Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach

Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern “very high resolution” or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow

Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series

Greenhouse mapping through remote sensing has received extensive attention over the last decades. In this article, the innovative goal relies on mapping greenhouses through the combined use of very high resolution satellite data (WorldView-2) and Landsat 8 Operational Land Imager (OLI) time series within a context of an object-based image analysis (OBIA) and decision tree classification. Thus, WorldView-2 was mainly used to segment the study area focusing on individual greenhouses. Basic spectral information, spectral and vegetation indices, textural features, seasonal statistics and a spectral metric (Moment Distance Index, MDI) derived from Landsat 8 time series and/or WorldView-2 imagery were computed on previously segmented image objects. In order to test its temporal stability, the same approach was applied for two different years, 2014 and 2015. In both years, MDI was pointed out as the most important feature to detect greenhouses. Moreover, the threshold value of this spectral metric turned to be extremely stable for both Landsat 8 and WorldView-2 imagery. A simple decision tree always using the same threshold values for features from Landsat 8 time series and WorldView-2 was finally proposed. Overall accuracies of 93.0% and 93.3% and kappa coefficients of 0.856 and 0.861 were attained for 2014 and 2015 datasets, respectively

Assessment of high spatial resolution satellite imagery for monitoring riparian vegetation: riverine management in the smallholding

Riverine habitats are essential ecotones that bridge aquatic and terrestrial ecosystems, providing multiple ecosystem services. This study analyses the potential use of high-resolution satellite imagery, provided by the WorldView-2 satellite, in order to assess its viability for monitoring riparian ecosystems. It is performed by calculating the riparian strip quality index (RSQI) and calibrating it with the riparian quality index (QBR). The methodology was implemented in the Umia River, which is characterised by elevated anthropogenic pressures (located in the northwest of Spain). The results obtained by the method have a 92% of veracity and a kappa coefficient of 0.88. The average quality value obtained for the RSQI index was 71.57, while the average value for the QBR was 55.88. This difference could be attributed to the fact that the former does not differ between autochthonous and non-autochthonous vegetation. The areas with more accurate mapping corresponded to stretches of vegetation with optimal cover (80–50%), with good connectivity with the adjacent forest ecosystem and few or no presence of invasive plants. The worst-scoring sites had the next characteristics: low connectivity (< 10%), low forest cover (< 10%) and a higher presence of invasive plants. The degradation of vegetation could be explained by the presence of agriculture and deficient land use rationing caused by the type of ownership of the study area. The application of this index through satellite images will facilitate the environmental governance of multiple ecosystems and in special riparian ecosystems, obtaining a quick and objective methodology, easily replicable in other basins.Universidade de Vigo/CISUGXunta de Galicia | Ref. ED431B 2022/1

Comparing Three Spaceborne Optical Sensors via Fine Scale Pixel-based Urban Land Cover Classification Products

Accessibility to higher resolution earth observation satellites suggests an improvement in the potential for fine scale image classification. In this comparative study, imagery from three optical satellites (WorldView-2, Pleiades and RapidEye) were used to extract primary land cover classesfrom a pixel-based classification principle in a suburban area. Following a systematic working procedure, manual segmentation and vegetation indices were applied to generate smaller subsets to in turn develop sets of ISODATA unsupervised classification maps. With the focus on the land cover classification differences detected between the sensors at spectral level, the validation of accuracies and their relevance for fine scale classification in the built-up environment domain were examined. If an overview of an urban area is required, RapidEye will provide an above average (0.69 k) result with the built-up class sufficiently extracted. The higher resolution sensors such as WorldView-2 and Pleiades in comparison delivered finer scale accuracy at pixel and parcel level with high correlation and accuracy levels (0.65-0.71k) achieved from these two independent classifications

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

Examining Longleaf Pine spectral properties to remotely map relict stands in Central North Carolina

This thesis has been prepared as a manuscript for submission and potential publication in a peer-reviewed academic journal. This thesis investigates the unique spectral reflectance properties of 109 “montane” longleaf pine canopies (Pinus palustris Mill.) growing on steep, south-facing slopes as well as 51 “piedmont” individuals growing in an area of low topographic relief, all found within the Uwharrie National Forest in central North Carolina. The geographic location of all sampled longleaf canopies were recorded on a digital map, and then spectrally analyzed to derive unique reflectance signatures that would allow for the remote mapping of the species using high-resolution multispectral WorldView-2 satellite imagery. Overall accuracies for classification procedures range from 91–96% between four study sites. Longleaf pine spectral properties were statistically investigated to quantify differences in reflectance due to topography and canopy height. Significant relationships (p &lt;0.05) were found for each variable, and suggest that spectral reflectance values for longleaf pine are not uniform throughout the study area and can vary according to topographic and morphological canopy features

Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastalwaters of Karachi. The continuous monitoring and mapping of this precious marine plant and their breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation wasachieved with WV-2 data that shows 15.5Ha (0.155 Km2)of seaweed cover along Karachi coast that is more representative of the field observed data. A much larger area wasestimated with Landsat 8 image (71.28Ha or 0.7128 Km2) that was mainly due to the mixing of seaweed pixels with water pixels. The WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat8 in mapping seaweed patche

Archaeological prospection with corona and wv-3 satellite imagery of the archaeological site of zar tepe (uzbekistan)

Abstract. The use of different data from satellite platforms for archaeological prospecting and remote sensing has been applied since the end of the 20th century. Although the current use of drones with different visible and multispectral sensors for small areas has partially replaced in some cases the use of this type of satellite information due to its higher spatial resolution. The historical importance of satellite imagery is essential to find out about and compare the transformations of the archaeological landscape of the last 60 years since the CORONA satellite program started in 1960. In this paper we propose the evaluation of a proposal for the automation of processes of two photographic reconnaissance correlative satellite programs CORONA (1960-1972), HEXAGON (1971-1986) declassified since 1995 and 2011 respectively, and the commercial satellite WorldView-3 (WV3) (2014) for use in the detection of buried archaeological structures at the archaeological site of Zar Tepe in the southeast of Uzbekistan. This is a site located in the Surkhan Darya region very little known between the first century BC and the fourth century AD. This methodology is part of the IPAEB project (International Pluridisciplinary Archaeological Expedition to Bactria) led by the University of Barcelona, the University of Salamanca and recently in 2019 the University of Zaragoza. IPAEB was started in 2006 in the South of Uzbekistan and is currently trying to explore the urban planning of the Zar Tepe archaeological site and the elements that make up its natural physical environment: evidence of communication routes, smallholdings, irrigation channels, fences and sources of raw materials