Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

Tropical Mountain Forest (TMF) provides important ecological functions 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 introduces 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 meteorological 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 calculated using the FAO-Penman Montheid method by applying data obtained from an automatic weather station. The enhanced vegetation 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 topographical 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:

Tropical ecosystems offer a unique setting for understanding ecohydrological processes, but to date, such investigations have been limited. The purpose of this paper is to highlight the impor- tance of studying these processes—specifically, how they are being affected by the transforma- tive changes taking place in the tropics—and to offer an agenda for future research. At present, the ongoing loss of native ecosystems is largely due to agricultural expansion, but parallel pro- cesses of afforestation are also taking place, leading to shifts in ecohydrological fluxes. Similarly, shifts in water availability due to climate change will affect both water and carbon fluxes in trop- ical ecosystems. A number of methods exist that can help us better understand how changes in land use and climate affect ecohydrological processes; these include stable isotopes, remote sens- ing, and process?based models. Still, our knowledge of the underlying physical mechanisms, espe- cially those that determine the effects of scale on ecosystem processes, remains incomplete.We assert that development of a knowledge base concerning the effects of transformative change on ecological, hydrological, and biogeochemical processes at different spatio?temporal scales is an urgent need for tropical regions and should serve as a compass for emerging ecohydrologists. To reach this goal, we advocate a research agenda that expands the number and diversity of eco- systems targeted for ecohydrological investigations and connects researchers across the tropics. We believe that the use of big data and open source software—already an important integrative tool/skill for the young ecohydrologist—will be key in expanding research capabilities.

Abstract:

Geographic Information Systems and Remote Sensing are important contributors to Sustainable Forestry Management Plans. Remote sensing techniques for image interpretation provides the means to extract valuable information that could be expensive and time-consuming to obtain through field observations (Franklin et al. 2001). Spatial Products derived from the interpretation of airborne and satellite borne images feed Geographic Information Systems to develop strategies and methodologies for resource management, harvest planning, fire management, map production, and model predictions. (Yusmah et al. 2015) This study has three important objectives: to test the feasibility of template matching for the identification of single pine tree crowns, to conduct a delineation of pine plantations using relational features and to evaluate how single tree crown size affects the accuracy of the proposed method. Templates of single trees were produced in the software eCognition Developer. The sampling process comprised the random selection of 3000 single pine trees in 7 different test sites (test sites were grouped in 3 categories according to the single tree sizes). A first rule set to detect single tree crowns was developed in eCognition Developer, using three different template groups (4, 8 and 16 templates). Through an analysis of variance, the number of single tree crowns detected was compared for the different template groups. Using a second rule set in eCognition, the template matching algorithm combined with relational, spectral and contextual information were applied to delineate pine plantation areas. An accuracy assessment was performed in the test sites for all thematic classes identified. Finally, an Analysis of Variance evaluated the influence of single tree crown size on the overall accuracy. Potential applications and improvements to the proposed methodology for single tree crown detection and plantation delineation are proposed at the end of the document.

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 from a laser scintillometer above the forest with 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:

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 ef?- cient ecosystem monitoring method with applications in forest management and conservation at the tree and landscape levels. In this study,we combine area-averagemeasurements froma laser scintillometer above the for- est with optical satellite data at high spatial resolution to obtain area-wide evapotranspiration data. The process- ing of ?eld 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 ?ux 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 at- mospheric 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 signi?cant 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:

Facing the ongoing loss of natural ecosystems, worldwide monitoring of biodiversity across different spatial scales is essential for conservation planning. Remote sensing (RS) has proven to be a cost-efficient tool to access environmental characteristics such as vegetation structure and associated distributions of animal species on a broad scale. Special emphasis is put on birds as indicators for biodiversity owing to their strong species–habitat relationship. So far, bird diversity was modeled ignoring that species–habitat relationships differ among feeding guilds. This is surprising, since habitat preferences strongly depend on diet specialization. Therefore, I investigated RS texture image based vegetation metrics to test whether the predictability of specialized avian feeding guilds including insectivores, frugivores and nectarivores is higher than of the less specialized omnivore guild and overall bird diversity. I used point count data of bird communities among 30 study sites in a complex tropical mountain forest ecosystem in south-eastern Ecuador to estimate (i) Shannon index and (ii) community composition as measures of ?-diversity and combined ?- and ?-diversity, respectively. In order to relate both diversity measures to RS metrics, I compared two high dimensional predictor sets – satellite images and airborne orthophotos – with structural indices derived from a discrete return airborne Lidar sensor. Partial least squares regression was used to unveil the predictive power of all fitted feeding guild models. For the comparability of all models, a sample size correction on species number per guild was applied. Shannon index predictability ranged between 37 % and 65 %; and best predictions were achieved for insectivores using metrics from satellite or Lidar images and nectarivores species using metrics from orthophotos. Community composition was generally better predicted than Shannon index with explained variations from 65 % to 85 %. Frugivore and nectarivore community compositions were best predicted using metrics from orthophotos, whereas the two other sensors best predicted omnivores. For both diversity measures, performance of satellite derived metrics revealed slightly better model results compared to other sensors emphasizing its applicability for the regarded study area. In conclusion, specialized feeding guilds were not consistently better predicted than omnivore or overall bird diversity; rather the study showed that model performances depended on the regarded diversity measure and RS image type. However, insectivores might be the best surrogate for overall diversity with high predictability in all compared models. In addition, the high explanatory power for community composition suggests that the measure should considered in avian diversity modeling for conservation planning.

Abstract:

The worldwide demand for large scale biodiversity monitoring systems is challenging for recent remote sensing techniques. For monitoring changes in species as well as functional diversity the consideration of ecosystem processes is crucial. Herbivory as a focal function in tropical forest ecosystems is therefore being investigated in this study. Aim of the study is to reveal the potential of remotely sensed image textures for predicting herbivory. For this leaf area loss (LAL) due to herbivory has been quantified in different vegetation layers of a tropical mountain forest in the Ecuadorian Andes. The study area is a structurally highly complex region along an elevation gradient from 1000 to 3000 m a.s.l. with undisturbed primary forest and disturbed forest fragments within agriculturally used land. It has been proven that LAL has similar patterns as herbivore abundance over the elevation gradient showing it to be a possible proxy for herbivore abundance. Further results show a correlation of LAL data and canopy cover thus local forest structure. Correlations between LAL and image textures as a proxy for vegetation structure were stronger than with pixel wise derived spectral values only. Unveiling the high potential of image textures as a surrogate for herbivory and ecosystem function.

Abstract:

The South Ecuadorian Andes are one of the hottest global hotspots of biodiversity and currently threatened by land use and climate change. Forest structure and composition are crucial factors for understanding the capacity of forest support species in changing environments. In order to prioritize limited conservation resources a better understanding of tree diversity patterns is needed. The use of image texture measures, as a proxy for spatial and forest structure has shown useful possibilities in explaining patterns of tree diversity and species richness. My goal was to evaluate the performance of different texture measures on NDVI, EVI and two broad-band combinations on high resolution (0.3m) aerial photography to predict tree diversity. Bayesian Model Averaging (BMA) was used to relate in situ measurements of tree diversity to measures of image texture. Texture explained up to 44.7% of the variability of tree diversity with measures related to habitat heterogeneity, particularly variance, providing highest explanatory power. Image texture measures bear considerable potential for predicting tree diversity in the tropics and can contribute to improvements on conservation efforts and management planning.

Abstract:

Human activities during the last decades provoked a notable reduction in global forest cover. Knowing that forest stands act as stock and sinks for carbon and other greenhouse gases, it is important to determine the existing forest cover at country level and to calculate annual deforestation rates. This work uses NOAAsatellite images in a resolution of 1 km x 1 km to classify the surface of continental Ecuador in “forest” – “non-forest” pixels and to estimate the annual deforestation rate from 1986 to 2001 as well as from 2001 to 2008. The method is based on a decision tree algorithm that includes different spectral bands of the NOAA-AVHRRsensor and additional topographic and meteorological parameters. The results show that the total forest cover of continental Ecuador was reduced from 48.1 % in 1986 to 36.8 % in 2008. The calculated annual deforestation rates indicate that forest reduction increased during the last decade. The most affected area is the Coastal Lowland, due to the enhanced population pressure, followed by the Amazon Basin, not only caused by the governmental supported oil and mining industry, but also due to the uncontrolled timber extraction. The Andean Highland has been less affected, because the major parts of this region were deforested before, during the Pre-Columbian-Era.

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.

Abstract:

This paper describes a method of low-altitude remote sensing in combination with in situ measurements (leaf area, spectroscopy, and position) to monitor the postfire canopy recovery of two competing grassland species. The method was developed in the Andes of Ecuador, where a tethered balloon with a digital camera was deployed to record a time series of very high spatial resolution imagery ( nominal resolution = 2cm ) of an experimental plot covered by two competing species: 1) the pasture grass, Setaria sphacelata; and 2) the invasive southern bracken, Pteridium arachnoideum. Image processing techniques were combined to solve geometric issues and construct high-quality mosaics for image classification. The semiautomatic and object-oriented classification method was based on geometrical and textural attributes of image segments and showed promising results for detecting the invasive bracken fern in Setaria pastures (performance by area under the curve, AUC = 0.88). Valuable insights are given into vegetation monitoring applications using unmanned aerial vehicles, which produces a time series of species-specific maps, including foliage projective cover (FPC) and leaf area index (LAI). This new method constitutes an important and accessible tool for ecological investigations of competing species in pastures and validation of remote sensing information on mountain environments.

Abstract:

Soil-vegetation-atmosphere transfer (SVAT) is to be predicted for 2050/2100 for a study area in the southern Ecuadorian Andes. SVAT models require information on land use/ land cover (LUC) as lower boundary conditions. Since the study area suffers from high deforestation rates, LUC cannot be assumed as staying constant with time. A spatially explicit land use/land cover change (LUCC) model is therefore needed for future SVAT prediction. The numbers of approaches of LUCC modelling are numerous. Difficulties are due to complex interactions of social and biophysical drivers of change. In this study a model of LUCC was built using information of past changes derived by remotely sensed data. Special focus was on forest development patterns. A training period of 14 years between 1987 and 2001 was chosen. Two LUC classifications were accomplished to Landsat data of the start and end date of this period. A change detection of the training period provided the basis for predictive LUCC modelling. Potential drivers for LUCC were applied to the model as GIS layers. The modelling procedure consisted of a combination of Markov chain analysis (MCA) for quantitative modelling and multi-layer perceptron (MLP) for revealing potential locations of change. A multi-objective land allocation (MOLA) served as final integration step. 14 LUC transitions were considered in the modelling procedure. Unconsidered LUC classes were assumed to stay constant in the future. The model results were maps of LUC for 2006, 2010 and afterwards for every 10 years up to 2100. An internal validation was performed with the training data. The results of the prediction were validated by comparing the model output of 2006 to an ASTER LUC classification of the same time. The validation methodology comprised crisp and fuzzy map comparison using Kappa statistics. The study area featured a deforestation of 13.61% in the training period. The model was able to explain deforestation in the training period 51% better than just by chance. The location of predicted deforestation reached a better than chance agreement of 30%. Predicted quantities of deforestation were 59% conforming with the reference. The validation of the prediction indicated the difficulty of modelling human impact on the ecosystem. Prospects and limitations of the model were identified with suggestions for future research tasks. The results of this study are assumed to present a good groundwork for future SVAT models.