Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results.  Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies

First Demonstration of Space-Borne Polarization Coherence Tomography for Characterizing Hyrcanian Forest Structural Diversity

Structural diversity is recognized as a complementary aspect of biological diversity and plays a fundamental role in forest management, conservation, and restoration. Hence, the assessment of structural diversity has become a major effort in the primary international processes, dealing with biodiversity and sustainable forest management. Because of prohibitive costs associated with the ground measurements of forest structure, despite their high accuracy, space-borne polarization coherence tomography (PCT) can introduce an alternative approach given its ability to provide a vertical reflectivity profile and spatiotemporal resolutions related to detecting forest structural changes. In this study, for the first time ever, the potential of space-borne PCT was evaluated in a broad-leaved Hyrcanian forest of Iran over 308 circular sample plots with an area of 0.1 ha. Two aspects of horizontal structure diversity, including standard deviation of diameter at breast height (σdbh) and the number of trees (N), were predicted as important characteristics in wood production and biomass estimation. In addition, the performance of prediction algorithms, including multiple linear regression (MLR), k-nearest neighbors (k-NN), random forest (RF), and support vector regression (SVR) were compared. We addressed the issue of temporal decorrelation in space-borne PCT utilizing the single-pass TanDEM-X interferometer. The data were acquired in standard DEM mode with single polarization of HH. Consequently, airborne laser scanning (ALS) was used to estimate initial values of height hv and ground phase φ0. The Fourier–Legendre series was used to approximate the relative reflectivity profile of each pixel. To link the relative reflectivity profile averaged within each plot with corresponding ground measurements of σdbh and N, thirteen geometrical and physical parameters were defined (P1−P13). Leave-one-out cross validation (LOOCV) showed a better performance of k-NN than the other algorithms in predicting σdbh and N. It resulted in a relative root mean square error (rRMSE) of 32.80%, mean absolute error (MAE) of 4.69 cm, and R2* of 0.25 for σdbh, whereas only 22% of the variation in N was explained using the PCT algorithm with an rRMSE of 41.56%. This study revealed promising results utilizing TanDEM-X data even though the accuracy is still limited. Hence, an entire assessment of the used framework in characterizing the reflectivity profile and the possible effect of the scale is necessary for future studies

Analysis and Evaluation of Effective Factors in Pedestrian Streets Promotion with Environmental Design Approach in the Framework of Greenways Planning the Cse study: Mashhad metropolis

Pedestrian streets is a tool related to social health, urban lifestyle, urban economy, environmental quality. In recent decades, these urban spaces have been highly regarded by urban planning experts for various reasons, including the creation of human scale, increasing dynamism and social interactions, environmental issues, personal and social health, creating freshness and attracting tourism. In the Mashhad metropolis, due to issues such as tourism, the increase number of cars 4.5 times in the last decade, the increase amount of environmental pollutants caused by transportation, the increase number of sedentary and overweight citizens and in general the unsuitability of livability of this metropolis the need to support clean and human-centered transportation has felt. Creating a desirable and environmentally friendly pedestrian axis can play an important role in improving pedestrian streets. What can play an effective role in planning these spaces and to cover the mentioned goals of urban planners, is to propose a solution that can be adapted to the material and spiritual needs of citizens. For this purpose, using the questionnaire and methods of analysis AHP and SAW, we have examined the sights and expectations of the urban community of an optimal pedestrian axis that effective in a new urban lifestyle. The ratio of increased daily activity of citizens after the addition of greenways to the urban transportation system is more than 3 times. In addition the results show that greenways can be a platform for change in urban lifestyle and the tendency to walk and daily activities and increase the economic value of the land by 23%. In addition, the use of vegetation, the allocation of a special bicycle lane and the consideration of crowded places in the planning of pedestrian spaces can play an important role in increasing the desire of citizens to be present in these spaces on foot

Watershed road network analysis with an emphasis on fire fighting management

The aim of this study is fire hazard zoning the Chehel-Chay watershed and analysis of road network in order to fire-fighting management. Using effective factors on fire occurrence, the fire hazard map of the study area produced by support vector machine algorithm and then was divided into four hazard classes. The road length and type were investigated in the each fire hazard classes. The results showed that most of occurred fires are located in the close distances of roads and forest areas. The results showed that road types and land cover are important in fire occurrences and suppression. In high dangerous zone, the roads pass through forestlands, but in low dangerous zone, the roads are passing from farmlands. The roads do not cover the half of area and do not pass at two third of high hazard class zones. Therefore, appreciate road network planning is necessary according to fire-fighting management. 

Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests

Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results.  Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests

Modeling tree species diversity by combining ALS data and digital aerial photogrammetry

Monitoring and assessment of tree species diversity in forests are essential tasks for forest managers. In the present study, we investigated the effectiveness of airborne laser scanner (ALS) and digital aerial photogrammetry (DAP) data for modeling tree diversity indices using machine learning algorithms in the uneven-aged Hyrcanian forests of Iran. Systematic sampling was adopted for the collection of field data on 300 circular plots (0.1 ​ha) in the study area. Menhenick, Margalef, Simpson’s heterogeneity, reciprocal of Simpson’s heterogeneity, Shanon-Winer heterogeneity and Simpson’s evenness indices were computed as measures of tree species diversity for each plot. Several commonly used variables were extracted from the ALS and DAP data. The results showed that the random forests (RF) algorithm produced the greatest accuracy among all machine learning algorithms. Additionally, combining ALS and DAP (ALS ​+ ​DAP) data increased prediction accuracy compared to separate modeling (0.2–6.6% reduction in relative root mean square error). The smallest RMSE% values based on independent validation data for Menhenick, Margalef, Simpson’s, reciprocal of Simpson’s, Shanon-Winer heterogeneity and Simpson’s evenness indices were 34.6%, 32.2%, 27.6%, 27.4%, 30.7% and 24.4%, respectively. These results demonstrate that the combination of ALS and DAP could be useful for monitoring tree species diversity in multi-story stands in the north of Iran and likely also in forests with complex canopy structures and multiple tree species

Forest type mapping using incorporation of spatial models and ETM+ data

Results of former researches have shown that spectrally based analysis alone could not satisfy forest type classification in mountainous mixed forests. Forest type based on composed different parameters such as topography elements like aspect, elevation and slop. These elements that are affected on occurrences of forest type can be stated as spatial distribution models. Using ancillary data integrated with spectral data could help to separate forest type. In order to find the abilities of using topographic spatial predictive models to improve forest type classification, an investigation was carried out to classify forest type using ETM+ data in a part of northern forests of Iran. The Tasseled Cap, Ratioing transformations and Principal Component Analysis were applied to the spectral bands. The best spectral and predictive data sets for classifying forest type using maximum likelihood classification were chosen using the Bhattacharya seperability index. Primary analysis between forest type and topographic parameters showed that elevation and aspect are most correlated with the occurrences of type. Probability occurrence rates of forest type were extracted in the aspect; elevation, integrated aspect and elevation as well as homogeneous units structured on elevation and aspect classes. Based on occurrence rates of forest type, spatial predictive distribution models were generated for each type individually. Classification of the best spectral data sets was accomplished by maximum likelihood classifier and using these spatial predictive models. Results were assessed using a sample ground truth of forest type. This study showed that spatial predictive models could considerably improve the results compared with spectral data alone from 49 to 60{\%}. Among spatial models used, the spatial predictive models constructed based on the homogeneous units could improve results in comparison to other models. Applying other parameters related to forest type like soil maps would generate accurate spatial predictive models and may improve the results

Investigation on development potential of endangered species of Taxus baccata at Golestan Province, based on GIS technology (Case study: Pooneh Aram reserve)

Taxus baccata is a native endangered coniferous species of Iran. Although the species had a wide range distribution in the past, but now has limited habitats. Therefore, further studies of the species spatial distribution and its possible development and extant by plantation projects, is necessary due to vital support of this medicinal species. The aim of the study was to compare the Yew’s ecological requirements with the ecological characteristics of the studied area at Golestan province in order to identify the most appropriate sites for forest plantation. For this reason, the multi-criteria evaluation (MCE) method, based on analysis of hierarchical process (AHP) was used. At first, the 10 required natural criteria which affect T. baccata’s growth, including altitude, slope gradient, slope aspect, geology, relatively air moisture, precipitation, temperature, soil type, plant cover and canopy cover density were considered and at the end after identifying their weight, final map of the area suitable for the Yew’s plantation was developed, based on the Multi Criteria Evaluation model. The results showed that from the studied total area of 30554 hectares, the classified lands for the Yew’s plantation were as follows: 2482 ha. Excellent, 10982 ha. fine, 10909 ha. moderate and 6181 ha poor. Overall, it’s easy to specify the areas suitable for Yew’s plantation as well as to develop a registered program and plan for its plantation at the Caspian forests of Iran