NASA Earth Resources Survey Symposium. Volume 3: Summary reports

This document contains the proceedings and summaries of the earth resources survey symposium, sponsored by the NASA Headquarters Office of Applications and held in Houston, Texas, June 9 to 12, 1975. Topics include the use of remote sensing techniques in agriculture, in geology, for environmental monitoring, for land use planning, and for management of water resources and coastal zones. Details are provided about services available to various users. Significant applications, conclusions, and future needs are also discussed

High Resolution Satellite Images to Reconstruct Recent Evolution of Domitian Coastline

In the last decades, combinations of natural and human factors have resulted in extensive morphological changes to our coastlines and in many cases have amplified erosion. In order to limit these changes and their impact on coastal zone, it is important to plan specific actions; for this purpose detailed cognizance of coastal zone is necessary. Different and heterogeneous data such as historical and recent maps, remotely sensed images and topographic survey result very useful to reconstruct temporal shoreline changes. In this study the attention is focalized on Domitian coastal zone (Italy), which is one of the most emblematic examples of coastal erosion in Europe. Study of the shoreline evolution in this area between 1876 and 2005 was used as the starting point of the present paper that investigates over a span of seven years (2005 to 2012), by using remotely sensed data. The aim is to adapt and integrate geomatics techniques to transform very high resolution satellite images in powerful tools to analyse coastline changes. So, in order to identify eroded and added areas, IKONOS-2 (2005), GeoEye-1 (2011) and WorldView-2 (2012) imageries are compared. These data-sets were re-georeferred to improve the positional accuracy. More over Normalized Difference Water Index (NDWI) was applied to pan-sharpened multispectral images to facilitate coastline vectorising at the same geometric resolution of panchromatic data. In addition, variance propagation was considered to establish the accuracy of the reconstruction of coastal evolution. Added and eroded areas were defined and related to the impact of the defence structures that were built in this zone in 2011

Harmonising topographic & remotely sensed datasets, a reference dataset for shoreline and beach change analysis.

This paper presents a novel reference dataset for North Norfolk, UK, that demonstrates the value of harmonising coastal field-based topographic and remotely sensed datasets at local scales. It is hoped that this reference dataset and the associated methodologies will facilitate the use of topographic and remotely sensed coastal datasets, as demonstrated here using open-access UK Environment Agency datasets. Two core methodologies, used to generate the novel reference dataset, are presented. Firstly, we establish a robust approach to extracting shorelines from vertical aerial photography, validated against LiDAR (Light Detection and Ranging) and coastal topography surveys. Secondly, we present a standard methodology for quantifying sediment volume change from spatially continuous LiDAR elevation datasets. As coastal systems are monitored at greater spatial resolution and temporal frequency there is an unprecedented opportunity to determine how and why coastal systems have changed in the past with a view to informing future forecasting. With revelation of trends that suggest increasing coastal risk, coastal change research is needed to inform the management and protection of coasts

Coastline shift analysis in data deficient regions: Exploiting the high spatio-temporal resolution Sentinel-2 products

In most developing countries, coastline shift monitoring using in-situ (ground-based) data faces challenges due, e.g., to data unreliability, inconsistency, deficiency, inaccessibility or incompleteness. Even where practically applicable, the traditional “boots on the ground” methods are labour intensive and expensive, thus imposing burden on poor countries struggling to meet other urgent pressing daily needs, i.e., food and medicine. Remote sensing (RS) techniques provide a more efficient and effective way of collecting data for coastline shift analysis. However, moderate spatio-temporal resolution RS products such as the widely used Landsat products (30 m and 16 days) may be insufficient where high accuracy is desired. In 2015, Sentinel-2 Multi-Spectral Instrument (MSI) remotely sensed products with higher spatio-temporal resolution (10 m and 5 days) and high spectral resolution (13 bands), which promises to improve coastline movement monitoring to high accuracy, was launched. Using two war-impacted countries (Liberia and Somalia) as case studies of regions with data deficiency or of poor quality, for the period 2015–2018, this contribution aims at (i) assessing the suitability of the new freely available high spatio-temporal Sentinel-2 products to monitor coastline shift, (ii) assessing the possibility of filling the missing Sentinel-2 gaps with Landsat 8 panchromatic band (15 m) products to provide alternative data source for mapping of coastline movements where Sentinel-2 data is unusable, e.g., due to cloud cover, and (iii), undertake a comparative analysis between Sentinel-2 (10 m), Landsat panchromatic (15 m), and Landsat multi-spectral (30 m). The results of the evaluation indicate 23% (on average) improvement gained by using Sentinel-2 compared to the traditional Landsat 30 m resolution data (i.e., 32% for Liberia and 14% for Somalia). A comparison of 100 check points from Google Earth Pro (i.e., surrogate in-situ reference data) show 91% agreement for Liberia and 85% for Somalia, indicating the potential of using Sentinel-2 data for future coastal shift studies, particularly for the data deficient regions. The results of comparative studies for Sentinel-2, Landsat panchromatic (PAN), and Landsat multi-spectral (MS) show that the percentages of Sentinel-2 and Landsat PAN that falls within 10 m threshold is much higher than Landsat MS by 35% and 26%, respectively, and for the 2016–2017 period, they provide more detailed mapping of the Liberian coastline compared to Landsat MS (30 m). Finally, panchromatic Landsat data with 15 m resolution are found to be capable of filling the missing Sentinel-2 gaps, i.e., where cloud cover hampers its usability

Application of ERTS-1 data to integrated state planning in the state of Maryland

There are no author-identified significant results in this report

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

Avaliação da evolução do índice de vegetação de teledetecção usando de técnicas de processamento de imagens

Vegetation has a substantial role as an indicator of anthropic effects, specifically in cases where urban planning is required. This is especially the case in the management of coastal cities, where vegetation exerts several effects that heighten the quality of life (alleviation of unpleasant weather conditions, mitigation of erosion, aesthetics, among others). For this reason, there is an increased interest in the development of automated tools for studying the temporal and spatial evolution of the vegetation cover in wide urban areas, with an adequate spatial and temporal resolution. We present an automated image processing workflow for computing the variation of vegetation cover using any publicly available satellite imagery (ASTER, SPOT, LANDSAT, MODIS, among others) and a set of image processing algorithms specifically developed. The automatic processing methodology was developed to evaluate the spatial and temporal evolution of vegetation cover, including the Normalized Difference Vegetation Index (NDVI), the vegetation cover percentage and the vegetation variation. A prior urban area digitalization is required. The methodology was applied in Monte Hermoso city, Argentina. The vegetation cover per city block was computed and three transects over the city were outlined to evaluate the changes in NDVI values. This allows the computation of several information products, like NDVI profiles, vegetation variation assessment, and classification of city areas regarding vegetation. The information is available in GIS-readable formats, making it useful as support for urban planning decisions.A vegetação tem um papel importante como indicador de efeitos antrópicos, especificamente nos casos em que o planejamento urbano é necessário. Este é especialmente o caso na gestão de cidades costeiras, onde a vegetação exerce diversos efeitos que elevam a qualidade de vida (alívio de condições climáticas desagradáveis, mitigação da erosão, estética, entre outras). Por essa razão, há um interesse crescente no desenvolvimento de ferramentas automatizadas para o estudo da evolução temporal e espacial da cobertura vegetal em grandes áreas urbanas, com adequada resolução espacial e temporal. Apresentamos um fluxo de trabalho automatizado de processamento de imagens para calcular a variação da cobertura vegetal usando qualquer imagem de satélite publicamente disponível (ASTER, SPOT, LANDSAT, MODIS, entre outros) e um conjunto de algoritmos de processamento de imagem desenvolvidos especificamente. A metodologia de processamento automático foi desenvolvida para avaliar a evolução espacial e temporal da cobertura vegetal, incluindo o Índice de Vegetação da Diferença Normalizada (NDVI), o percentual de cobertura vegetal e a variação da vegetação. Uma digitalização prévia da área urbana foi necessária. A metodologia foi aplicada na cidade de Monte Hermoso, na Argentina. A cobertura vegetal por quarteirão foi computada e três transectos sobre a cidade foram delineados para avaliar as mudanças nos valores de NDVI. Isso permite o cálculo de vários produtos de informação, como perfis de NDVI, avaliação da variação da vegetação e classificação das áreas da cidade em relação à vegetação. A informação está disponível em formatos legíveis pelo GIS, tornando-a útil como suporte para decisões de planejamento urbano.Fil: Revollo Sarmiento, Natalia Veronica. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Revollo Sarmiento, Gisela Noelia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages". Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo Desages"; ArgentinaFil: Huamantinco Cisneros, María Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentin

FINE SCALE MAPPING OF LAURENTIAN MIXED FOREST NATURAL HABITAT COMMUNITIES USING MULTISPECTRAL NAIP AND UAV DATASETS COMBINED WITH MACHINE LEARNING METHODS

Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes

Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview

Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment