Independent Accuracy Assessment Research Articles

Investigating the proficiency of an AI tool in summarizing foot and ankle literature: A quantitative, qualitative and accuracy analysis

Artificial Intelligence (AI) is rapidly transforming various sectors of industry, including the medical community, due to its ability to simulate human intelligence processes. This study evaluates the capacity of an AI tool, ChatGPT 3.5, to summarize scientific papers in the foot and ankle surgery literature, comparing its performance to summaries written by podiatric surgery residents. Quantitative and Qualitative analyses were performed, including BLEU and ROUGE metrics, Flesch Reading Ease Score (FRES), Flesch-Kincaide Grade Level (FKGL) readability statistics, qualitative analysis by independent reviewers, and independent accuracy assessments. Results indicate that AI-generated summaries closely resemble those produced by podiatric surgery residents in terms of content, readability, and accuracy. Although differing slightly from their human-generated counterparts with a higher level of writing, AI-generated summaries offer consistency and conciseness. Overall, this study demonstrates the potential of AI tools to streamline research processes while emphasizing the importance of judicious use and oversight to maintain scholarly integrity.

Mapping methane reduction potential of tidal wetland restoration in the United States

Coastal wetlands can emit excess methane in cases where they are impounded and artificially freshened by structures that impede tidal exchange. We provide a new assessment of coastal methane reduction opportunities for the contiguous United States by combining multiple publicly available map layers, reassessing greenhouse gas emissions datasets, and applying scenarios informed by geospatial information system and by surveys of coastal managers. Independent accuracy assessment indicates that coastal impoundments are under-mapped at the national level by a factor of one-half. Restorations of freshwater-impounded wetlands to brackish or saline conditions have the greatest potential climate benefit of all mapped conversion opportunities, but were rarer than other potential conversion events. At the national scale we estimate potential emissions reduction for coastal wetlands to be 0.91 Teragrams of carbon dioxide equivalents year−1, a more conservative assessment compared to previous estimates. We provide a map of 1,796 parcels with the potential for tidal re-connection.

Assessing the suitability of urban-oriented land cover products for mapping rural settlements

In recent years, new settlement mapping products have become available at the global and continental scale. Although accuracy assessments have indicated the high quality of these products, assessments were performed mainly on urban areas. However, there is also a need to monitor rural settlement development, as it is often located in proximity to biodiversity hotspots. In this paper, we verified the suitability of three settlement products (i.e., Global Urban Footprint – GUF, European Settlement Map – ESM and Open Street Map – OSM) to detect rural settlements in the Carpathian ecoregion. Two independent accuracy assessments indicated that the GUF captures rural settlements most effectively (overall accuracy – OA – 65.4% and 92.5% depending on the procedure). In contrast, the ESM overestimated settlements (OA – 49.5% and 90.8%), while the OSM (OA – 61.2% and 90.2%) was the most inconsistent source of settlement data. A regional comparison indicated some deviations from these accuracies, reflecting the variability of settlement structures within the study area. This study highlights that although the GUF was the best-performing product in mapping rural settlements across the whole study area, the settlement information it provided was rather conservative, and rural settlements are still insufficiently represented in all tested datasets.

Comparing Fire Extent and Severity Mapping between Sentinel 2 and Landsat 8 Satellite Sensors

Mapping of fire extent and severity across broad landscapes and timeframes using remote sensing approaches is valuable to inform ecological research, biodiversity conservation and fire management. Compiling imagery from various satellite sensors can assist in long-term fire history mapping; however, inherent sensor differences need to be considered. The New South Wales Fire Extent and Severity Mapping (FESM) program uses imagery from Sentinel and Landsat satellites, along with supervised classification algorithms, to produce state-wide fire maps over recent decades. In this study, we compared FESM outputs from Sentinel 2 and Landsat 8 sensors, which have different spatial and spectral resolutions. We undertook independent accuracy assessments of both Sentinel 2 and Landsat 8 sensor algorithms using high-resolution aerial imagery from eight training fires. We also compared the FESM outputs from both sensors across 27 case study fires. We compared the mapped areas of fire severity classes between outputs and assessed the classification agreement at random sampling points. Our independent accuracy assessment demonstrated very similar levels of accuracy for both sensor algorithms. We also found that there was substantial agreement between the outputs from the two sensors. Agreement on the extent of burnt versus unburnt areas was very high, and the severity classification of burnt areas was typically either in agreement between the sensors or in disagreement by only one severity class (e.g., low and moderate severity or high and extreme severity). Differences between outputs are likely partly due to differences in sensor resolution (10 m and 30 m pixel sizes for Sentinel 2 and Landsat 8, respectively) and may be influenced by landscape complexity, such as terrain roughness and foliage cover. Overall, this study supports the combined use of both sensors in remote sensing applications for fire extent and severity mapping.

Agricultural crop discrimination in a heterogeneous low-mountain range region based on multi-temporal and multi-sensor satellite data

Crop type information such as its location and spatial distribution is relevant for agricultural planning and decision making about food sustainability and security. This information can be obtained through the analysis of images obtained through optical satellite remote sensing. Activities such as accurate discrimination of crops require dense time-series of satellite data which can capture the diverse crop phenology. However, given the presence of clouds at important periods of crops’ development, the required time-series is impossible to obtain from just one optical satellite sensor. The Harmonized Landsat and Sentinel-2 (HLS) project by NASA provides fused data from both Operational Land Imager and Multispectral Instrument optical sensors of Landsat and Sentinel systems respectively. The present study used a multi-temporal HLS data and a target-oriented cross-validation (TOV) modelling approach with random forest algorithm to discriminate 13 crop types. 15 phenological metrics derived from time-series HLS data, together with 48 spectral and 2 topographic information were used as predictors in the model. A forward feature selection (FFS) procedure of the TOV was used to improve the classification model. 16 predictors comprising of spectral, phenological and topographic information were selected as useful for the crop discrimination. An independent accuracy assessment of the final model based on the selected predictors by the FFS procedure resulted in an overall accuracy of 76%. While most of the crop classes, achieved higher producer’s and user’s accuracies (>80%), the discrimination accuracies of potato, summer oat and winter triticale were comparatively low (<60%). Based on the outcome of the FFS, three models consisting of different predictor combinations, were further developed to ascertain the contribution of the predictors. A comparison of the models showed that the addition of phenological metrics increased the discrimination performance of the model as a whole and for cereals in particular. However, the inclusion of the topographic information did not have any significant effect on the overall model performance. Therefore, future crop type discrimination research in areas with similar terrain characteristics like Northern Hesse should reconsider the use of topographic predictors. Overall, our findings depict that the TOV modelling approach based on multi-temporal and multi-sensor data is promising for effective, accurate and practical crop type discrimination.

Estimates of Forest Canopy Height Using a Combination of ICESat-2/ATLAS Data and Stereo-Photogrammetry

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1–10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

High-resolution remote sensing and advanced classification techniques for the prospection of archaeological sites’ markers: The case of dung deposits in the Shashi-Limpopo Confluence area (southern Africa)

Archaeological prospection through remote sensing is based on the contrast between areas of archaeological interest and their surroundings. It has been used as the cheapest and the fastest way of locating and documenting areas of archaeological interest since the 1920s, initially with the aid of film-based aerial photographs. In recent years, there has been a shift towards the use of multispectral satellite data in prospecting for archaeological sites because of their ability to give information on spectral characteristics of archaeological material beyond the visible spectrum. However, spectral signatures for identifying archaeological sites are not universal, and an assessment of the applicability of remote sensing techniques in different archaeological landscapes is needed. This study tests the feasibility of prospecting for archaeological sites previously occupied by farming communities in the Shashi-Limpopo Confluence Area of southern Africa, using very high-resolution satellite WorldView-2 images. It also assesses the performance of advanced classification algorithms (support vector machine and random forest) and the contribution of new WorldView-2 bands in detecting archaeological sites. Two independent accuracy assessments were carried out, using a data set collected by Huffman (2011, 2009a) and a randomly generated holdout test dataset, respectively. The results demonstrate the potential of remote sensing methods in prospecting for archaeological sites previously occupied by farming communities using very high-resolution satellite images and advanced classification algorithms. Very high overall accuracies were achieved by: random forest, 95.29% using holdout sample and 97.71% using independent dataset; support vector machine, 88.82% using holdout sample and 95.88% using independent dataset, respectively. The new WorldView-2 bands were of least importance (compared to traditional bands) in detecting sites in Shashi-Limpopo Confluence Area. Despite high classification accuracies achieved by both classifiers, there were some misclassifications between vitrified dung sites and river sand. Therefore, to address this problem, this study recommends the use of robust classifiers such as object-based algorithms because of their ability to segment an image into homogenous objects and classify them using a combination of spectral, textual, sub-pixels, spatial, relational and contextual methods.

An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January–June 2016 and period 2: July–December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size = 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94%, with a producer’s accuracy of 85.9% (or omission error of 14.1%), and user’s accuracy of 68.5% (commission error of 31.5%) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015. The online product, referred to as the Global Food Security-support Analysis Data @ 30-m for the African Continent, Cropland Extent product (GFSAD30AFCE) is distributed through the NASA’s Land Processes Distributed Active Archive Center (LP DAAC) as (available for download by 10 November 2017 or earlier): https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001 and can be viewed at https://croplands.org/app/map. Causes of uncertainty and limitations within the crop extent product are discussed in detail.

A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles

Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better.

High-resolution global maps of 21st-century annual forest loss: Independent accuracy assessment and application in a temperate forest region of Atlantic Canada

Monitoring forest ecosystems requires accurate and up-to-date information on the type and extent of forest depletions, which may exist but are not always open and transparent. The ever growing, freely accessible Landsat archive provides data to derive such information; however, the manipulation of raw imagery can constitute a barrier to those lacking remote sensing expertise. The Landsat-based, global dataset of annual forest loss (version 1.0, Hansen et al., 2013) makes such information readily available. While the accuracy of this dataset has been assessed at the global scale, its applicability for reliable local monitoring of forest harvesting has not yet been validated. Here we undertake such an exercise in a temperate forest in Atlantic Canada. We used a census, polygon-based approach to comprehensively assess thematic, temporal and structural accuracy. We vectorized Hansen's forest loss raster for the 8,520km2 of public lands in the Miramichi River basin (13,496km2), which yielded 9299 polygons of 1ha minimum size. Then we used the provincial forest harvest inventory as reference. User's and producer's accuracies were 81% and 82% based on area, and 86% and 85% based on polygon counts. Detection probability decreased with decreasing cutblock size and harvest intensity. From all Hansen polygons, 85% had the correct harvest year and 88% structurally matched one or more reference polygons either alone or together with other Hansen polygons. After the validation, we used the Hansen dataset to derive trends for the entire basin. Mean annual harvest rate was 0.92ha/km2/year between 2000 and 2012. Most of the catchments around the western headwaters of the Miramichi River underwent intensive harvesting, underscoring the need of further monitoring. Our results indicate that the Hansen dataset could be used as a surrogate harvest layer for temperate forests where clear-cutting is common and fire is rare.

Global mapping of artificial surfaces at 30-m resolution

Abstract Urbanization is expected to accelerate with population growth and economic development at the global scale. The artificial surface is the main land cover form of urbanization. On the one hand, urbanization provides spaces for industry, economic activities and residence. On the other hand, artificial surfaces change the earth surface to a large extent, thus significantly affecting natural processes such as the heat exchange, hydrological processes and ecological balance. Therefore, the global mapping of artificial surfaces is valuable for both natural science and social science. This study produced the global artificial surface maps at 30-m resolution for two base-years using the satellite images acquired around 2000 and 2010. First, we proposed a new definition of “artificial surface” based on patch level with consideration of its geographic meaning and image features at 30-m resolution. Second, pixel-based and object-based image processing techniques were combined for the extraction of artificial surface patches. Finally, human editing and a quality control system were employed to guarantee the quality of global mapping. Independent accuracy assessments show that the user’s accuracy of this product is higher than 80%. It can be concluded that the product is the most reliable one among all the available global datasets of artificial surfaces (or related types). The data can significantly contribute to various research fields, such as urbanization and ecosystem assessment.

Contemporary forest loss in Ireland; quantifying rare deforestation events in a fragmented forest landscape

Accurate quantification of forest loss is required to meet international reporting requirements, even in countries where deforestation rates are low. In Ireland, recent evidence has suggested the rate of gross annual deforestation is increasing. However, no spatially explicit data on the extent and character of contemporary deforestation exists. Here, we quantify deforestation in a region where forest loss is rare. Deforestation estimates derived from wall-to-wall photointerpretation, official records (e.g. felling licences), the CORINE land-use/land cover changes dataset and a combined approach (hereafter termed “the Deforestation Map”) are compared in two regions in Ireland for the period 2000 to 2012. Deforestation area based on the Deforestation Map (1497 ha) was greater than estimates derived from using photo-interpretation (730 ha), official records (908 ha) and CORINE (139 ha) alone. Independent accuracy assessment highlighted high errors of omission for photo-interpretation (68.9%), official records (66.7%) and CORINE (91.84%) estimates compared to the Deforestation Map (20%). No general increase in the deforestation rate during the study period was recorded, despite regional variations. Post deforestation land-use transitions were principally to wetland, grassland and settlement although the magnitude and proportion of change varied regionally. Gross annual deforestation was higher in older broadleaf forests than in conifer plantation forests, a surprising finding considering the small area and conservation status of many broadleaf forests in Ireland. For countries with small forest area and/or low rates of deforestation, the use of methodologies employed herein can provide a valuable record of forest loss and be used to validate sample-based or remotely sensed deforestation estimates.

Comparison of remotely-sensed surveys vs. in situ plot-based assessments of sea grass condition in Barnegat Bay-Little Egg Harbor, New Jersey USA

With the increasing appreciation that sea grass habitats are in global decline, there is a great need to be able to efficiently and effectively assess and characterize the status and trends of sea grass in our coastal ecosystems. This paper examines the utility of remotely sensed vs. in situ plot-based monitoring using the Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, USA estuarine system as a case study. Eelgrass (Zostera marina) is the dominant species, while widgeon grass (Ruppia maritima) is also common in lower salinity regions of the BB-LEH. Aerial imagery collected during the months of July and August 2009 was interpreted and mapped using object based image analysis techniques, similar to techniques used in the 2003 mapping survey of this system. Boat-based in situ monitoring data were collected concurrently with the aerial photography to assist the image interpretation and for an independent accuracy assessment. We compared the remotely-sensed mapping of sea grass cover change (in 2003 vs. 2009) vs. in situ plot-based monitoring conducted from 2004 through 2009. Comparison of the remotely-sensed vs. the in situ plot-change analysis suggests that the two methodologies had broadly similarly results, with the percent area showing declines in sea grass cover greater than those that exhibited increases. In conclusion, the two studies provide corroborating evidence that sea grass has declined in percent cover in the BB-LEH system during the decade of the 2000’s. While remotely-sensed surveys provide synoptic information for a “big picture” view on sea grass distribution, site specific in situ sampling is required to determine other aspects of sea grass status, e.g. above vs. below-ground biomass, blade length, shoot density, epiphytic loading, etc. Either method alone gives an incomplete picture. As demonstrated in this study, to fully characterize the spatial extent, health, and density of sea grass meadows across the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the most robust approach.

IMPLEMENTATION AND EVALUATION OF A MOBILE MAPPING SYSTEM BASED ON INTEGRATED RANGE AND INTENSITY IMAGES FOR TRAFFIC SIGNS LOCALIZATION

Abstract. Recent advances in positioning techniques have made it possible to develop Mobile Mapping Systems (MMS) for detection and 3D localization of various objects from a moving platform. On the other hand, automatic traffic sign recognition from an equipped mobile platform has recently been a challenging issue for both intelligent transportation and municipal database collection. However, there are several inevitable problems coherent to all the recognition methods completely relying on passive chromatic or grayscale images. This paper presents the implementation and evaluation of an operational MMS. Being distinct from the others, the developed MMS comprises one range camera based on Photonic Mixer Device (PMD) technology and one standard 2D digital camera. The system benefits from certain algorithms to detect, recognize and localize the traffic signs by fusing the shape, color and object information from both range and intensity images. As the calibrating stage, a self-calibration method based on integrated bundle adjustment via joint setup with the digital camera is applied in this study for PMD camera calibration. As the result, an improvement of 83 % in RMS of range error and 72 % in RMS of coordinates residuals for PMD camera, over that achieved with basic calibration is realized in independent accuracy assessments. Furthermore, conventional photogrammetric techniques based on controlled network adjustment are utilized for platform calibration. Likewise, the well-known Extended Kalman Filtering (EKF) is applied to integrate the navigation sensors, namely GPS and INS. The overall acquisition system along with the proposed techniques leads to 90 % true positive recognition and the average of 12 centimetres 3D positioning accuracy.

IMPLEMENTATION AND EVALUATION OF A MOBILE MAPPING SYSTEM BASED ON INTEGRATED RANGE AND INTENSITY IMAGES FOR TRAFFIC SIGNS LOCALIZATION

Abstract. Recent advances in positioning techniques have made it possible to develop Mobile Mapping Systems (MMS) for detection and 3D localization of various objects from a moving platform. On the other hand, automatic traffic sign recognition from an equipped mobile platform has recently been a challenging issue for both intelligent transportation and municipal database collection. However, there are several inevitable problems coherent to all the recognition methods completely relying on passive chromatic or grayscale images. This paper presents the implementation and evaluation of an operational MMS. Being distinct from the others, the developed MMS comprises one range camera based on Photonic Mixer Device (PMD) technology and one standard 2D digital camera. The system benefits from certain algorithms to detect, recognize and localize the traffic signs by fusing the shape, color and object information from both range and intensity images. As the calibrating stage, a self-calibration method based on integrated bundle adjustment via joint setup with the digital camera is applied in this study for PMD camera calibration. As the result, an improvement of 83% in RMS of range error and 72% in RMS of coordinates residuals for PMD camera, over that achieved with basic calibration is realized in independent accuracy assessments. Furthermore, conventional photogrammetric techniques based on controlled network adjustment are utilized for platform calibration. Likewise, the well-known Extended Kalman Filtering (EKF) is applied to integrate the navigation sensors, namely GPS and INS. The overall acquisition system along with the proposed techniques leads to 90% true positive recognition and the average of 12 centimetres 3D positioning accuracy.

Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets

The Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view Earth observing instrument that builds on the heritage of the Along Track Scanning Radiometer (ATSR) series. SLSTR is planned for launch in 2013 on Sentinel-3, with two concurrently operating satellites planned for daily global coverage. Here we review the aspects of the SLSTR instrument design specifically targeted at biomass burning events, including operation of the SWIR channels at night and the inclusion of low-gain middle infrared and thermal IR channels that will minimise saturation over even high intensity fires. We detail the active fire detection and fire radiative power dataset to be provided from these SLSTR observations, which will be part of the SLSTR land product suite aimed at supporting both Global Monitoring for Environment and Security (GMES) operational services and scientific applications. We describe in detail the pre-launch active fire product algorithm, which uses data from the SLSTR near-nadir scan. The algorithm detects pixels containing actively burning fires, and uses the MIR radiance method to estimate their fire radiative power (FRP). We test the algorithm using a series of EOS MODIS scenes covering a range of fire-affected forest and savannah environments, comparing performance to that of the existing MODIS MOD14 ‘Fire and Thermal Anomaly’ products. Across 385 scenes covering Africa, South America and Australia, we find that the SLSTR algorithm applied to MODIS data detects in total 20% more fire pixels than does the MOD14 algorithm applied to the same data. Some scenes show very large differences, while others showed no differences, and some of the extra detections made by SLSTR maybe false alarms. For a better evaluation, we use the simultaneous high spatial resolution active fire detections made from ASTER to provide an independent accuracy assessment. Across 45 separate geographical regions covered simultaneously by ASTER and MODIS, we find that the SLSTR algorithm in fact detects 13% more correctly identified clusters of active fire pixels than the MOD14 algorithm, and that these contain 36% more active fire pixels. In particular, the SLSTR algorithm shows increased detection probabilities at small/low FRP fires, mainly due to the more liberal characteristics of its potential fire pixel detection stage. This performance enhancement comes, however, at the expense of a small (<2%) increase in commission error (i.e. false alarm rate) when compared to MOD14. The SLSTR algorithms ability to better detect low FRP fires maybe important, since these are usually the most common component of a region's fire regime.