Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery

Conversion of native forests to agriculture and urban land leads to fragmentation of forested landscapes with significant consequences for habitat conservation and forest productivity. When quantifying land-cover patterns from airborne or spaceborne sensors, the interconnectedness of fragmented landscapes may vary depending on the spatial resolution of the sensor and the extent at which the landscape is being observed. This scale dependence can significantly affect calculation of remote sensing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and its subsequent use to predict biophysical parameters such as the fraction of photosynthetically active radiation intercepted by forest canopies (fPAR). This means that simulated above-ground net primary productivity (NPP_A) using canopy radiation interception models such as 3-PG (Physiological Principles for Predicting Growth), coupled with remote sensing observations, can yield different results in fragmented landscapes depending on the spatial resolution of the remotely sensed data.We compared the amount of forest fragmentation in 1?km SPOT-4 VEGETATION pixels using a simultaneously acquired 20?m SPOT-4 multispectral (XS) image. We then predicted NPP_A for New Zealand native forest ecosystems using the 3-PG model with satellite-derived estimates of the fPAR obtained from the SPOT-4 VEGETATION sensor, using NDVI values with and without correction for fragmentation. We examined three methods to correct for sub-pixel fragmentation effects on NPP_A. These included: (1) a simple conversion between the broad 1?km scale NDVI values and the XS NDVI values; (2) utilization of contextural information from XS NDVI pixels to derive a single coefficient to adjust the 1?km NDVI values; and (3) calculation of the degree of fragmentation within each VEGETATION 1?km pixel and reduce NDVI by an empirically derived amount based on the proportional areal coverage of forest in each pixel. Our results indicate that predicted NPP_A derived from uncorrected 1?km VEGETATION pixels was significantly higher than estimates using adjusted NDVI values; all three methods reduced the predicted NPP_A. In areas of the landscape with a large degree of forest fragmentation (such as forest boundaries) predictions of NPP_A indicate that the fragmentation effect has implications for spatially extensive estimates of carbon uptake by forests.     

静止卫星图像热带气旋云系自动识别

热带气旋对我国东南沿海地区国民经济和人民生命财产威胁巨大,静止卫星云图是热带气旋实时监测的主要数据源。热带气旋在卫星云图上的纹理特征与其它云系相似度高,为气旋云系的自动准确提取带来困难。本文在矢量矩概念的基础上,提出了旋转系数的概念来表征热带气旋的形态本质特征从而实现热带气旋的自动识别。建立了基于静止卫星图像,运用最大类间方差法确定目标云系分割阈值,结合云系面积和亮温分布特性,利用旋转系数进行热带气旋云系自动识别的方法流程。以1211台风海葵为例,在台风生成发展期、成熟期以及消亡期内,进行了改进前后方法识别率的对比实验,统计发现改进方法的识别率分别为76%、95%、78%,均高于原始方法的59%、90%、63%。实验表明改进方法分割的热带气旋云系更为完整,对各阶段的热带气旋云系识别率均更高。     

Estimating woody browse abundance from aerial imagery

In the deciduous forests of the eastern US, timber harvest programmes are often designed to increase the availability of woody browse for terrestrial wildlife. However, assessing the efficacy of timber harvest at increasingly available browse has traditionally required labour‐intensive field‐based measurements of woody plant growth and abundance. The objective of this study was to use readily available digital aerial imagery to estimate the amount of woody browse in regenerating clearcuts in central West Virginia. Aerial imagery from the National Agriculture Imagery Program and woody browse data collected from 11 regenerating clearcuts in the summer of 2007 were used in this analysis. Red, green and blue visible bands, as well as a simple texture metric, were used to create a multiple linear regression model to predict the amount of woody browse. The final model exhibited large correlation (R ² = 0.94) and was statistically significant (F = 22.48, p = 0.0009), indicating that simple measures of image digital numbers and texture have potential utility in assisting forest and wildlife managers to assess habitat quality in forest regeneration areas.     

Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle.The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression.Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n = 31 circular nested sample plots of 154 m² for trees and shrubs and 1 m² for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs.Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions.The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE = 1.50 t/ha, SE% = 9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 t/ha with a root mean square error RMSE = 6.42 t/ha, RMSE_r = 44%) resulting from leave-one-out cross-validation. The k-NN-approach allows mapping and analysis of the spatial variability of AGC. The results show high spatial variability with AGC predictions ranging from 4.3 t/ha to 28.8 t/ha, reflecting the highly heterogeneous conditions in those permafrost-influenced landscapes. The means and totals of linear regression and k-NN predictions revealed only small differences but some regional distinctions were recognized in the maps.     

Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data

Estimates of forest area are among the most common and useful information provided by national forest inventories. The estimates are used for local and national purposes and for reporting to international agreements such as the Montréal Process, the Ministerial Conference on the Protection of Forests in Europe, and the Kyoto Protocol. The estimates are usually based on sample plot data and are calculated using probability-based estimators. These estimators are familiar, generally unbiased, and entail only limited computational complexity, but they do not produce the maps that users are increasingly requesting, and they generally do not produce sufficiently precise estimates for small areas. Model-based estimators overcome these disadvantages, but they may be biased and estimation of variances may be computationally intensive. The study objective was to compare probability- and model-based estimators of mean proportion forest using maps based on a logistic regression model, forest inventory data, and Landsat imagery. For model-based estimators, methods for evaluating bias and reducing the computational intensity were also investigated. Four conclusions were drawn: the logistic regression model exhibited no serious lack of fit to the data; all the estimators produced comparable estimates for mean proportion forest, except for small areas; probability-based inferences enhanced using maps produced increased precision; and the computational intensity associated with estimating variances for model-based estimators can be greatly reduced with no detrimental effects.     

Change detection of buildings from satellite imagery and lidar data

Geospatial objects change over time and this necessitates periodic updating of the cartography that represents them. Currently, this updating is done manually, by interpreting aerial photographs, but this is an expensive and time-consuming process. While several kinds of geospatial objects are recognized, this article focuses on buildings. Specifically, we propose a novel automatic approach for detecting buildings that uses satellite imagery and laser scanner data as a tool for updating buildings for a vector geospatial database. We apply the support vector machine (SVM) classification algorithm to a joint satellite and laser data set for the extraction of buildings. SVM training is automatically carried out from the vector geospatial database. For visualization purposes, the changes are presented using a variation of the traffic-light map. The different colours assist human operators in performing the final cartographic updating. Most of the important changes were detected by the proposed method. The method not only detects changes, but also identifies inaccuracies in the cartography of the vector database. Small houses and low buildings surrounded by high trees present significant problems with regard to automatic detection compared to large houses and taller buildings. In addition to visual evaluation, this study was checked for completeness and correctness using numerical evaluation and receiver operating characteristic curves. The high values obtained for these parameters confirmed the efficacy of the method.     

Extraction of bajadas from digital elevation models and satellite imagery

A methodology was designed for the extraction of bajadas from the 15 min US Geological Survey digital elevation models and Landsat Thematic Mapper imagery. The method was demonstrated for the Death Valley-California where progressive eastward tilting has enabled the west-side fans to coalesce and form bajadas. First, the drainage that crossed the uplands and the bajadas was extracted from the DEM. The drainage pixels were successively grown by checking the surrounding pixels on the basis of their gradient. It was concluded that for gradient in the interval [2°,11°] the upslope bajadas border was segmented. In order to eliminate the drainage pixels that belonged to the uplands, the drainage pixels were subtracted. Then, the isolated small 8-connected foreground pixels were identified and subtracted too. Finally, region growing was performed again to the remaining pixels with the same growing criterion. Isolated 8-connected background pixels, representing almost flat regions inside bajadas, were identified and merged to the segmented pixels. At the end, by taking into account the spectral response in the satellite image, the downslope border of bajadas was segmented. The extracted polygon was in agreement with the information depicted on (a) the US Geological Survey topographic map of scale 1:100,000 and (b) the satellite image and (c) the polygon classified manually by a photo-interpreter.     

Analysing potential cliff erosivity from ERS SAR satellite imagery

This paper demonstrates a method to elucidate potential erosivity (PE) of cliff strata that fall within the radar shadow of European Remote Sensing (ERS) Synthetic Aperture Radar (SAR) imagery. ERS-1 imagery of cliff faces in Israel's Negev Desert that faced 'away' from the satellite look direction contained alternating grey/black stripes that corresponded to the sedimentological units that make up the cliffs. High return values or digital numbers (DN) relate to gently sloping surfaces with softer lithologies that yield higher rates of weathering. Conversely, lower return values (DN) represent steeper surfaces with harder lithologies that yield a slower rate of weathering. Backscatter and Z-score values were extracted from the image to derive an index of PE for strata at the feature and sub-feature level. This method may be used to determine relative erosivity of cliff strata, compliment existing geological mapping techniques and refine topographical representation of cliff faces in existing digital elevation models (DEMs).     

Deep-PHURIE: deep learning based hurricane intensity estimation from infrared satellite imagery

Hurricanes are among the most destructive natural phenomena on Earth. Timely prediction and tracking of hurricane intensity is important as it can help authorities in emergency planning. Several manual, semi and fully automated techniques based on different principles have been developed for hurricane intensity estimation. In this paper, a deep convolutional neural network architecture is proposed for fully automated hurricane intensity estimation from satellite infrared (IR) images. The proposed architecture is robust to errors in annotation of the storm center with a smaller root-mean-squared error (RMSE) (8.82 knots) in comparison to the previous state of the art methods. A web server implementation of Deep-PHURIE and its pre-trained neural network model are available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#Deep-PHURIE.

     

Automatic detection and delineation of citrus trees from VHR satellite imagery

In this article, we present a novel approach to detecting and delineating individual citrus trees through very-high resolution (VHR) GeoEye-1 satellite images at two different test sites. The approach is based on vegetation extraction, fast radial symmetry (FRS) transform, and simple object-based hierarchical operations. Our basic assumption is that each citrus tree presents a symmetric feature in the image. Multiple parameter combinations were tested to determine the optimum parameter set. The results calculated with the combination of optimum parameters were then evaluated based on both pixel- and object-based approaches. Promising results (up to 90% accuracy) were obtained for both detection and delineation rates, especially in areas with regular planting patterns and minimum tree crown overlap. The results indicate that object-based evaluation improves the accuracy at certain detection and delineation rates.     

Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery

The k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest inventory plot data, the technique has been shown to produce useful estimates of many forest attributes including forest/non-forest, volume, and basal area. However, variance estimators for quantifying the uncertainty of means or sums of k-NN pixel-level predictions for areas of interest (AOI) consisting of multiple pixels have not been reported. The primary objectives of the study were to derive variance estimators for AOI estimates obtained from k-NN predictions and to compare precision estimates resulting from different approaches to k-NN prediction and different interpretations of those predictions. The approaches were illustrated by estimating proportion forest area, tree volume per unit area, tree basal area per unit area, and tree density per unit area for 10-km AOIs. Estimates obtained using k-NN approaches and traditional inventory approaches were compared and found to be similar. Further, variance estimates based on different interpretations of k-NN predictions were similar. The results facilitate small area estimation and simultaneous and consistent mapping and estimation of multiple forest attributes.     

A new approach to removing cloud cover from satellite imagery

A new approach is proposed for removing cloud clover from satellite imagery, based on O. R. Mitchell's model (O. R. Mitchell, B. J. Delp, and P. L. Chen, IEEE Trans. Geosci. Electron. GE-15, 1977, 137–141). It is useful in the case where clouds have relatively low spatial-frequency content compared to the ground reflectance. Results from a computer simulation are shown.     

Size distributions of clouds in real time from satellite imagery

Abstract

An instrument is proposed to gather global data on cloud size distributions. The instrument contains a broad-band visible/infrared radiometer to scan the clouds with a field of view comparable to the grid squares of circulation models, but a resolution within the field of view of approximately 100 m, adequate to resolve the fine structure of clouds. The image is digitized, discriminated to distinguish cloudy and clear pixels, and submitted to a dedicated parallel computer which calculates the size distribution of the clouds in real time. Only the size distribution is transmitted to Earth, so the data transmission rate from the satellite is low and the archival problems at the earth station are minimal. This paper examines several alternative definitions of the size distribution of clouds, and concludes that the procedure of ‘sizing by openings’ is most appropriate. It is then shown that the processing speed required by the image analyser can be achieved at realistic power levels with present day technology.     

Surface motion of mountain glaciers derived from satellite optical imagery

A complete and detailed map of the ice-velocity field on mountain glaciers is obtained by cross-correlating SPOT5 optical images. This approach offers an alternative to SAR interferometry, because no present or planned RADAR satellite mission provides data with a temporal separation short enough to derive the displacements of glaciers. The methodology presented in this study does not require ground control points (GCPs). The key step is a precise relative orientation of the two images obtained by adjusting the stereo model of one “slave”' image assuming that the other “master” image is well georeferenced. It is performed with numerous precisely-located homologous points extracted automatically. The strong ablation occurring during summer time on the glaciers requires a correction to obtain unbiased displacements. The accuracy of our measurement is assessed based on a comparison with nearly simultaneous differential GPS surveys performed on two glaciers of the Mont Blanc area (Alps). If the images have similar incidence angles and correlate well, the accuracy is on the order of 0.5 m, or 1/5 of the pixel size. Similar results are also obtained without GCPs. An acceleration event, observed in early August for the Mer de Glace glacier, is interpreted in term of an increase in basal sliding. Our methodology, applied to SPOT5 images, can potentially be used to derive the displacements of the Earth's surface caused by landslides, earthquakes, and volcanoes.     

On the interpretation and classification of mesoscale cyclones from satellite infrared imagery

Subjective interpretation of cloud imagery is commonly used to identify mesoscale cyclones in cold air streams (mesocyclones) over the southern oceans. To determine mesocyclone attributes, and evaluate the classification of their cloud vortex signatures, Defense Meteorological Satellite Program ( DMSP) Infrared (IR) imagery is analysed for selected transition and winter season months of 1988 and 1989. Mesocyclones occupy a statistically smaller size range compared with synoptic scale ( frontal) vortices, and have maximum frequencies of occurrence in transition season months. The mesocyclone classification scheme separates the different signature types most reliably in winter, which is also the season when they are most frequently represented on hemispheric-scale synoptic chart analyses. Characteristic patterns of cloud form and level associated with vortex types, provide insights into mesocyclone dynamics that are now being evaluated using microwave techniques.     

Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.     

Unsupervised building detection in complex urban environments from multispectral satellite imagery

A generic algorithm is presented for automatic extraction of buildings and roads from complex urban environments in high-resolution satellite images where the extraction of both object types at the same time enhances the performance. The proposed approach exploits spectral properties in conjunction with spatial properties, both of which actually provide complementary information to each other. First, a high-resolution pansharpened colour image is obtained by merging the high-resolution panchromatic (PAN) and the low-resolution multispectral images yielding a colour image at the resolution of the PAN band. Natural and man-made regions are classified and segmented by the Normalized Difference Vegetation Index (NDVI). Shadow regions are detected by the chromaticity to intensity ratio in the YIQ colour space. After the classification of the vegetation and the shadow areas, the rest of the image consists of man-made areas only. The man-made areas are partitioned by mean shift segmentation where some resulting segments are irrelevant to buildings in terms of shape. These artefacts are eliminated in two steps: First, each segment is thinned using morphological operations and its length is compared to a threshold which is determined according to the empirical length of the buildings. As a result, long segments which most probably represent roads are masked out. Second, the erroneous thin artefacts which are classified by principal component analysis (PCA) are removed. In parallel to PCA, small artefacts are wiped out based on morphological processes as well. The resultant man-made mask image is overlaid on the ground-truth image, where the buildings are previously labelled, for the accuracy assessment of the methodology. The method is applied to Quickbird images (2.4 m multispectral R, G, B, near-infrared (NIR) bands and 0.6 m PAN band) of eight different urban regions, each of which includes different properties of surface objects. The images are extending from simple to complex urban area. The simple image type includes a regular urban area with low density and regular building pattern. The complex image type involves almost all kinds of challenges such as small and large buildings, regions with bare soil, vegetation areas, shadows and so on. Although the performance of the algorithm slightly changes for various urban complexity levels, it performs well for all types of urban areas.     

ALADIN and WRF forecasting evaluation using satellite imagery

Simulated satellite images are a good indicator of the state of the atmosphere described by the fields predicted by numerical weather prediction (NWP) models. Therefore, in order to control NWP operational models used by the Algerian meteorological service, especially over the desert region, Meteosat Second Generation (MSG) simulated images of brightness temperature (BT) were generated from ALADIN (Aire Limitée Adaptation Dynamique Development International) and WRF (Weather and Research Forecasting) outputs using the Radiative Transfer for TIROS Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV9) model. As reference data, MSG images were used to compute certain deterministic and probabilistic statistical parameters. This version of the RTTOV model assimilates cumuliform clouds, stratiform clouds and those of upper levels, such as cirrus. This comparative study shows that WRF reproduces BTs well where they exist, but raises too many false alarms for very cold BTs with values of bias around 1. The number of false alarms greatly affects the quality of Heidke skill scores (HSS), unlike the ALADIN model, which reveals fewer false alarms but detects events less well. With low values of bias for the lowest temperatures, ALADIN HSS scores are better than those of WRF for the lowest BTs. The double-penalty impact is slightly lessened for local and convective cloud by about 10% with WRF compared with ALADIN.     

Estimating impervious surfaces from medium spatial resolution imagery: a comparison between fuzzy classification and LSMA

Impervious surfaces are important environmental indicators and are related to many environmental issues, such as water quality, stream health and the urban heat island effect. Therefore, detailed impervious surface information is crucial for urban planning and environment management. To extract impervious surfaces from remote sensing imagery, many algorithms and techniques have been developed. However, there are still debates over the strengths and limitations of linear versus nonlinear algorithms in handling mixed pixels in the urban landscapes. In the meantime, although many previous studies have compared various techniques, few comparisons were made between linear and nonlinear techniques. The objective of this study is to compare the performance between nonlinear and linear methods for impervious surface extraction from medium spatial resolution imagery. A linear spectral mixture analysis (LSMA) and a fuzzy classifier were applied to three Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images acquired on 5 April 2004, 16 June 2001 and 3 October 2000, which covered Marion County, Indiana, United States. An aerial photo of Marion County with a spatial resolution of 0.14 m was used for validation of estimation results. Six impervious surface maps were yielded, and an accuracy assessment was performed. The root mean square error (RMSE), the mean average error (MAE), and the coefficient of determination (R ²) were calculated to indicate the accuracy of impervious surface maps. The results show that the fuzzy classification outperformed LSMA in impervious surface estimation in all seasons. For the June image, LSMA yielded a result with an RMSE of 13.2%, while the fuzzy classifier yielded an RMSE of 12.4%. For the April image, LSMA yielded an accuracy of 21.1% and the fuzzy classifier yielded 17.0%. For the October image, LSMA yielded a result with an RMSE of 19.8%, but the fuzzy classifier yielded an RMSE of 17.5%. Moreover, a subset image of the commercial, high-density and low-density residential areas was selected in order to compare the effectiveness of the developed algorithms for estimating impervious surfaces of different land use types. The result shows that the fuzzy classification was more effective than LSMA in both high-density and low-density residential areas. These areas prevailed with mixed pixels in the medium resolution imagery, such as ASTER. The results from the tested commercial area had a very high RMSE value due to the prevalence of shade in the area. It is suggested that the fuzzy classifier based on the nonlinear assumption can handle mixed pixels more effectively than LSMA.     

On determining appropriate aerosol optical depth values for atmospheric correction of satellite imagery for biophysical parameter retrieval: requirements and limitations under Australian conditions

Atmospheric correction of high spatial resolution (10–30 m pixel sizes) satellite imagery for use in large-area land-cover monitoring is difficult due to the lack of aerosol optical depth (AOD) estimates made coincident with image acquisition. We present a methodology to determine the upper and lower bounds of AOD estimates that allow the subsequent calculation of a biophysical variable of interest to a pre-determined precision. Knowledge of that range can be used to identify an appropriate method for estimating AOD. We applied the methodology to Landsat 5 Thematic Mapper data in Queensland (QLD) and New South Wales (NSW), Australia, and determined that AOD must be estimated within approximately 0.05 of actual AOD for retrieval of foliage projective cover (FPC) to a precision of 10%. That knowledge was then used to determine the relative merit of using a fixed constant, Aerosol Robotic Network (AERONET) climatology, or dense dark vegetation (DDV) method for estimating AOD in QLD and NSW. It was found that using a fixed AOD of 0.05 allows estimates of FPC within 10% of their true value when the true value of AOD is less than 0.1. Such AOD values account for approximately 90% of all inland observations and 65% of coastal observations as determined by analysis of data obtained from AERONET. Using an AERONET climatology to estimate AOD was found to increase the likelihood of accurate FPC retrieval in coastal locations to 83%, although it should be noted that AERONET data are very sparse. DDV has potential in eastern and central areas for retrieving AOD observations with greater precision than fixed values or climatologies. However, more work is needed to understand the temporal variation of vegetation reflectance before the DDV method can be used operationally.