There are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches (HPs). Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors. We proposed two structurally different methods to identify HPs: predefined thresholding-based one (static one), and statistical thresholding-based technique (dynamic one). In the first method, 3 different thresholds were used: 5%, 10%, and 20%. Next, it was aimed to assess how delineated HPs were spatially matched to satellite data with coarse spatial resolution. Selected cell sizes were 25, 50, 100, 250, and 500 m. The number of particular grid cells which almost entirely fell into registered HPs was counted (leaving 2% cell area tolerance level). This procedure was executed separately for each variant and selected structural variables, as well as for their intersection parts. The results of this investigation revealed that ALS data might have the potential in the identification of HPs of forest stands. We showed that different ALS based variables and thresholds of HPs definition influenced areas which can be treated as similar and homogeneous. We proved that integration of more than one structural variable limits size of the HPs, in contrast, visual interpretation revealed that inside such patches vegetation structure is more constant. We concluded that ALS data can be used as a potential source of data to "enlarge" small ground sample plots and to be used for evaluation and calibration of remotely sensed datasets provided by global systems with coarse spatial resolutions.
Support vector machine (SVM) is a superior machine learning methodology with great results in classification of remotely sensed data sets. Determination of optimal parameters applied in SVM classification technique is still vague to some scientists. This study was aimed to detect tree crowns on UltraCam-D (UCD), very high spatial resolution aerial imagery in Zagros woodlands by SVM optimised by Taguchi method. A 500 × 600 m2 plot covered with Persian oak (Quercus brantii var. persica) coppice trees was selected in Zagros woodlands, Iran. The UCD aerial imagery of the plot (0.06-m spatial resolution) was obtained to extract crowns of the trees in this study. The SVM classification technique parameters were optimised by Taguchi method, and thereafter, the imagery was classified with optimal parameters. The results showed that Taguchi method is a robust approach to optimise the combination of parameters of SVM classification technique. It was also concluded that the technique could detect the tree crowns with a KHAT coefficient of 0.961, which showed a great agreement with the observed samples, and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimise the parameters of classifiers like SVM classification technique.
An approach was developed to construct a percent canopy cover (PCC) map of Zagros semi-arid woodlands, West Iran, using UltraCam-D airborne imagery. We detected crowns of Persian oak coppice trees on the imagery by use of the support vector machine (SVM) classifier optimized via Taguchi method. Then, PCC was calculated in raster grids with various block sizes and their accuracy metrics revealed the appropriate sizes. Results showed the optimized SVM success in separating Persian oak crowns as revealed in receiver operating characteristic (ROC) curve analysis (area under curve: AUC ∼ 0.82). After filtering the raster maps and reassessing their accuracies, validation outputs of the final PCC map with 3000 m2 resolution yielded an overall accuracy of 90% (KHAT=0.71) and was introduced as the optimal map in this study.
Tree height is one of the key parameters in forest plantations that plays a crucial role in estimation of above-ground biomass (AGB) of trees and stands. The parameter may be obtained by different methods from airborne remotely sensed datasets such shadow length of each tree individual or crown height models (CHMs). However, tree height estimation based on shadow length might be biased considering diverse topography of forest sites. Therefore, this study aims to develop a reliable method to estimate tree height in a plantation forest using shadow length on UAV imagery. First, heights of 151 pine (Pinus eldarica) trees were precisely measured in Pardisan Park, North Khorasan province, Iran. Additionally, a collection of images was captured by a Phantom 4Pro UAV in order to illustrate the study area. Then, two different approaches were considered to estimate the height of trees. In the first approach, tree heights were estimated based on shadow length on the UAV orthomosaic and correcting the effect of slope. The second approach considered the UAV-based CHM and height estimation using CHM segmentation and local maximum filtering. The results showed that tree heights estimated by the first approach were not significantly different from the insitu data (p=0.298). Furthermore, the heights estimated by the slope corrected shadow length showed higher precision compared to the heights estimated by the shadow length without slope correction (Relative root mean squared error, RRMSE 5.6% and 8.2% respectively). However, the heights obtained from the first approach were less precise than the second approach (RRMSE 5.6% and 4.2% respectively). In general, it was concluded that height estimation of pine trees based on shadow length after correction of slope effects can be considered as a reliable approach, although CHM is more efficient in estimating the tree heights. The findings of this study are applicable for height estimation of pine trees within plantation forests on UAV imagery.
Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran’s mangroves for three decades, using the Landsat imagery available for the years 1990, 2000, 2010, and 2020. Four general vegetation indices and eight mangrove-specific indices were employed for mangrove mapping in three study sites. Additionally, six important landscape metrics were implemented to quantify the spatiotemporal alteration of the mangrove forests during the study period. Our results showed the robustness of the submerged mangrove recognition index (SMRI), validated as the most effective index (F1-score ≥ 0.89), which was used for mangrove identification within all nine sites. The mangrove area of southern Iran was estimated at approximately 13,000 ha in 2020, with an overall increase of 2313 ha over the whole period. A similar trend could be observed for both the landscape connectivity and complexity. Our results revealed that a stronger connectivity and higher complexity could be detected in most sites, while there was increased fragmentation and a weaker connection in some locations. This study provides an accurate map of Iran’s mangrove forests over time and space.
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