstheoretisches Screening lieferte die wichtigsten Prädiktoren, basierend auf Lufttemperatur- und Niederschlagsmessungen. Die AGB-Produktion wurde für verschiedene Klimaregime, abgeleitet aus den Beobachtungen des Versuchszeitraums, geschätzt. Wir fanden, dass die zukünftige AGB-Produktion hauptsächlich von der Niederschlagsmenge abhängt, gefolgt von Lufttemperatur und Niederschlagsvariabilität. Variablere Niederschläge reduzierten die AGB und umgekehrt. Die AGB-Produktion unter trockenen Bedingungen verringerte sich mit steigenden Lufttemperaturen weiter. Im Kontrast zu weithin erwarteter Ertragssteigerungen durch erhöhte [CO2]s, führen solche Bedingungen zu AGB-Vorhersagen unter denen der aktuellen AGBs. Da Klimamodelle für Sommer in Mitteleuropa steigende Lufttemperaturen und abnehmende Niederschlagsmengen mit zunehmender Variabilität projizieren, deuten unsere Ergebnisse trotz steigender [CO2]s auf eine reduzierte zukünftige Grünland-Sommer-AGB hin.

Abstract:

The increase in atmospheric greenhouse gas concentrations from anthropogenic activities is the major driver of recent global climate change1. The stimulation of plant photosynthesis due to rising atmospheric carbon dioxide concentrations ([CO2]) is widely assumed to increase the net primary productivity (NPP) of C3 plants—the CO2 fertilization effect (CFE). However, the magnitude and persistence of the CFE under future climates, including more frequent weather extremes, are controversial. Here we use data from 16 years of temperate grassland grown under ‘free-air carbon dioxide enrichment’ conditions to show that the CFE on above-ground biomass is strongest under local average environmental conditions. The observed CFE was reduced or disappeared under wetter, drier and/or hotter conditions when the forcing variable exceeded its intermediate regime. This is in contrast to predictions of an increased CO2 fertilization effect under drier and warmer conditions. Such extreme weather conditions are projected to occur more intensely and frequently under future climate scenarios. Consequently, current biogeochemical models might overestimate the future NPP sink capacity of temperate C3 grasslands and hence underestimate future atmospheric [CO2] increase.

Abstract:

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the appli- cability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation prox- ies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegeta- tion records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predic- tors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were consid- erably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.

Abstract:

Recent work on bryophyte diversity in lowland forests of northern South America has suggested the existence of a new type of cloud forest, the ‘tropical lowland cloud forest’ (LCF). LCF occurs in river valleys in hilly areas with high air humidity and morning fog, and is rich in epiphytes. We explored epiphyte abundance and canopy microclimate of LCF in a lowland area (200–400 m asl) near Saül, central French Guiana. We analyzed the vertical distribution of epiphytic cover and biomass on 48 trees, in LCF and in lowland rain forest (LRF) without fog. Trees in LCF had significantly more epiphytic biomass than in LRF; mean total epiphytic biomass in LCF was about 59 g/m2, and 35 g/m2 in LRF. In all height zones on the trees, total epiphyte cover in LCF exceeded that in LRF, with ca 70 percent mean cover in LCF and ca 15 percent in LRF. During both wet and dry seasons, mean diurnal relative air humidity (RH) was higher in LCF than in LRF, and persistence of high RH after sunrise significantly longer in LCF. We suggest that the prolonged availability of high air humidity in LCF and the additional input of liquid water through fog, enhance epiphyte growth in LCF by shortening the desiccation period and lengthening the period of photosynthetic activity of the plants.