Low Vegetation Areas Research Articles

Remote sensing metrics to assess exposure to residential greenness in epidemiological studies: A population case study from the Eastern Mediterranean

Introduction/aimsApplication of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. MethodsNDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. ResultsFor the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. ConclusionsLSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in dense urban areas. Therefore, LSU potentially improves exposure assessment precision, implying reduced exposure misclassification.

Habitat Selection of Skylarks During the Breeding Season on the Volcanic Slope of Mt. Tarumae

The Eurasian Skylark, Alauda arvensis, is a passerine bird that mainly breeds in open habitats, such as lowland grasslands and farmlands. However, recent studies have shown that they form breeding territories on volcanic and alpine slopes in central/northern Japan. During the 2018 breeding season, we investigated the density and habitat selection of Skylarks on the volcanic slopes above timber line, of Mt. Tarumae, Hokkaido. Our results showed that there were 27 Skylark territories in the study area, with a density of 7.26 territories per square kilometer. This spatial arrangement was dense around the eastern slope. Skylarks favored habitats with a low vegetation height. The quantity of short plant species, i.e. Carex oxyandra and Arcterica nana was larger in territories than in randomly selected points. C. oxyandra plays important roles not only in forming low vegetation, but also in available and limited nest material in their breeding territories on the volcanic slopes. These results suggest that Skylarks occupy areas of low vegetation with C. oxyandra, as there are limited resources available for nest materials within the breeding territories that are on the volcanic slope above a timber line.

Satellite‐Derived Global Surface Water Extent and Dynamics Over the Last 25 Years (GIEMS‐2)

AbstractA method has been developed to extend the Global Inundation Estimate from Multiple Satellites (GIEMS). The method presented here is based on retrieval principals similar to GIEMS but with an updated estimation of microwave emissivity in order to be less dependent on ancillary data and with some changes to the final surface water estimation to correct a known overestimation over low vegetation areas. The new methodology, GIEMS‐2, provides monthly estimates of surface water extent, including open water, wetlands, or rice paddies, and it has been applied to the Special Sensor Microwave/Imager and the Special Sensor Microwave Imager Sounder intercalibrated observations to produce a global data record of surface water extent from 1992 to 2015, on an equal area grid of 0.25° × 0.25° at the equator (∼25 km). The time series have been thoroughly evaluated: they are seamless and do not show any obvious artifact related to changes in satellite instrumentation over the ∼25 years. Comparisons with precipitation estimates show good agreement, displaying expected patterns related to surface conditions and precipitation regimes. The temporal variability of basin‐averaged estimates has also been compared with altimeter river height, showing a reasonable agreement. Production will be continued up to current time as soon as the observations become available, with efforts to improve the spatial and temporal resolutions of the estimates currently underway.

Spacialization of the Degradation Risk in the Natural Monument of the São Francisco River

Understanding changes related to environmental degradation by parameters such as Normalized Difference Vegetation Index (NDVI), Surface Albedo (α) and Moving Standard Deviation Index (MDSI) has been of great relevance in the study of environmental impacts. The objective of the present study was to analyze the evolution of soil degradation and also of the soil use and occupation in the San Francisco River Natural Monument, using surface data and images from Landsat-5 and Landsat-8, for the 1987, 1997, 2007 and 2017 years. Remote sensing techniques were used to estimate indices such as NDVI, albedo (α) and MDSI. The change detection technique and decision tree classification based on predefined rules in NDVI, albedo and MDSI were applied to infer degradation, soil use and occupation. There was a significant increase in degradation, especially for areas with high degradation. Vegetation indices showed the lowest values for areas of low vegetation and exposed soil, being the highest values found for Caatinga dense vegetation. It was concluded that the change detection technique and decision tree classification were efficient in identifying the degradation during the study period. The change detection technique algorithm was more sensitive to water bodies than the change intensity technique.

Factors promoting the recolonization of Oahu, Hawaii, by Bristle-thighed Curlews

Suitable habitat for Arctic-breeding migratory shorebirds is decreasing at their traditional wintering islands and atolls in the Central Pacific Flyway (i.e., Oceania) due to habitat degradation, reclamation, and sea-level rise. To maintain the size and resiliency of their populations, migratory shorebirds will need to expand their winter ranges by either colonizing new sites or recolonizing old sites from which they were extirpated. Bristle-thighed Curlews (Numenius tahitiensis) are long-distance migratory shorebirds that breed only in Alaska and winter across a vast region of the Central Pacific, typically on remote, unpopulated islands and atolls. Historically, Bristle-thighed Curlews were considered uncommon transients on the main Hawaiian Islands, but in the mid-1990s, curlews became regular visitors to Oahu and subsequently began wintering at the James Campbell National Wildlife Refuge on Oahu Island in Hawaii. Curlew numbers at this site grew steadily from <5 birds in the mid-1990s to an estimated 126 winter residents (95%CI 108–147) in 2013–2014. Timing of the recolonization event coincided with the establishment of a fenced pond complex that was managed for endangered waterbirds by maintaining areas of shallow water and low vegetation. High rates of apparent annual survival exhibited by adults and subadults (0.86–0.95) confirmed the suitability of the Refuge to curlews. Our results suggest that curlews in Oceania can naturally recolonize wintering islands, a trait that may be key to the survival of this species of conservation concern in an era of rising sea levels.

Repeat-pass SAR interferometry for land cover classification: A methodology using Sentinel-1 Short-Time-Series

In this paper we explore the potential of repeat-pass interferometric SAR (InSAR) for land cover classification purposes. We introduce a novel approach for the generation of large-scale thematic maps, based on the use of multi-temporal data, acquired over short observation intervals (short-time-series). A larger interferometric coherence loss is expected with the increasing time difference between two interferometric acquisitions. This phenomenon is normally indicated as temporal decorrelation whose amount differs depending on the type of imaged target on ground. The basic idea is therefore to accurately model the evolution in time of the temporal decorrelation and use the estimated parameters, together with backscatter, as input features for the Random Forest machine learning classification algorithm. The work has been carried out on the case study of land cover mapping over central Europe, considering Sentinel-1 C-band interferometric stacks, acquired over a time span of about one month. Three different land cover classes have been considered: artificial surfaces as e.g. urban areas, forests, and non-forested areas as the ensemble of low vegetation, bare soil, and agricultural areas. The results show a level of agreement above 91%, when compared to the CORINE land cover map product of 2012, which has been used as external reference for both training and testing of the classification algorithm.

Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

In this study, we evaluated the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products and compared them with the Collection 6 (C6) products over regions in China with multifarious underlying surfaces during 2001–2016. The AOD retrievals were validated against 20 AERONET (V3) sites, and the results show that the correlation coefficient (R) for dark target (DT) retrievals in C6.1 is 0.946, while the fraction within the expected error (EE) can be considered relatively low at only 54.03%. Deep blue (DB) retrievals in C6.1 have a slightly lower R value (0.931), but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the low elevation vegetation (LEV) areas. For the high elevation vegetation (HEV) areas, DB performs integrally better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the land cover types and surface reflectance. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DB tends to be high in areas where the main land cover type is arid or semiarid and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals in C6.1 increases slightly compared to C6, and the major improvements are observed for the coarse aerosol particles and for high elevation areas at surface reflectance from 0.11 to 0.2, respectively.

Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models

Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.

Evaluation of the Multilook Size in Polarimetric Optimization of Differential SAR Interferograms

The interferometric coherence is a measure of the correlation between two SAR images and constitutes a commonly used estimator of the phase quality. Its estimation requires a spatial average within a 2-D window, usually named as multilook. The multilook processing allows reducing noise at the expenses of a resolution loss. In this letter, we analyze the influence of the multilook size while applying a polarimetric optimization of the coherence. The same optimization algorithm has been carried out with different multilook sizes and also with the nonlocal SAR filter filter, which has the advantage of preserving the original resolution of the interferogram. Our experiments have been carried out with a single pair of quad-polarimetric RADARSAT-2 images mapping the Mount Etna's volcanic eruption of May 2008. Results obtained with this particular data set show that the coherence is increased notably with respect to conventional channels when small multilook sizes are employed, especially over low-vegetated areas. Conversely, very decorrelated areas benefit from larger multilook sizes but do not exhibit an additional improvement with the polarimetric optimization.

Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p=H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω=0 and HR=0.1, whereas values of ω=0.06 − 0.08 and HR=0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p=H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω=0.10, HR=0.4 and NRp=−1 (p=H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R=0.61, bias=−0.019m3m−3, ubRMSE=0.062m3m−3 for the IGBP-based calibration; against R=0.54, bias=−0.034m3m−3 and ubRMSE=0.070m3m−3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product.

Landuse and NDVI change analysis of Sperchios river basin (Greece) with different spatial resolution sensor data by Landsat/MSS/TM and OLI

The assessment of land use–land cover (LULC) and normalized difference vegetation index (NDVI) changes on hydrology is essential for the development of sustainable water resource strategies. This study focuses on Sperchios river catchment, a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities. Difficulties such as multi-seasonal spectral variables corresponding to different stages of land cover phenological development were addressed by choosing an unsupervised classification algorithm called k-means in combination with the Corine land cover database and past scientific studies. Available remote sensing data included three Landsat images (MSS, TM, OLI) all of which were resampled to a common 30-m resolution. Final LULC classifications in the period 1972–2013 separated forest, agricultural land and heath land and detected unified bare with urban land and pastures with areas of low vegetation. The recorded results include a significant reduction (–80%) of vegetation (NDVI values), 28 and 26% reduction rates in forests and pastures-low vegetation, respectively, a strong increase (114%) in heath land and an 47% increase, as concerns the agricultural land. Using of random points estimated the classification accuracy to be 75.4 and 85.2% for LULC of 1972–2013, respectively. This particular study constitutes a preliminary stage of an attempted integrated basin management of Sperchios river intending to contribute to overall ecological quality assessment of Sperchios river.

SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates

Abstract This research aims to test data obtained by level 2 retrieval algorithm of SMOS over land, in order to provide information regarding vegetation and soil moisture over forested areas. Results presented in this paper were obtained using the last 620 version of the algorithm. The correlation between the new vegetation optical depth (VOD) product and the height of the forest estimated by ICES at GLAS lidar on a global scale is investigated. Over South American and African forests a good correspondence between the two variables is observed, with saturation occurring above about 30 m height. Moreover, the comparison between the VOD and the height of the forest shows good spatial and temporal stability, and the r 2 correlation coefficient is within a 0.59–0.69 range. Conversely, discrepancies are observed in some Indonesian islands, particularly New Guinea. Over specific areas, the trends vs. forest height obtained with SMOS VOD are compared with the corresponding trends of AMSR-E VOD. Results are also validated at country-level scale. To this aim, accurate estimates of forest biomass derived from airborne lidar over selected forests of Peru, Columbia and Panama are used. Finally, the soil moisture retrieved over forests is investigated, reporting continental maps for Tropical areas and comparisons with ground measurements in selected forests of the US. Continental maps obtained with the new level 2 V620 algorithm cover almost all forest areas, and show seasonal variations which are dependent on climatic zones. Comparisons between soil moisture retrievals in forests and ground measurements of the US SCAN network produce worse RMSE values with respect to low vegetation areas. Significant improvements however are achieved after averaging among close nodes of the ground network.

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40–50km resolution SMOS and AMSR-E data are disaggregated at 1km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010–2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.

Classification of high resolution satellite images using spatial constraints-based fuzzy clustering

A spatial constraints-based fuzzy clustering technique is introduced in the paper and the target application is classification of high resolution multispectral satellite images. This fuzzy-C-means (FCM) technique enhances the classification results with the help of a weighted membership function (Wmf). Initially, spatial fuzzy clustering (FC) is used to segment the targeted vegetation areas with the surrounding low vegetation areas, which include the information of spatial constraints (SCs). The performance of the FCM image segmentation is subject to appropriate initialization of Wmf and SC. It is able to evolve directly from the initial segmentation by spatial fuzzy clustering. The controlling parameters in fuzziness of the FCM approach, Wmf and SC, help to estimate the segmented road results, then the Stentiford thinning algorithm is used to estimate the road network from the classified results. Such improvements facilitate FCM method manipulation and lead to segmentation that is more robust. The results confirm its effectiveness for satellite image classification, which extracts useful information in suburban and urban areas. The proposed approach, spatial constraint-based fuzzy clustering with a weighted membership function (SCFCWmf), has been used to extract the information of healthy trees with vegetation and shadows showing elevated features in satellite images. The performance values of quality assessment parameters show a good degree of accuracy for segmented roads using the proposed hybrid SCFCWmf-MO (morphological operations) approach which also occluded nonroad parts.

Assessment of the EUMETSAT LSA-SAF evapotranspiration product for drought monitoring in Europe

Evapotranspiration is a key parameter for water stress assessment as it is directly related to the moisture status of the soil-vegetation system and describes the moisture transfer from the surface to the atmosphere. With the launch of the Meteosat Second Generation geostationary satellites and the setup of the Satellite Application Facilities, it became possible to operationally produce evapotranspiration data with high spatial and temporal evolution over the entire continents of Europe and Africa. In the frame of this study we present an evaluation of the potential of the evapotranspiration (ET) product from the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA-SAF) for drought assessment and monitoring in Europe.To assess the potential of this product, the LSA-SAF ET was used as input for the ratio of ET to reference evapotranspiration (ET0), the latter estimated from the ECMWF interim reanalysis. In the analysis two case studies were considered corresponding to the drought episodes of spring/summer 2007 and 2011. For these case studies, the ratio ET/ET0 was compared with meteorological drought indices (SPI, SPEI and Sc-PDSI for 2007 and SPI for 2011) as well as with the anomalies of the fraction of absorbed photosynthetic active radiation (fAPAR) derived from remote sensing data. The meteorological and remote sensing indicators were taken from the European Drought Observatory (EDO) and the CARPATCLIM climatological atlas.Results show the potential of ET/ET0 to characterize soil moisture variability, and to give additional information to fAPAR and to precipitation distribution for drought assessment. The main limitations of the proposed ratio for drought characterization are discussed, including options to overcome them. These options include the use of filters to discriminate areas with a low percentage vegetation cover or areas that are not in their growing period and the use of evapotranspiration without water restriction (ETwwr), obtained as output of the LSA-SAF model instead of ET0. The ET/ETwwr ratio was tested by comparing its accumulated values per growing period with the winter wheat yield values per country published by Eurostat. The results point to the potential of using the remote sensing based LSA-SAF evapotranspiration and the ET/ETwwr ratio for vegetation monitoring at large scale, especially in areas where data is generally lacking.

First Report of Zonate Leaf Spot Caused by Hinomyces moricola on Japanese Hop in Korea.

Japanese hop (Humulus japonicus Siebold & Zucc. = H. scandens (Lour.) Merr.), native to East Asia, is an annual, climbing or trailing vine. The vines can spread to cover large areas of open ground or low vegetation, eventually blanketing the land and vegetation. Pollen of H. japonicus is allergenic, and this species is considered as one of the important causes of pollinosis in Korea and China. It is a notorious invasive weed in the United States and also in France, Hungary, and Italy (1). In September 2012, zonate leaf spots were observed on Japanese hops growing in wetlands in Yeongdong County of Korea. A voucher specimen was preserved in the Korea University Herbarium (KUS-F26901). Initial symptoms included grayish-green to grayish-brown spots without border lines. As the lesions enlarged, they coalesced, leading to leaf blight. Sporophores on the leaf lesions were dominantly hypophyllous, rarely epiphyllous, solitary, erect, easily detachable, and as long as 700 μm. The upper portion of the sporophores consisted of a pyramidal head was ventricose, 320 to 520 μm long and 110 to 150 μm wide. The fungus was isolated from leaf lesions and maintained on potato dextrose agar (PDA). Sclerotia were produced on PDA after 4 to 5 weeks at 18°C without light, but conidia were not observed in culture. These morphological and cultural characteristics were consistent with those of Hinomyces moricola (I. Hino) Narumi-Saito & Y. Harada (= Cristulariella moricola (I. Hino) Redhead) (3,4). An isolate was preserved in the Korean Agricultural Culture Collection (Accession No. KACC46955). Genomic DNA was extracted using the DNeasy Plant Mini DNA Extraction Kit (Qiagen Inc., Valencia, CA). The complete internal transcribed spacer (ITS) region of rDNA was amplified with the primers ITS1/ITS4 and sequenced. The resulting sequence of 452 bp was deposited in GenBank (Accession No. KC460209). A BLAST search in GenBank revealed that the sequence showed an exact match with those of C. moricola (JQ036181 ex Acer negundo and JQ036182 ex Glycine max). To determine the pathogenicity of the fungus, according to the procedure of Cho et al. (2), sporophores with the pyramidal head were carefully detached from a lesion on the naturally infected leaf using a needle. Each sporophore was transferred individually onto five places of four detached healthy leaves. The leaves were placed in dew chambers and incubated at 16°C. Symptoms were observed after 2 days on all inoculated leaves. A number of sporophores and immature sclerotia which were morphologically identical to the ones observed in the field were formed on the abaxial surface of the leaf 2 weeks after inoculation. The pathogen was reisolated from lesions on the inoculated leaves, confirming Koch's postulates. No symptoms were observed on the control leaves kept in humid chambers for 2 weeks. H. moricola was known to cause zonate leaf spots and defoliation on a wide range of woody and annual plants (3). To the best of our knowledge, this is the first report of Hinomyces infection on Japanese hops in Korea.

PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LIDAR INTENSITY DATA

Abstract. Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel- based classification technique; and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object- based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.

LAND COVER INFORMATION EXTRACTION USING LIDAR DATA

Abstract. Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined by the LiDAR sensors. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LiDAR data only (range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights, Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LiDAR Data as one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis (PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the four bands extracted from the Principal Component Analysis (PCA).

An airborne LiDAR-based methodology for vineyard parcel detection and delineation

An airborne lidar-based technique to delineate vineyard parcels from surrounding land uses is proposed and assessed in the Texas Hill Country American Viticultural Area near Austin, Texas, USA. Although most vineyard site analyses are based on multispectral aerial and satellite images, this study takes advantage of the height-based uniqueness of vineyard land uses inherent in the vine-trellising structure to differentiate vineyard areas from non-vineyard areas. A normalized digital surface model was created from lidar data and smoothed with a focal statistics method to identify vine rows and delineate vineyard land-use parcels. A simple unsupervised classification of the three study sites was performed to identify low vegetation areas. The vineyard areas were extracted from the low vegetation class and compared with manually digitized versions. The results suggest that lidar-based data sets can efficiently differentiate vineyard from non-vineyard land use. Our study yielded a mean classification accuracy of 97.55% and successfully extracted vineyard parcel area (mean accuracy 88.79%).