Laboratory for
Climatology and Remote Sensing

Abstract:

The Atacama Desert is considered as one of the driest places on Earth. At the coastline, however, small-scale fog oases harbor a specialized vegetation and fauna, living from moisture by fog, which is used by humans to feed water demands of industrial projects. To date, knowledge about fog and low stratus (FLS) clouds as well as their physical properties is limited in that only local observations or spatial products from satellites with coarse resolutions are available generally failing to capture local patterns resulting from the complex topography. Consequently, we provide the first climatology of FLS with 30m spatial resolution based on over 400 Landsat scenes acquired since 1986. The new product provides valuable estimates of FLS optical and micro-physical properties. FLS over the Pacific Ocean featured cloud optical depth values around 13.5 declining over land to 4.2. Effective radii were around 5.3 μm. Liquid water path was between 71.0 − gm 2 over the Ocean and 14.9 − gm 2 over land surfaces. The climatologies of the new Landsat product were successfully validated against those of the MODIS cloud property product over homogeneous surfaces. Over areas with heterogeneous topographies, the new product outperforms existing ones with coarse spatial resolutions if compared against in situ measurements. This shows the general need for cloud products with high spatial resolutions in areas where the development of small scale clouds is favored e.g., by a complex topography leading to systematical biases in existing retrievals.

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:

The Atacama Desert is well known for the high occurrence of large-scale fog (spatial extents: hundreds of kilometers) emerging as low stratus (LST) decks over the Pacific Ocean. By contrast, the small-scale and heterogeneous occurrence of small-scale fog (hundreds of meters) particularly during summers is widely unconsidered. However, these events are important for the local vegetation and particularly for the biological soil crusts (BSC) that are widely distributed in this extreme ecosystem. Consequently, a case study in a typical fog oasis in the Pan de Azúcar National Park was conducted to test the feasibility combining field measurements, drone profiling, remote sensing and numerical modeling (i) to investigate fog-type specific differences regarding dynamics, physical properties and formation, (ii) to test the applicability of remote sensing technology for fog monitoring based on existing low-resolution and a proposed new high-resolution product and (iii) to estimate the related fog water input to BSCs. Two types of fog were observed. The well-known fog/LST deck emerging from the Pacific Ocean with high water path and large spatial extent was the first type. Fog of the second type was patchier, small-scale and not necessarily connected to the LST over the ocean. Instead, fog formation of the second type was related to thermal breeze systems, which produced shallow clouds containing less water than those of type 1. In general, such small-scale fog events were not captured well by existing remote sensing products but could be detected with the proposed new high-resolution product which provided promising results. Both fog types were important water resources for the BSCs, with approximately 8% to 24% of the fog water flux available to the BSCs at the surface. The results indicated the feasibility of the proposed methods’ pool to estimate the water budget of BSCs with a high spatial resolution in the future.

Abstract:

Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error <8 mm·month?1 and annual deviation <17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes.

Abstract:

In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and the provisioning of ecosystem services. To find sustainable land use options adapted to the local situation, a profound knowledge of the long-term spatiotemporal patterns of land cover change and its drivers is necessary, but hitherto lacking. The complex topography and the high cloud frequency make the use of remote sensing in this area a challenge. To deal with these conditions, we pursued specific pre-processing steps before classifying five Landsat scenes from 1975 to 2001. Then, we quantified land cover changes and habitat fragmentation, and we investigated landscape changes in relation to key spatial elements (altitude, slope, and distance from roads). Good classification results were obtained with overall accuracies ranging from 94.5% to 98.5% and Kappa statistics between 0.75 and 0.98. Forest was strongly fragmented due to the rapid expansion of the arable frontier and the even more rapid invasion by bracken. Unexpectedly, more bracken-infested areas were converted to pastures than vice versa, a practice that could alleviate pressure on forests if promoted. Road proximity was the most important spatial element determining forest loss, while for bracken the altitudinal range conditioned the degree of invasion in deforested areas. The annual deforestation rate changed notably between periods: ~1.5% from 1975 to 1987, ~0.8% from 1987 to 2000, and finally a very high rate of ~7.5% between 2000 and 2001. We explained these inconstant rates through some specific interrelated local and national political and socioeconomic drivers, namely land use policies, credit and tenure incentives, demography, and in particular, a severe national economic and bank crisis.