Evaluation of ERTS-1 data for inventory of forest and rangeland and detection of forest stress

The author has identified the following significant results. Results of photointerpretation indicated that ERTS is a good classifier of forest and nonforest lands (90 to 95 percent accurate). Photointerpreters could make this separation as accurately as signature analysis of the computer compatible tapes. Further breakdowns of cover types at each site could not be accurately classified by interpreters (60 percent) or computer analysts (74 percent). Exceptions were water, wet meadow, and coniferous stands. At no time could the large bark beetle infestations (many over 300 meters in size) be detected on ERTS images. The ERTS wavebands are too broad to distinguish the yellow, yellow-red, and red colors of the dying pine foliage from healthy green-yellow foliage. Forest disturbances could be detected on ERTS color composites about 90 percent of the time when compared with six-year-old photo index mosaics. ERTS enlargements (1:125,000 scale, preferably color prints) would be useful to forest managers of large ownerships over 5,000 hectares (12,500 acres) for broad area planning. Black-and-white enlargements can be used effectively as aerial navigation aids for precision aerial photography where maps are old or not available

Evaluation of Skylab (EREP) data for forest and rangeland surveys

The author has identified the following significant results. Four widely separated sites (near Augusta, Georgia; Lead, South Dakota; Manitou, Colorado; and Redding, California) were selected as typical sites for forest inventory, forest stress, rangeland inventory, and atmospheric and solar measurements, respectively. Results indicated that Skylab S190B color photography is good for classification of Level 1 forest and nonforest land (90 to 95 percent correct) and could be used as a data base for sampling by small and medium scale photography using regression techniques. The accuracy of Level 2 forest and nonforest classes, however, varied from fair to poor. Results of plant community classification tests indicate that both visual and microdensitometric techniques can separate deciduous, conifirous, and grassland classes to the region level in the Ecoclass hierarchical classification system. There was no consistency in classifying tree categories at the series level by visual photointerpretation. The relationship between ground measurements and large scale photo measurements of foliar cover had a correlation coefficient of greater than 0.75. Some of the relationships, however, were site dependent

An integrated study of earth resources in the State of California based on ERTS-1 and supporting aircraft data, volume 1

There are no author-identified significant results in this report

Detecting informal buildings from high resolution quickbird satellite image, an application for insitu [sic.] upgrading of informal setellement [sic.] for Manzese area - Dar es Salaam, Tanzania.

Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.Documentation and formalization of informal settlements ("insitu" i.e. while people continue to live in the settlement) needs appropriate mapping and registration system of real property that can finally lead into integrating an informal city to the formal city. For many years extraction of geospatial data for informal settlement upgrading have been through the use of conventional mapping, which included manual plotting from aerial photographs and the use of classical surveying methods that has proved to be slow because of manual operation, very expensive, and requires well-trained personnel. The use of high-resolution satellite image like QuickBird and GIS tools has recently been gaining popularity to various aspects of urban mapping and planning, thereby opening-up new opportunities for efficient management of rapidly changing environment of informal settlements. This study was based on Manzese informal area in the city of Dar es salaam, Tanzania for which the Ministry of Lands and Human Settlement Development is committed at developing strategic information and decision making tools for upgrading informal areas using digital database, Orthophotos and Quickbird satellite image. A simple prototype approach developed in this study, that is, 'automatic detection and extraction of informal buildings and other urban features', is envisaged to simplify and speedup the process of land cover mapping that can be used by various governmental and private segments in our society. The proposed method, first tests the utility of high resolution QuickBird satellite image to classify the detailed 11 classes of informal buildings and other urban features using different image classification methods like the Box, maximum likelihood and minimum distance classifier, followed by segmentation and finally editing of feature outlines. The overall mapping accuracy achieved for detailed classification of urban land cover was 83%. The output demonstrates the potential application of the proposed approach for urban feature extraction and updating. The study constrains and recommendations for future work are also discussed

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations

t Variability of solar surface irradiances in the 1-minute range is of interest especially for solar energy applications. Eight variability classes were previously defined for the 1 min resolved direct normal irradiance (DNI) variability inside an hour. In this study spatial structural parameters derived fromsatellite-based cloud observations are used as classifiers in order to detect the associated direct normal irradiance (DNI) variability class in a supervised classification scheme. A neighbourhood of 3×3 to 29×29 satellite pixels is evaluated to derive classifiers describing the actual cloud field better than just using a single satellite pixel at the location of the irradiance observation. These classifiers include cloud fraction in a window around the location of interest, number of cloud/cloud free changes in a binary cloud mask in this window, number of clouds, and a fractal box dimension of the cloud mask within the window. Furthermore, cloud physical parameters as cloud phase, cloud optical depth, and cloud top temperature are used as pixel-wise classifiers. A classification scheme is set up to search for the DNI variability class with a best agreement between these classifiers and the pre-existing knowledge on the characteristics of the cloud field within each variability class from the reference data base. Up to 55 % of all DNI variability class members are identified in the same class as in the reference data base. And up to 92 % cases are identified correctly if the neighbouring class is counted as success as well – the latter is a common approach in classifying natural structures showing no clear distinction between classes as in our case of temporal variability. Such a DNI variability classification method allows comparisons of different project sites in a statistical and automatic manner e.g. to quantify short-term variability impacts on solar power production. This approach is based on satellite-based cloud observations only and does not require any ground observations of the location of interest

A New Data Processing System for Generating Sea Ice Surface Roughness and Cloud Mask Data Products from the Multi-Angle Imaging SpectroRadiometer (MISR)

This study describes two novel data products derived from Multi-angle Imaging SpectroRadiometer (MISR) imagery: Arctic-wide maps of sea ice roughness and a binary cloud detection algorithm. The sea ice roughness maps were generated using a data processing system that matched MISR pixels with co-located and concurrent lidar-derived roughness measurements from Airborne Topographic Mapper (ATM), calibrated the multi- angle data to values of surface roughness using a K-Nearest Neighbor (KNN) algorithm, and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April and July 2016. The resulting maps show good agreement with independent ATM roughness data and enable characterization of the roughness of different ice types. The binary cloud detection algorithm was developed using a neural network approach and a training dataset constructed from Top-of-Atmosphere red band values from all MISR’s nine different viewing cameras for the same two months in various regions of the Arctic. The algorithm showed good performance in classifying pixels into cloudy and clear categories in MISR images, with better performance for clear pixels in April 2016 and better performance for cloudy pixels in July 2016. The algorithm also provides a significant advantage over existing MISR cloud mask products SDCM and ASCM in terms of accuracy and spatial resolution, with a resolution of 275 meters. The data products presented here can be used to gain insights into the seasonal and interannual changes in sea ice roughness and cloud cover over the Arctic and to develop and improve more accurate classification algorithms in the field of remote sensing