MLS-assisted validation of WorldView-2 panchromatic image for estimating Pinus sylvestris crown height

High spatial resolution satellite imaging has the advantages of both fine scale and large coverage that indicate the potential for measuring forest morphologies. However, because of the aerial view, imaging has limited capacity of explicitly deriving the under-crown structural parameters. A possible solution is to explore the relationships between this kind of variables such as crown height (CH) and the feature parameters readily derived from the satellite images. However, field sampling of the training data is not a trivial task. To handle this issue, this study attempted the state-of-the-art remote sensing technology of vehicle-based mobile laser scanning (MLS) for collecting the sample data. Evaluation for the case of the Scots pine (Pinus sylvestris) trees has preliminarily validated the plan. That is, MLS mapping enabled the parameter of CH to be estimated from WorldView-2 panchromatic images

Assessment of the Contribution of WorldView-2 Strategically Positioned Bands in Bracken fern (Pteridium aquilinum (L.) Kuhn) Mapping

In the eThekwini Metropolitan Area, mitigation of the Bracken fern (Pteridium aquilinum (L.) Kuhn) invasion within the KwaZulu-Natal Sandstone Sourveld (KZNSS) has been identified as a major environmental priority. To facilitate informed interventions, reliable Bracken fern spatial distribution is necessary. Earlier efforts to map the fern using lower spatial and spectral resolution imagery have been unsuccessful. Consequently, this study sought to determine the reliability of the “new generation” World View-2 (WV-2) image characterised by higher spatial and spectral resolution in delineating the fern invaded areas. The eight band WV2 image was atmospherically corrected and spectrally resized as the SPOT-5 wavebands, additional bands and all bands. The classification accuracy was compared to results from the SPOT-5 image. Results showed that classification based on WV-2s additional bands had superior classification accuracy than the rest of the categories. Furthermore, classification based on all the WV-2s bands and the traditional bands perfomed better than the SPOT-5 image in delineating areas covered by the fern. These findings indicate the value of of the “new generation” imagery characterised by higher spatial and spectral resolution in improving the accuracy of the fern invaded landscapes

Estimation of some stand parameters using digital aerial photographs for conservation and service oriented forests

Forest inventory, which is the first step of forest management planning, is the most difficult stage that requires much time and a lot of efforts. To reduce fieldworks that are considered time consuming and expensive methods of ground measurements, remote sensing data are widely used. Aerial photographs have been an integral part of forest inventory data in Turkey since 1963. Panchromatic and RGBI (Red, Green, Blue, Infrared) aerial photographs acquired by digital aerial cameras proved to be very important in forest inventory. They have maintained their importance for forest management planning process. The aim of this study is to construct a fast and practical inventory model that requires least fieldwork for forest management planning process. Pixel values and vegetation indices (NDVI, DVI, IPVI, RVI and PCA), obtained from remote sensing data, and stand parameters (stand volume, volume increment and number of trees) have been compared statistically. Black pine Pinus nigra J. F. Arnold plantations located in the south-east region of Turkey, Çelikhan Forest Planning Unit, was chosen as a research area. 0.5 meter spacing and 8 bit radiometric resolution Ultracam-X Digital Aerial Photos were used as remote sensing data. According to statistical analysis, IPVI and Green Band values provided the highest evaluation coefficient compared to the models developed for the estimation of stand parameters. Adjusted R square of stand volume, volume increment and the number of tree in the models were found to yield 0.74, 0.73 and 0.50 respectively. It was concluded that stand characteristics estimated by statistical models can be used for forest areas managed for conservation and service purposes

Absolute Density Measures Estimation Functions with Very High Resolution Satellite Images

Assessment and monitoring of forest structure is frequently done with absolute density measures with field forest inventory data and expansion methods. The development of basal area and the number of trees estimation functions with data derived from very high spatial resolution satellite images enable their short-term and cost-effective evaluation, allowing also the estimation for the area not requiring extrapolation methods. The functions of basal area and the number of trees per hectare are based on crown cover obtained with very high spatial resolution satellite images for two evergreen oaks and umbrella pine. The three tree species are especially important in the agroforestry systems of the Mediterranean region. The linear functions fitted for pure stands of the three species and mixed stands of cork and holm oak and of cork oak and umbrella pine showed a better performance for basal area than for the number of trees per hectare. The inclusion of dummy variables for species composition improved the accuracy of the functions

Exploring the utility of the additional WorldView-2 bands and support vector machines in mapping land use/land cover in a fragmented ecosystem, South Africa

Land use/land cover (LULC) classification is a key research field in environmental applications of remote  sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has  provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally  resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located  bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns.Keywords: land use/cover classification, fragmented ecosystem, WorldView-2, support vector  machines

Classifying silvicultural systems (coppices vs. high forests) in Mediterranean oak forests by Airborne Laser Scanning data

Forest classification by silvicultural systems (coppices vs. high forests) is important for forest resource assessment as such systems relate to a wide variety of ecosystem services. In this paper the potential of Airborne Laser Scanning (ALS) data for Mediterranean oak forests classification of coppices with standards vs. high forests was investigated in three study areas in Italy. We addressed the following issues: can coppices and high forests be distinguished using a raster Canopy Height Model (CHM)? Which are the most efficient CHM-derived metrics? Does the scale of analysis influence the classification potential of CHM metrics? Our results show that CHM in grid format (1-m2 pixel) provides support information to classify silvicultural systems

Application of handheld laser scanning technology for forest inventory purposes in the NE Turkey

Forest inventory (FI) is the most challenging stage of forest management and planning process. Therefore, in situ surveys are often reinforced by modern remote sensing (RS) methods for collecting forestry-related data more efficiently. This study tests a state-of-the-art data collection method for practical use in the Turkish FI system for the first time. To this end, forest sampling plots were conventionally measured to collect dendrometric data from 437 trees in Artvin and Saçınka Forest Enterprises. Then, each plot was scanned using a handheld mobile laser scanning (HMLS) instrument. Finally, HMLS data were compared against ground measurements via basic FI measures. Based on statistical tests, no apparent differences were found between the two datasets at the plot level (P 0.97; P < 0.01). Residual analysis showed that both positive and negative errors had a homogeneous distribution, except for plot 8 where tree stems were in irregular shapes due to anthropogenic pressures. When all plots’ data were aggregated, average values for the number of trees, basal area, and timber volume were estimated as 535 trees/ha–1, 49.6 m2/ha–1, and 499.7 m3/ha–1, respectively. Furthermore, secondary measures such as the number of saplings and slope were successfully retrieved using HMLS method. The highest overestimation was in timber volume with less than 10% difference at the landscape level. The differences were attributed to poor data quality of conventional measurements, as well as marginal site conditions in some plots. We concluded that the HMLS method met the accuracy standards for most FI measures, except for stand height. Thus, the Turkish FI system could benefit from this novel technology, which in turn supports the implementation of sound forest management and planning

Evaluation of hierarchical segmentation for natural vegetation: a case study of the Tehachapi Mountains, California

abstract: Two critical limitations for hyperspatial imagery are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are the solution, more data sources and large amounts of testing at high costs are required. In this study, I used tree density segmentation as the key element of a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects. A two-step procedure, which involved environmental stratifications and the random walker algorithm, was used for tree density segmentation. I determined whether variation in tone and texture could be reduced within environmental strata, and whether tree density segmentations could be labeled by species associations. At the final level, two tree density segmentations were partitioned into smaller subsets using eCognition in order to label individual species or tree stands in two test areas of two tree densities, and the Z values of Moran's I were used to evaluate whether imagery objects have different mean values from near segmentations as a measure of segmentation accuracy. The two-step procedure was able to delineating tree density segments and label species types robustly, compared to previous hierarchical frameworks. However, eCognition was not able to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework is applicable for fine-scale, time-series vegetation mapping to develop baseline data for evaluating climate change impacts on vegetation at low cost using widely available data and a personal laptop.Dissertation/ThesisM.A. Geography 201

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied