A MODIS based tool to assess inundation patterns: an example for the Paraná Delta River

Wetlands are one of the most productive yet altered ecosystems due to human activities across the world. They rely largely on their water flow to provide several ecosystem functions. Then, to develop a land use plan that allows a productive use maintaining the ecological integrity it is critical to understand the flooding patterns. In that sense, the capability of remote sensors to estimate water cover for large areas at detailed spatial and temporal scales can help to develop managerial decision tools. However, the temporal and spatial variation of water components may alter it spectral properties. We studied the capability of different MODIS derived spectral indices to estimate water cover or water presence-absence. The study region was the Paraná Delta River, which is a 2 Mha wetland area. Between all the models evaluated, one based on the spectral index NDWI1 ((Red – SWIR) / (Red + SWIR)) was the most accurate. A NDWI1 = -0.2 threshold allowed to separate those pixels with less than those with more than 60% of water cover with an accuracy of 91%. By this model, we described the flooding patterns of different landscape units of the region during the last 12 years and classified the region according to the impact of ordinary and extraordinary flooding events. We consider this information can help to improve the knowledge about the hydrodynamics, monitor the impact of some activities and develop a more sustainable regional planning.Los humedales están entre los ecosistemas más productivos y, a su vez, están fuertemente alterados por el ser humano. Los múltiples servicios que proveen dependen en gran medida del flujo de agua. Por ello, para desarrollar un plan de uso de la tierra que permita un uso productivo manteniendo la integridad ecológica es fundamental comprender los patrones de inundación. En ese sentido, la capacidad de los sensores remotos de estimar la cobertura de agua en áreas grandes a escalas espaciales y temporales detalladas contribuirían a desarrollar herramientas que favorezcan la toma de decisiones. Sin embargo, la variación temporal y espacial de los componentes del agua puede alterar sus propiedades espectrales. Se estudió la capacidad de diferentes índices espectrales derivados del sensor MODIS para estimar la cobertura de agua o la presencia/ ausencia de agua. La región de estudio fue el Delta del Río Paraná, un humedal de 2 millones de hectáreas. Entre todos los modelos evaluados, uno basado en el índice espectral NDWI1 ((Rojo - SWIR) / (Rojo + SWIR)) fue el más preciso. Un valor umbral de NDWI1 = -0,2 permitió separar píxeles con menos de 60% de cobertura de agua de aquellos con más del 60% con una precisión del 91%. Mediante este modelo se describieron los patrones de inundación de diferentes unidades de paisaje de la región durante los últimos 12 años y se clasificó la región de acuerdo al impacto de los eventos de inundación ordinarios y extraordinarios. Consideramos que esta información puede ayudar a mejorar el conocimiento sobre la hidrodinámica, monitorear el impacto de algunas actividades y desarrollar una planificación regional más sostenible.EEA Concepción del UruguayFil: Durante, Martin. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Concepción del Uruguay; ArgentinaFil: Di Bella, Carlos Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Buenos Aires. Facultad de Agronomía. Departamento de Métodos Cuantitativos y Sistemas de Información; Argentin

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data

Progress in the remote sensing of groundwater-dependent ecosystems in semi-arid environments

Remote sensing of groundwater-dependent ecosystems (GDEs) has increased substantially in recent years. Of significant prominence, is the delineation and mapping of groundwater-dependent vegetation (GDV), species diversity, and water quality in these ecosystems. Groundwater-dependent ecosystems provide several ecological services such as habitat for wildlife fauna, carbon sequestration and water purification. The recent technological advancements and readily accessibility of new satellite sensors with improved sensing characteristics have resulted in numerous state-of-the-art applications for GDEs assessment and monitoring. These studies were done at varying scales, essentially in light of global climate change and variability. In this study, we review and assess the progress on the remote sensing of GDEs in semi-arid environments. We present the key trends in GDEs remote sensing that underpin many of the recent scientific research milestones and application developments. In addition, we observed a considerable shift towards the use of advanced spatial modelling techniques, using high- resolution remotely sensed data to further improve the characterisation and understanding of GDEs. Thus, literature shows the successful use of freely available remotely sensed data in mapping GDEs

Análise de curvas fenológicas obtidas a partir de séries temporais NDVI-MODIS em escalas inter-anuais e intra-anuais no município de Pacaraima, Roraima

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2015.A pesquisa objetivou caracterizar as curvas fenologia da vegetação do município de Pacaraima e arredores, por meio de estatísticas inter e intra-anuais derivadas de séries temporais de índice de vegetação por diferença normalizada (NDVI) do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) no período 2001-2014. Para atingir esse objetivo, foi aplicada um conjunto de metodologias que compreendem: Séries NDVI-MODIS, confeccionadas com o produto de 8 dias; filtragem de ruídos Savitzky-Golay, com utilização de picos negativos e positivos; seleção de amostras dos tipos vegetacionais; confecção de métricas em escalas interanuais e intra-anuais; e comparação dos resultados com variáveis climáticas. As classes analisadas foram: Floresta Estacional Semidecidual Aluvial, Floresta Estacional Semidecidual Submontana, Floresta Estacional Semidecidual Montana, Floresta Ombrófila Densa Montana, Savana Arborizada, Savana Estépica, Savana Gramíneo-Lenhosa, Savana Parque, Mata Ciliar e Uso Antrópico. As séries foram processadas para gerar as seguintes estatísticas inter e intra-anuais: Coeficiente de Variação, Curtose, Desvio Padrão, Média, Obliquidade, Somatório do Quadrado, Valor Máximo, Valor Mínimo e Variância. As formações savânicas, florestais e de uso da terra apresentaram comportamentos bem distintos com relação às medidas estatísticas de médias e de dispersão. Os resultados do estudo permitiram evidenciar que os ciclos fenológicos característicos de cada vegetação apresentam uma sensibilidade a variações climáticas sazonais e aos eventos de El Niño.The research aimed to characterize the phenological curves of vegetation in the city of Pacaraima and surrounding areas, through inter and intra-annual statistics derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data in the period of 2001-2014. To achieve this, it was applied a set of methodologies that include: NDVI-MODIS time series, deriving from the 8-day product; denoising of the series with Savitzky-Golay filter, with negative and positive peaks; selection of vegetation type samples; production of metrics in interannual and intra-annual scales; and comparison of the metrics with climatic variables. The analyzed classes of vegetation were: Seasonal Alluvial Semideciduous Forest, Seasonal Submontane Semideciduous Forest, Seasonal Montane Semideciduous Forest, Montane Rain Forest, Wooded Savanna, Steppic Savanna, Grass-shrub Savanna, Parklands Savanna, Riparian Forest and Anthropic Use. The time series were processed to obtain the following statistics in inter and intra-annual time scales: Coefficient of Variation, Kurtosis, Maximum Values, Mininum Values, Mean, Range, Standard Deviations, Skewness, Sum of Squares and Variance. The savanna formations, Forest and Land Use classes had very different behaviors relative to the statistical measures of averages and dispersion. The study results highlighted the characteristic phenological cycles of each class of vegetation and presented sensitivity to the seasonal climatic changes and events of El Niño

An assessment of the impacts of climate and land use/cover changes on wetland extent within Mzingwane catchment, Zimbabwe

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy(Geography and Environmental Science). Johannesburg, June 2018.Wetlands ecosystems are amongst the most diverse and valuable environments which provide a number of goods and services pertinent to human and natural systems functioning yet they are increasingly threatened by anthropogenic and climatic changes. This thesis, examines the impact of climatic trends and variations, and land use/land (LU/LC) cover changes on wetland extent within Mzingwane catchment, south-western of Zimbabwe. An attempt is made to establish how the two stressors (climate and LU/LC changes) modify areal extents of wetlands over time, grounded on the hypothesis that, climate and LU/LC related changes impact on wetland ecosystems resulting in their degradation, shrinking in size and in some cases overall loss. To achieve the broader objective of the study, a number of parametric and non-parametric statistical analyses were employed to quantify and ascertain climate variability and change in Mzingwane catchment through the use of historic and current climatic trends in rainfall and temperature (T). Remote sensing data was used for wetland change analysis for the period between 1984 and 2015as well as future land cover predictions based on CA-Markov Chain model. LU/LC changes on nested wetlands were modelled at catchment level. In addition the study simulated future rainfall and extreme events and their implications on wetland dynamics using Regional Climate Models derived from CORDEX data. Trends in annual Tmax significantly increased (p=1mm) has decreased by 34%, thus suggesting much more concentrated and increased rainfall intensity. A notable shift in both the onset and cessation dates of the rainy season is recorded, particularly during the 21st century, which has resulted in a significant reduction (p<0.05) in the length of the rainy season. Land change analysis results show a decline in woodland and wetland cover which could be resulting from both human and natural factors. Major conversions are from wetland cover to crop field, suggesting agricultural encroachment onto wetland areas. Wetland area thus significantly decreased by 60.16% (236, 52 ha) in the last 30 years (p < 0.05). CA-Markov model results for the years 2025, 2035 and 2045 predicted an overall increase in the crop field areas at the expense of woodland and wetland areas. LU/LC modelling results suggest that LU/LC changes modify wetland hydrology which consequently influences wetland areal extent. Trend results for projected rainfall suggest a significant decreasing trend in future rainfall (2016-2100) at p<0.05. In addition, a general decreasing trend in the number of rainy days is projected for the future climate although the significance and magnitude varied with station location. Regional Climate Models projections suggest an increased occurrence of future extreme events particularly towards the end of this century. The findings are important for developing appropriate sustainable and adaptive strategies given climate changes as well as designing catchment level wetland management approaches aimed at sustaining wetland ecosystems for the current and future generations. Any future efforts towards protection of the remaining wetlands should be combined with developing a sustainable relationship between social and ecological systems which will enable communities to adapt to the effects of changing climates.LG201

The impact of land use and land cover changes on wetland productivity and hydrological systems in the Limpopo transboundary river basin, South Africa

Philosophiae Doctor - PhDWetlands are highly productive systems that act as habitats for a variety of flora and fauna. Despite their ecohydrological significance, wetland ecosystems are under severe threat as a result of environmental changes (e.g. the changing temperature and rainfall), as well as pressure from anthropogenic land use activities (e.g. agriculture, rural-urban development and dam construction). Such changes result in severe disturbances in the hydrology, plant species composition, spatial distribution, productivity and diversity of wetlands, as well as their ability to offer critical ecosystem goods and services. However, wetland degradation varies considerably from place to place, with severe degradation occurring particularly in developing regions, such as sub-Saharan Africa, where Land Use and Land Cover changes impact on wetland ecosystems by affecting the diversity of plant species, productivity, as well as the wetland hydrology

Mallines del sur de la patagonia: interacciones entre unidades fisiográficas y productividad en diversos ambientes geomorfológicos

Tesis para obtener el grado de Doctor en Ciencias Aplicadas, presentada en la Universidad Nacional de Luján en 2017Esta investigación surge con el propósito comprender el rol que ejercen las condiciones de emplazamiento en los mallines del Sur de la Patagonia, así como analizar sus características hidro-eco-geomorfológicas, su interacción con la productividad y con su estado de deterioro, en un ambiente caracterizado por su clima frío.EEA Santa CruzFil: Vargas, Paola Patricia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentin

Spatio-Temporal Information Extraction Under Uncertainty Using Multi-Source Data Integration and Machine Learning: Applications To Human Settlement Modelling

Due to advances in information and communication technology, new ways of acquisition, storage, and analysis of digital data have emerged. This constitutes new opportunities, but also imposes challenges for many scientific disciplines, including the geospatial sciences, where the availability, accessibility, and spatio-temporal granularity and coverage of environmental, geographic, and socioeconomic data is steadily increasing. Multi-source data measuring identical or related processes typically increase the reliability of knowledge derived but also lead to higher levels of discrepancies. In order to fully benefit from the value of such multi-source data, the contained information needs to be extracted effectively and efficiently, employing adequate data integration, mining, and analysis techniques. This work demonstrates how the integration of coherent multi-source geospatial data supports information extraction and analysis to generate new knowledge of both, the data itself and the underlying phenomenon, exemplified by the spatio-temporal distribution of human settlements. I present three applications in the field of human settlement modelling where data integration is a key component for knowledge acquisition. These three applications consist of i) a deep-learning based classification framework for fully automated extraction of built-up areas from historical maps in the spatial domain, ii) a machine-learning based time series classification framework for estimating changes in built-up areas in the temporal domain, based on multispectral remote sensing time series data, and iii) a novel framework for an in-depth accuracy assessment of model-generated data, exemplified by the Global Human Settlement Layer, for a detailed analysis of data uncertainty in the spatio-temporal domain, as well as across different scales and aggregation levels, attempting to quantify the fitness-for-use of such data.</p

A classification of large wetlands in Africa’s elevated drylands based on their formation, structure, and hydrological functioning using Earth Observation (EO) data and Geographic Information System (GIS)

Due to wetland inaccessibility and limited wetland geomorphological studies, there is limited information on the geomorphological origin and hydrological functioning of different types of wetlands in Africa’s elevated drylands. As a result, there is limited information for the development of a comprehensive wetland classification system that classifies wetlands based on long-term geomorphic processes that determine their formation and shape, their structure and hydrological functioning. Therefore, the current study was designed to classify large wetlands in Africa’s elevated drylands based on processes that determine their formation, and shape their structure and hydrological functioning using remote sensing and Geographic Information System (GIS) techniques. Although wetlands perform a number of hydrological functions including groundwater recharge and water purification, the current study focuses mainly on their flood attenuation function. Detailed analysis of topographic information was undertaken using Shuttle Radar Topographic Mission (SRTM) elevations measured at the scale of 30 m x 30 m. LandsatLook and Google Earth images, tectonic as well as geological data were used as supplementary data for developing an understanding of the origin, structure and hydrological characteristics of wetlands. The Principal Component Analysis (PCA) of wetland environmental variables was used to identify and explain wetland heterogeneity. The results of the study showed that fluvial processes, tectonic history and the evolution of Africa’s landscape played a fundamental role in the formation and evolution of wetlands. This study demonstrates a wide range of processes that contribute to wetland formation, structure and functioning. At one extreme it is clear that tectonic processes may be primarily responsible for the creation of basins that host wetlands. At another extreme, wetlands may be structured primarily by fluvial processes. At a third extreme are wetlands that superficially appear to be structured by fluvial processes, but which have their structures modified by gradual rising of the base level at their distal ends, either through marginal uplift adjacent to rift valleys, or through aggradation of a floodplain that blocks a tributary valley. Overall, the classification of wetlands considered in this study can be summarised into four distinct groupings, with two of these divided further into two groupings each: (1) Tectonic basins with little or no indication of fluvial development (Bahi and Wembere wetlands), (2) Tectonic basins evolving towards a wetland with a structure increasingly shaped by fluvial characteristics (Usangu wetland), (3) Fluvially modified valleys with a local base level at the toe of the wetland such as a resistant lithology or a tectonic control that limits the rate of incision of easily weathered and eroded lithologies, leading to valley widening and longitudinal slope reduction, which are of two distinct types: (a) With a catchment on Kalahari Group sediment that is transported fluvially as bedload, and therefore with no prominent alluvial ridge or backwater depressions (Upper Zambezi and Barotse wetlands), (b) With a catchment that produces abundant fine sediment that is deposited as overbank sediments, leading to channel migration via meandering and to the construction of an elevated alluvial ridge (Lufira wetland), (4) Fluvially modified basins with evidence of gradual elevation of the base level at the toe of the wetland, which are of two types: (a) Tectonic marginal rift valley uplift such that they behave more as depression wetlands rather than as wetlands shaped by fluvial processes (Kafue and Luapula wetlands), (b) Tributary valley wetlands blocked by aggradation of the trunk valley (Lukanga wetland). In conclusion, although few geomorphological studies have been conducted on southern African wetlands because of their inaccessibility, Africa’s surface topography and its historical evolution, as well as aridity, provide an opportunity for illustrating the important role that the long-term tectonic, geological and geomorphological processes play in determining wetland origin, structure and dynamics. GIS methodology and Earth Observation (EO) data on the other hand, provide a practical means for acquiring information on inaccessible and hard to traverse wetland systems. A novel cut-and-fill approach for delineating wetlands from a Digital Elevation Model (DEM) was presented as another way in which GIS methodology and Earth Observation (EO) data can provide practical means for assessing inaccessible and hard to traverse wetland systems