Laboratory for
Climatology and Remote Sensing

Abstract:

Die Arbeit stellt die Entwicklung eines Verfahrens zur Erkennung der Niederschlagsfläche in optischen Satellitendaten dar. Die Abgrenzung regnender Wolken beruht dabei auf dem Modellkonzept, dass diese über eine ausreichend große Kombination aus optischer Dicke und effektivem Tropfenradius verfügen müssen.

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:

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:

Ecosystem water regulation couples energy and water balance, depends on the integrity of the ecosystem, and responds to changes in climate. Changes in tree-water relationships in the biodiversity hotspot of the tropical Andes in southern Ecuador might be potentially observed at the level of individual trees, thus providing an efficient ecosystem monitoring method with applications in forest management and conservation at the tree and landscape levels. In this study,we combine area-average measurements froma laser scintillometer above the forestwith optical satellite data at high spatial resolution to obtain area-wide evapotranspiration data. The processing of field data includes the calculation of energy storage in forest biomass and the partitioning of evapotranspiration into transpiration and evaporation. Satellite-based estimates are calibrated by using tower flux measurements and meteorological data within periods of humid and less-humid atmosphere. The annual evapotranspiration was 1316 mm, of which 1086 mm per year corresponds to the forest transpiration at the study site. Average values of 4.7 and 4.1 mm d?1 per tree crown are observed under humid and less-humid atmospheric conditions, respectively, when applying high-resolution area-wide evapotranspiration in individual crown analysis. Approximately 24% of the observed crowns show a positive monthly change in ET, and 51% of the crowns show a significant change in the daily ET, which can be considered sensitive individuals concerning water relationships. The limitations in the area-wide evapotranspiration at the crown level can be explained by considering the spectral responses of the crown individuals. The presented method can be robustly deployed in the ecological monitoring of mountain forests.

Abstract:

Due to the significant impact of fog and low stratus (FLS) on economy, ecology and traffic systems, there is a growing demand for high-resolution information on FLS occurrence. In this study, a baseline climatology of FLS h day?1 based on data recorded from 2006–2015 by the Spinning Enhanced Visible and Infrared Imager system (SEVIRI) aboard the Meteosat Second Generation satellites is computed for Europe to provide the requested information. It is the first 10 year, spatially explicit climatology for FLS based on data with a temporal resolution of 15 min. The dataset is validated against Meteorological Aviation Routine Weather Reports (METAR) and shows good accordance with an average Heidke Skill Score of 0.45. Temporal and spatial variations in FLS frequency as well as interannual trends are analyzed. Winter shows the highest FLS occurrence, but a general decrease over the investigated period. Spring, summer and autumn show less pronounced trends and lower average FLS frequencies. Possible reasons for these distributions are discussed.

Abstract:

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This thesis presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data. The latter have been derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). For the detection of ground fog in MODIS imagery a new algorithm has been developed. It does not rely on cloud microphysical assumptions and was validated using camera data.

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:

A new rainfall retrieval technique for determining rainfall rates in a continuous manner (day, twilight, and night) resulting in a 24-h estimation applicable to midlatitudes is presented. The approach is based on satellite-derived information on cloud-top height, cloud-top temperature, cloud phase, and cloud water path retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) data and uses the random forests (RF) machine-learning algorithm. The technique is realized in three steps: (i) precipitating cloud areas are identified, (ii) the areas are separated into convective and advective-stratiform precipitating areas, and (iii) rainfall rates are assigned separately to the convective and advective-stratiform precipitating areas. Validation studies were carried out for each individual step as well as for the overall procedure using collocated ground-based radar data. Regarding each individual step, the models for rain area and convective precipitation detection produce good results. Both retrieval steps show a general tendency toward elevated prediction skill during summer months and daytime. The RF models for rainfall-rate assignment exhibit similar performance patterns, yet it is noteworthy how well the model is able to predict rainfall rates during nighttime and twilight. The performance of the overall procedure shows a very promising potential to estimate rainfall rates at high temporal and spatial resolutions in an automated manner. The near-real-time continuous applicability of the technique with acceptable prediction performances at 3–8-hourly intervals is particularly remarkable. This provides a very promising basis for future investigations into precipitation estimation based on machine-learning approaches and MSG SEVIRI data.

Abstract:

A method is presented for fog and low stratus detection from daytime satellite imagery based on Meteosat 8 SEVIRI (Spinning-Enhanced Visible and Infra-Red Imager) data. With its excellent spatial, spectral and temporal resolutions, this imagery is an ideal basis for operational fog monitoring. The scheme utilizes a range of pixel-based and novel object-oriented techniques to separate fog and low stratus clouds from other cloud types. Fog and low stratus are identified by a number of tests which explicitly and implicitly address fog/low stratus spectral, spatial and microphysical properties. The scheme's performance is evaluated using ground-based measurements of cloud height over Europe. The algorithm is found to detect low clouds very accurately, with probabilities of detection (POD) ranging from 0.632 to 0.834 (for different inter-comparison approaches), and false alarm ratios (FAR) between 0.059 and 0.021. The retrieval of sub-pixel and temporal effects remain issues for further investigation.

Abstract:

The distribution of fog and low stratus (FLS) is of importance in nowcasting, aviation forecasting and climate applications. This paper presents satellite-derived FLS maps as a basis for building a European FLS climatology with high spatial and temporal resolution. Averaged maps covering several winter seasons of Meteosat Second Generation (MSG) data are analysed and compared to a cloud climatology based on a 26-year record of ground-based visual cloud observations. The general patterns seen in both products are found to be in good agreement and plausible.

Abstract:

In the anthropogenic fire-disturbed ecosystem of the San Francisco Valley in the Andes of southeastern Ecuador, dense stands of an aggressive invasive weed, the southern bracken fern (Pteridium arachnoideum and Pteridium caudatum), dominate the landscape. To secure sustainable land management in the region, a comprehensive understanding of bracken spatial-distribution patterns and life cycle dynamics is crucial. We investigated the possibility of detecting bracken-infested areas and frond status (live, fungi-infected, and dead) by means of a high-resolution QuickBird scene from October 2010 and spectral signatures based on field spectroscopy. After image pre-processing, a two-step classification procedure first delineates the bracken-infested area by means of a maximum-likelihood hard classification. The probability-guided unmixing classifier with field-derived end-members is applied in the second step to obtain the fractional cover of the different frond statuses per pixel. The results showed that the areas infested by bracken could be distinguished from the other land-cover classes with high accuracy (overall accuracy of 0.9973). Also, the three frond statuses could be accurately classified at the sub-pixel level. The ‘dead’ class was the dominant frond status at the time of image acquisition (October 2010). We conclude that the extreme dry spell in October 2010 was particularly responsible for this dominance.

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.

Abstract:

The Tibetan Plateau suffers from progressive degradation caused by over-grazing due to improper live- stock management, global climate change and herbivory from small mammals. Therefore, a robust indicator system for rangeland degradation has to be developed and tested. This paper investigates local patterns of degradation at two sites (Lake Namco and Mt. Kailash) in Xizang province (China) that are cov- ered by vegetation types typical of a large portion of the plateau. The suitability of a two-indicator system is analysed using hyperspectral field measurements, and its transferability to spaceborne data is tested. The indicators are (1) land-cover fractions derived from linear spectral unmixing and (2) chlorophyll content as a proxy for nutrient and water availability calculated using hyperspectral vegetation indices and partial least squares regression. Because cattle remain near settlements overnight in the local semi- nomadic pastoral system, it can be expected that grazing intensity is highest near the settlement and declines with increasing distance. Therefore, we tested the effect of distance on both indicators using a Spearman correlation analysis. The predicted chlorophyll content and land cover fractions of the indica- tor system were in good agreement with field observations (correlation coefficients between 0.70 and 0.98). High correlations between distance from settlements and land-cover fractions at both study sites demonstrated that the land-cover fraction is a reliable indicator for degradation. A positive correlation between distance from settlements and photosynthetically active vegetation (PV) revealed over-grazing patterns at the first site. Furthermore, the chlorophyll indicator was proven suitable because chlorophyll concentration declined with increasing distance from settlements. This underlines the over-grazing pat- tern because cattle excrement was the only external source of nutrients in the ecosystem and it was positively correlated with grazing intensity. However, at the second site, we found a significant positive effect of distance on the amount of photosynthetically non-active vegetation; no effect of distance on PV and chlorophyll content was found. Therefore, no evidence of pasture degradation was detected at the second site. Regarding the potential use of satellite data for degradation monitoring, we found that (1) the land-cover indicator derived from multispectral data was more robust than using noise-filtered hyper- spectral information and (2) the chlorophyll amount indicator was estimated from simulated EnMAP data with low error rates. Because the proposed two-indicator system can be derived from multi- and hyperspectral satellite data and combines site conditions and local plant cover, it provides a time-saving and robust method to measure pasture degradation across large areas, assuming that respective satellite data are available.