David Aragonés

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images.

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630-690 nm), band 5 (1550-1750 nm) and the ratio between bands 1 (450-520 nm) and 4 (760-900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520-600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.

Determining Water Body Characteristics of Doana Shallow Marshes Through Remote Sensing

We analyze the potential of Landsat TM and ETM images to discriminate inundation, depth and turbidity patterns in the very heterogeneous shallow marshes of Donana National Park. According to the results we will reconstruct historical changes in such variables with a long time series of Landsat images (MSS, TM and ETM+). For this purpose we sampled 334 ground-truth points simultaneously to 6 Landsat scenes during springtime of 2004, 2005 and 2006. Then we applied statistical models to field data and we predict inundation level, depth and turbidity at every sample unit with reflectivity data. Results show that SWIR band is the best predictor of inundation level at any point (especially in sediment-charged waters and medium-high plant cover). Therefore we propose two statistical models explaining 31 % of deviance for water turbidity and 70 % of deviance depth in inundated areas.

Long-Term Monitoring of the Flooding Regime and Hydroperiod of Doñana Marshes with Landsat Time Series (1974–2014)

This paper presents a semi-automatic procedure to discriminate seasonally flooded areas in the shallow temporary marshes of Donana National Park (SW Spain) by using a radiommetrically normalized long time series of Landsat MSS, TM, and ETM+ images (1974–2014). Extensive field campaigns for ground truth data retrieval were carried out simultaneous to Landsat overpasses. Ground truth was used as training and testing areas to check the performance of the method. Simple thresholds on TM and ETM band 5 (1.55–1.75 μm) worked significantly better than other empirical modeling techniques and supervised classification methods to delineate flooded areas at Donana marshes. A classification tree was applied to band 5 reflectance values to classify flooded versus non-flooded pixels for every scene. Inter-scene cross-validation identified the most accurate threshold on band 5 reflectance (ρ < 0.186) to classify flooded areas (Kappa = 0.65). A joint TM-MSS acquisition was used to find the MSS band 4 (0.8 a 1.1 μm) threshold. The TM flooded area was identical to the results from MSS 4 band threshold ρ < 0.10 despite spectral and spatial resolution differences. Band slicing was retrospectively applied to the complete time series of MSS and TM images. About 391 flood masks were used to reconstruct historical spatial and temporal patterns of Donana marshes flooding, including hydroperiod. Hydroperiod historical trends were used as a baseline to understand Donana’s flooding regime, test hydrodynamic models, and give an assessment of relevant management and restoration decisions. The historical trends in the hydroperiod of Donana marshes show two opposite spatial patterns. While the north-western part of the marsh is increasing its hydroperiod, the southwestern part shows a steady decline. Anomalies in each flooding cycle allowed us to assess recent management decisions and monitor their hydrological effects.

Monitoring Marsh Dynamics Through Remote Sensing

Remote sensing has been used widely, and in many different ways, for wetlands. From simple wetland delineation and mapping to water body characterisation and the extraction of biophysical parameters, remote sensing images have provided useful results. Remote sensing offers synoptic and repetitive views of the same places on Earth. Additionally, remote sensors have been capturing long time series of images, with most sensors still fully active. This allows historical reconstruction of land cover changes and ensures future monitoring. However, several limitations exist and these must be taken into account when dealing with long time series of images. In this paper, we introduce the different remote sensing monitoring protocols adopted for Donana marshlands, and present some results on mapping hydroperiod and flooding, water depth and turbidity with a multitemporal Landsat dataset (1975–2008). Interpretation of the results is allowing us to test the validity of actions proposed in the framework of the Donana 2005 restoration project. We also present preliminary results from monitoring the spread of an alien species in Donana.

Effect of Protection Level in the Hydroperiod of Water Bodies on Doñana’s Aeolian Sands

Mediterranean temporary ponds on Donana’s aeolian sands form an extensive system of small dynamic water bodies, dependent on precipitation and groundwater, of considerable importance for biodiversity conservation. Different areas of the aeolian sands have received different levels of environmental protection since 1969, and this has influenced the degree of conservation and the flooding dynamic of these temporary surface waters. We use the Landsat series of satellite images from 1985 to 2014 to study the temporal dynamic of small temporary water bodies on the aeolian sands in relation to the protection level and to distance to water abstraction pressures from agriculture and residential areas. The results show that even with small and ephemeral water bodies optical remote sensing time-series are an effective way to study their flooding temporal dynamics. The protected areas of the aeolian sands hold a better preserved system of temporary ponds, with a flooding dynamic that fluctuates with precipitation. The unprotected area shows an increase in mean hydroperiod duration, and surface flooded, and a decline in hydroperiod variability. This seems to be due to the creation of irrigation ponds and the artificialization of the flooding regime of the natural temporary ponds, that either receive excess irrigation water or dry-up due to the lowering of the groundwater table level. Although a decline in hydroperiod duration of temporary ponds is seen as negative to the system, an increase in hydroperiod of surface waters due to artificialization, or a decline in variability cannot be considered as positive compensatory effects.

Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy

The use of Pseudoinvariant Areas (PIA) makes it possible to carry out a reasonably robust and automatic radiometric correction for long time series of remote sensing imagery, as shown in previous studies for large data sets of Landsat MSS, TM, and ETM+ imagery. In addition, they can be employed to obtain more coherence among remote sensing data from different sensors. The present work validates the use of PIA for the radiometric correction of pairs of images acquired almost simultaneously (Landsat-7 (ETM+) or Landsat-8 (OLI) and Sentinel-2A (MSI)). Four pairs of images from a region in SW Spain, corresponding to four different dates, together with field spectroradiometry measurements collected at the time of satellite overpass were used to evaluate a PIA-based radiometric correction. The results show a high coherence between sensors (r2 = 0.964) and excellent correlations to in-situ data for the MiraMon implementation (r2 > 0.9). Other methodological alternatives, ATCOR3 (ETM+, OLI, MSI), SAC-QGIS (ETM+, OLI, MSI), 6S-LEDAPS (ETM+), 6S-LaSRC (OLI), and Sen2Cor-SNAP (MSI), were also evaluated. Almost all of them, except for SAC-QGIS, provided similar results to the proposed PIA-based approach. Moreover, as the PIA-based approach can be applied to almost any image (even to images lacking of extra atmospheric information), it can also be used to solve the robust integration of data from new platforms, such as Landsat-8 or Sentinel-2, to enrich global data acquired since 1972 in the Landsat program. It thus contributes to the program’s continuity, a goal of great interest for the environmental, scientific, and technical community.

Using UAVs to Map Aquatic Bird Colonies

In this chapter, we present the results of several flight campaigns carried out in 2015 and 2016 using multirotor Unmanned Airborne Vehicles (UAVs) over Slender-billed Gull (Chroicocephalus genei) colonies in the Donana Nature Space, south west Spain. The images were taken at different times during the breeding season. The requirements for the flight campaigns were to acquire sufficient visible and nadir pictures at 5 cm pixel resolution and to cover the entire nesting colony with maximum overlap. Although we carried out the flights under clear skies, low wind speed was not always possible, causing a few blurred pictures. After georeferencing and mosaicking the set of raw pictures, we adopted photo-interpretation as the first technique to identify and delineate birds, either lying, standing or flying. A nest position was assigned when the clear pattern of a lying birds was recognised. We then selected a set of breeding individuals (nests) to train a supervised classification in semi-automatic nest delineation. We applied two different algorithms and tested their accuracy in identifying gulls with an independent set of manually delineated individuals. We chose the best method according to the accuracy results and applied it to the whole colony. We found major issues for nest identification and delineation for nests under tree and shrub canopies. The different campaigns and flight characteristics were useful to improve bird identification accuracy. As a result, we provided estimates of the number of breeding pairs per year to managers and cross-checked these with estimates from the ground monitoring and colony sampling. As an added value, the spatial coordinates of nests can be used for spatial analysis and investigate nest aggregation, density and distribution in order to reveal spatial relationships with environmental factors such as distance to colony edges, distance to colony centroid, distance to predators, etc.

Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data

Satellite data offer the opportunity for monitoring the temporal flooding dynamics of seasonal wetlands, a parameter that is essential for the ecosystem services these areas provide. This study introduces an unsupervised approach to estimate the extent of flooded areas in a satellite image relying on the physics of light interaction with water, vegetation and their combination. The approach detects automatically thresholds on the Short-Wave Infrared (SWIR) band and on a Modified-Normalized Difference Vegetation Index (MNDVI), derived from radiometrically-corrected Sentinel-2 data. Then, it combines them in a meaningful way based on a knowledge base coming out of an iterative trial and error process. Classes of interest concern water and non-water areas. The water class is comprised of the open-water and water-vegetation subclasses. In parallel, a supervised approach is implemented mainly for performance comparison reasons. The latter approach performs a random forest classification on a set of bands and indices extracted from Sentinel-2 data. The approaches are able to discriminate the water class in different types of wetlands (marshland, rice-paddies and temporary ponds) existing in the Donana Biosphere Reserve study area, located in southwest Spain. Both unsupervised and supervised approaches are examined against validation data derived from Landsat satellite inundation time series maps, generated by the local administration and offered as an online service since 1983. Accuracy assessment metrics show that both approaches have similarly high classification performance (e.g., the combined kappa coefficient of the unsupervised and the supervised approach is 0.8827 and 0.9477, and the combined overall accuracy is 97.71% and 98.95, respectively). The unsupervised approach can be used by non-trained personnel with a potential for transferability to sites of, at least, similar characteristics.