Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping

Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km² pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover.     

Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery

Accelerated soil erosion, high sediment yields, floods and debris flow are serious problems in many areas of Iran, and in particular in the Golestan dam watershed, which is the area that was investigated in this study. Accurate land use and land cover (LULC) maps can be effective tools to help soil erosion control efforts. The principal objective of this research was to propose a new protocol for LULC classification for large areas based on readily available ancillary information and analysis of three single date Landsat ETM+ images, and to demonstrate that successful mapping depends on more than just analysis of reflectance values. In this research, it was found that incorporating climatic and topographic conditions helped delineate what was otherwise overlapping information. This study determined that a late summer Landsat ETM+ image yields the best results with an overall accuracy of 95%, while a spring image yields the poorest accuracy (82%). A summer image yields an intermediate accuracy of 92%. In future studies where funding is limited to obtaining one image, late summer images would be most suitable for LULC mapping. The analysis as presented in this paper could also be done with satellite images taken at different times of the season. It may be, particularly for other climatic zones, that there is a better time of season for image acquisition that would present more information.     

Problems of bias in mapping linear landforms from satellite imagery

Remotely sensed images are an important data source for the mapping of glacial landforms and the reconstruction of past glacial environments. However the results produced can differ depending on a wide range of factors related to the type of sensors used and the characteristics of the landforms being mapped. This paper uses a range of satellite imagery to explore the three main sources of variation in the mapped results: relative size, azimuth biasing and landform signal strength. Recommendations include the use of imagery illuminated with low solar elevation, although an awareness of the selective bias introduced by solar azimuth is necessary. Landsat ETM+ imagery meets the requirements for glacial landform mapping and is the recommended data source. However users may well have to consider alternative data in the form of SPOT, Landsat TM or Landsat MSS images. Digital elevation models should also be considered a valuable data source.     

Integration of WorldView-2 and airborne LiDAR data for tree species level carbon stock mapping in Kayar Khola watershed,Nepal

Integration of WorldView-2 satellite image with small footprint airborne LiDAR data for estimation of tree carbon at species level has been investigated in tropical forests of Nepal. This research aims to quantify and map carbon stock for dominant tree species in Chitwan district of central Nepal. Object based image analysis and supervised nearest neighbor classification methods were deployed for tree canopy retrieval and species level classification respectively. Initially, six dominant tree species (Shorea robusta, Schima wallichii, Lagerstroemia parviflora, Terminalia tomentosa, Mallotus philippinensis and Semecarpus anacardium) were able to be identified and mapped through image classification. The result showed a 76% accuracy of segmentation and 1970.99 as best average separability. Tree canopy height model (CHM) was extracted based on LiDAR’s first and last return from an entire study area. On average, a significant correlation coefficient (r) between canopy projection area (CPA) and carbon; height and carbon; and CPA and height were obtained as 0.73, 0.76 and 0.63, respectively for correctly detected trees. Carbon stock model validation results showed regression models being able to explain up to 94%, 78%, 76%, 84% and 78% of variations in carbon estimation for the following tree species: S. robusta, L. parviflora, T. tomentosa, S. wallichii and others (combination of rest tree species).     

Semi-automated detection of landslide timing using harmonic modelling of satellite imagery,Buckinghorse River,Canada

We manually detected and mapped 66 landslides from Landsat imagery over a 33-year period from 1985 to 2017 in the Buckinghorse River region, British Columbia, Canada. We semi-automatically determined landslide timing using the cumulative difference (CD) between the normalized difference vegetation index (NDVI) and a fitted harmonic sinusoidal curve (CDNDVI). The semi-automated dating method was capable of determining the timing of 80% of the landslides using CDNDVI and 85% of the landslides after detrending CDNDVI (dCDNDVI). The CDNDVI method generally detects landslides too early and the dCDNDVI method is generally too late. Mean absolute errors (in days) are lower for the dCDNDVI (208 and 188) than the CDNDVI (227 and 267), respectively. This study, however, has many examples of extreme outliers with very large errors (>1000 days). Our method is portable to other remote regions as long as vegetation anomalies can be used as an indicator for landslide activity. We conclude that the timeseries of images available in the Landsat Archive are useful for landslide mapping, but the pixel size limits the size of the landslides that can be mapped.     

Searching for ancient gold mines in Filippoi area,Macedonia, Greece,using Worldview-2 satellite imagery

The study area is located near the town of Filippoi, north of the city of Kavala in northern Greece, known from ancient times for its rich gold mines, situated inside hydrothermal alteration zones (Fe–Mn oxide minerals). A Very High-Resolution (0.5 m pixel size) image of Worldview-2 satellite was digitally enhanced, yielding target areas of potential ore existence and lineaments. Ground-truth that followed digital image processing, revealed abandoned ancient mines, slags and ore occurrences. Also, a number of lineaments delineated on the satellite image were verified as faults.     

Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery

Prescribed fire is crucial to the ecology and maintenance of tallgrass prairie, and its application affects a variety of human and natural systems. Consequently, maps showing the location and extent of these fires are critical to managing tallgrass prairies in a manner that balances the needs of all stakeholders. Satellite-based optical remote sensing can provide the necessary input for this mapping, but it requires the development mapping methods that are specific to tallgrass prairie. In this research, we devise and test a suitable mapping method by comparing the efficacy of seven combinations of bands and indices from the MODIS sensor using both pixel and object-based classification methods. Due to the relatively small size of many prescribed fires in tallgrass prairie, scenarios based on the 250 m spatial resolution red and NIR bands outperformed those based on the coarser 500 m spatial resolution bands, and a combination of both red and NIR performed better than each 250 m band individually. Object-based classification offered no improvement over pixel-based classification, and performed poorer in some cases. Our results suggest that mapping burned areas in tallgrass prairie should be done at a minimum of 250 m spatial resolution, should used a pixel-based classification technique, and should use a combination of red and NIR.     

Automated detection of slum area change in Hyderabad,India using multitemporal satellite imagery

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.     

A novel approach to optimize hierarchical vegetation mapping from hyper-temporal NDVI imagery,demonstrated at national level for Namibia

This paper presents a novel methodological approach to countrywide vegetation mapping. We used green vegetation biomass over the year as captured by coarse resolution hyper-temporal NDVI satellite-imagery, to generate vegetation mapping units at the biome, ecoregion and at the next lower hierarchical level for Namibia, excluding the Zambezi Region. Our method was based on a time series of 15 years of SPOT-VGT-MVC images each representing a specific 10-day period (dekad). The ISODATA unsupervised clustering technique was used to separately create 2–100 NDVI-cluster maps. The optimal number of temporal NDVI-clusters to represent the information on vegetation contained in the imagery was established by divergence separability statistics of all generated NDVI-clusters. The selected map consisted of legend of 81 cluster-specific temporal NDVI-profiles covering each a 15-year period of averaged NDVI data representing all pixels classified to that cluster. Then, by legend-entry using the dekad-medians of all 15 annual repeats, we produced generalized legend-entries without year-specific anomalies for each cluster. Subsequently, a hierarchical cluster analysis of these temporal NDVI-profiles was used to produce a dendrogram that generated grouping options for the 81 legend-entries. Maps with cluster-groups of 8 and 4 legend-entries resulted. The 81-cluster map and its 65 legend-entries vector version have no equivalent in published vegetation maps. The 8 cluster-group map broadly corresponds with published ecoregion level maps and the 4 cluster-group map with the published biome maps in their number of legend units. The published vegetation maps varied considerably from our NDVI-profile maps in the location of mapping unit boundaries. The agreement index between our map and published biome maps ranges from 70−93. For the ecoregion level, the agreement index is much lower, namely 51−75. Our methodological approach showed a considerably higher discretionary power for hierarchical levels and the number of vegetation mapping units than the approaches applied to previously published maps. We recommended an approach to transform our three hyper-temporal NDVI-profiles based legend-entries into more specific vegetation units. This might be accomplished by re-analysis of available, spatially-comprehensive plant species occurrence data.     

The application of day and night time ASTER satellite imagery for geothermal and mineral mapping in East Africa

There is considerable interest in optimizing geothermal exploration techniques via the mapping of alteration and evaporate mineralisation, as well as of thermal emissions associated with geothermally active areas on the Earth’s surface. Optical and thermal satellite sensor technologies, improvements in processing algorithms and the means for large scale (e.g. 1:250,000) spatial data distribution are required for detecting both these attributes. The extensive visible, -near, -shortwave and thermal infrared (VNIR-SWIR-TIR) data archive acquired by the multi-spectral Advanced Spaceborne Thermal Emission Reflectance Radiometer (ASTER) provides a rich source of geoscience related imagery for geothermal exploration. Examples of generating large scale mosaicked ASTER imagery to provide province to continental mineral mapping have been undertaken in areas including such as Australia, western USA, Namibia and Zagros Mountains Iran. In addition, ASTER’s thermal infrared imagery also provides night time land surface temperature (LST) estimates relevant for detecting possible geothermal related anomalies.This study outlines existing methods for the application of ASTER data for geothermal exploration in East Africa. The study area encompasses a section of the East African Rift System across the Tanzanian and Kenyan border. The area includes rugged volcanic terrain which has had geological mapping of limited coverage at detailed scales, from various heritages and mapping agencies. This study summarizes the technology, the processing methodology and initial results in applying ASTER imagery for such compositional and thermal anomaly mapping related to geothermal activity. Fields observations have been used from the geothermal springs of Lake Natron, Tanzania, and compared with ASTER derived spectral composition and land surface temperature results. Published geothermal fields within the Kenyan portion of the study area have also been incorporated into this study.     

Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan,Indonesia

Monitoring loss of humid tropical forests via remotely sensed imagery is critical for a number of environmental monitoring objectives, including carbon accounting, biodiversity, and climate modeling science applications. Landsat imagery, provided free of charge by the U.S. Geological Survey Center for Earth Resources Observation and Science (USGS/EROS), enables consistent and timely forest cover loss updates from regional to biome scales. The Indonesian islands of Sumatra and Kalimantan are a center of significant forest cover change within the humid tropics with implications for carbon dynamics, biodiversity maintenance and local livelihoods. Sumatra and Kalimantan feature poor observational coverage compared to other centers of humid tropical forest change, such as Mato Grosso, Brazil, due to the lack of ongoing acquisitions from nearby ground stations and the persistence of cloud cover obscuring the land surface. At the same time, forest change in Indonesia is transient and does not always result in deforestation, as cleared forests are rapidly replaced by timber plantations and oil palm estates. Epochal composites, where single best observations are selected over a given time interval and used to quantify change, are one option for monitoring forest change in cloudy regions. However, the frequency of forest cover change in Indonesia confounds the ability of image composite pairs to quantify all change. Transient change occurring between composite periods is often missed and the length of time required for creating a cloud-free composite often obscures change occurring within the composite period itself. In this paper, we analyzed all Landsat 7 imagery with <50% cloud cover and data and products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify forest cover loss for Sumatra and Kalimantan from 2000 to 2005. We demonstrated that time-series approaches examining all good land observations are more accurate in mapping forest cover change in Indonesia than change maps based on image composites. Unlike other time-series analyses employing observations with a consistent periodicity, our study area was characterized by highly unequal observation counts and frequencies due to persistent cloud cover, scan line corrector off (SLC-off) gaps, and the absence of a complete archive. Our method accounts for this variation by generating a generic variable space. We evaluated our results against an independent probability sample-based estimate of gross forest cover loss and expert mapped gross forest cover loss at 64 sample sites. The mapped gross forest cover loss for Sumatra and Kalimantan was 2.86% of the land area, or 2.86 Mha from 2000 to 2005, with the highest concentration having occurred in Riau and Kalimantan Tengah provinces.     

CBERS-02B卫星数据在涪陵区土地利用分类中的应用

为推进国产卫星数据的应用,本文以CBERS-02B卫星的CCD数据和HR数据为数据源,以重庆市涪陵区为实验区,将DEM、NDVI数据作为补充,采用分层分类的方法,对实验区土地利用/土地覆盖现状信息进行提取,分析CBERS-02B数据的特点及在应用中存在的问题。研究结果表明,CBERS-02B数据可满足复杂地区土地利用宏观监测的需要。     

Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.     

Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields

This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R² of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R² and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.     

Small-scale surface mining of gold placers: Detection,mapping, and temporal analysis through the use of free satellite imagery

Informal small-scale mining is spread in many countries and provides livelihood to numerous families in rural areas yet often with devastating social and environmental impacts. The alluvial gold mining process in Colombia, also known as placer mining, involves excavations using heavy machinery and creates large footprints of bare soil and mining ponds. The very dynamic nature of this extractive activity and its spread in rural and remote areas make its mapping and monitoring very challenging. The use of freely available satellite data of the Copernicus programme provides great new possibilities to study these activities and provides stakeholders integrated data to better understand the spatial and temporal extent of the activities and mitigate affected areas. The objective of this work is to assess the potential of Sentinel-2 data to identify mining areas and to understand the dynamics in landcover change over a study area located at the border of the municipalities of El Bagre and Zaragoza in Bajo Cauca, Colombia. The study utilizes a classification approach followed by post-processing using field knowledge on a set of images from 2016 to 2019. Sequential pattern mining of classified images shows the likelihood of certain annual and seasonal changes in mining-impacted landcover and in the natural vegetation. The results show a slight reduction in the detected mining areas from 2016 to 2019. On the other hand, there are more mining activities in the dry season than in the wet season. Excavated areas of bare soil have a 50% chance to remain in excavation over the considered period or they transition to non-vegetated areas or mining ponds. Vegetation loss due to the extractive activities corresponds to about 35% while recovered vegetated areas are 7% of the total excavated areas in June 2019. An analysis of abandoned sites using NDVI shows that it takes a much longer period than the one considered in this paper for potential natural recovery of vegetation. Finally, the work was disseminated among stakeholders and the public on MapX (https://mapx.org), an online open platform for mapping and visualizing geospatial data on natural resources. It is a pilot study the will be the basis of the analysis of more regions in the department of Antioquia.     

A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery

Abstract

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.     

A general framework of TOPSIS method for integration of airborne geophysics,satellite imagery,geochemical and geological data

This work presents the promising application of three variants of TOPSIS method (namely the conventional, adjusted and modified versions) as a straightforward knowledge-driven technique in multi criteria decision making processes for data fusion of a broad exploratory geo-dataset in mineral potential/prospectivity mapping. The method is implemented to airborne geophysical data (e.g. potassium radiometry, aeromagnetic and frequency domain electromagnetic data), surface geological layers (fault and host rock zones), extracted alteration layers from remote sensing satellite imagery data, and five evidential attributes from stream sediment geochemical data. The central Iranian volcanic-sedimentary belt in Kerman province at the SE of Iran that is embedded in the Urumieh–Dokhtar Magmatic Assemblage arc (UDMA) is chosen to integrate broad evidential layers in the region of prospect. The studied area has high potential of ore mineral occurrences especially porphyry copper/molybdenum and the generated mineral potential maps aim to outline new prospect zones for further investigation in future. Two evidential layers of the downward continued aeromagnetic data and its analytic signal filter are prepared to be incorporated in fusion process as geophysical plausible footprints of the porphyry type mineralization. The low values of the apparent resistivity layer calculated from the airborne frequency domain electromagnetic data are also used as an electrical criterion in this investigation. Four remote sensing evidential layers of argillic, phyllic, propylitic and hydroxyl alterations were extracted from ASTER images in order to map the altered areas associated with porphyry type deposits, whilst the ETM+ satellite imagery data were used as well to map iron oxide layer. Since potassium alteration is generally the mainstay of porphyry ore mineralization, the airborne potassium radiometry data was used. The geochemical layers of Cu/B/Pb/Zn elements and the first component of PCA analysis were considered as powerful traces to prepare final maps. The conventional, adjusted and modified variants of the TOPSIS method produced three mineral potential maps, in which the outputs indicate adequately matching of high potential zones with previous working and active mines in the region.     

Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area,SE Spain

A new approach to the analysis of hyperspectral data for the purpose of surface compositional mapping is presented in this paper. We use an interpolated value of the absorption band position and the absorption band depth for the diagnostic of mineral absorption features. Using thresholds for this depth and position, the data is transformed to indicator [0,1] values. By kriging these values, we obtain the probability of exceeding certain absorption depth and the probability of a pixel exhibiting absorption features within a specified wavelength region. Using Bayesian statistics, the indicator kriging derived probabilities are used to produce a hard classification result. By adapting the prior probabilities to the dominant mineralogy in the various alteration facies mapped, data stratification is achieved. The classification results are compared to results derived using the spectral angle mapper and maximum likelihood classification. In addition, the results are statistically compared to field spectral data classified into dominant mineralogy. The indicator approach and the spectral angle mapper produce favourable results relative to field data and in comparison to the maximum likelihood classifier. A data set from the Rodalquilar high-sulfidation epithermal gold system in SE Spain, consisting of HyMAP airborne imaging spectrometer data and ASD field spectra focusing on the key minerals alunite, kaolinite, illite and chlorite, is used to illustrate the methodology.     

Analysis of elevation changes in relation to surface characteristics for six glaciers in Northern Labrador,Canada using advanced space-borne thermal emission and reflection radiometer imagery

Advanced space-borne thermal emission and reflection radiometer imagery and Digital Elevation Models were used to analyse surface elevation changes of six glaciers in Northern Labrador. Results indicate an average surface thinning of0.94 ± 0.49 m y^?1 (water equivalent) between 2000 and 2009. Three glaciers had an average elevation change of ?1.16 ± 0.55 m y^?1 (water equivalent) whichis three times the thinning rate found in a study from 1981 to 1983 ?0.36 ± 0.10 m y^?1 water equivalent). Analysis of surface characteristics in relation to elevation changes shows expected results of rapid thinning in bare ice areas and near zero change in accumulation areas. Debris covered areas of three glaciers show expected results of moderate thinning, but three other glaciers indicate high rates of thinning. Variability in thinning rates suggests possible influences in the type ofdebris and/or variations in climate such as increased rainfall.     

A hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario,Canada

This study presents a hybrid framework for single tree detection from airborne laser scanning (ALS) data by integrating low-level image processing techniques into a high-level probabilistic framework. The proposed approach modeled tree crowns in a forest plot as a configuration of circular objects. We took advantage of low-level image processing techniques to generate candidate configurations from the canopy height model (CHM): the treetop positions were sampled within the over-extracted local maxima via local maxima filtering, and the crown sizes were derived from marker-controlled watershed segmentation using corresponding treetops as markers. The configuration containing the best possible set of detected tree objects was estimated by a global optimization solver. To achieve this, we introduced a Gibbs energy, which contains a data term that judges the fitness of the objects with respect to the data, and a prior term that prevents severe overlapping between tree crowns on the configuration space. The energy was then embedded into a Markov Chain Monte Carlo (MCMC) dynamics coupled with a simulated annealing to find its global minimum. In this research, we also proposed a Monte Carlo-based sampling method for parameter estimation. We tested the method on a temperate mature coniferous forest in Ontario, Canada and also on simulated coniferous forest plots with different degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering, thus increasing the overall detection accuracy by approximately 10% on all of the datasets.