Comparison of adaxial and abaxial spectral reflectance of Fagus orientalis Lipsky and Carpinus betulu using field spectroradiometer and spectral indices

The spectral reflectance of tree crown can be different from spectral reflectance of its leaves because of diverse leaf and branch angles as well as internal space of tree crown. For these reasons it is necessary to study spectral reflectance of both adaxial and abaxial surfaces of the leaves. Such information is necessity for modeling the reflectance of tree crown and forest stands. The main objective of this study was to obtain and study the spectral reflectance of both adaxial and abaxial leaves of beech and hornbeam in natural condition and to investigate their spectral differences using indices sensitive to chlorophyll, chlorophyll to carotenoid ratio and photosynthetic pigments. Field spectroradiometric measurements were performed using a portable spectroradiometer (ASD FieldSpec) in Kheyrud forest. A total of 52 trees were sampled and 312 spectra were recorded and analyzed. Spectral measurements cover the wavelength range between 350 – 2500 nm. The results of the spectral reflectance analysis of these two species showed that the abaxial spectral reflectance from 350 to 2500 nm was higher than the adaxial one for hornbeam species. However, for beech species in the visible region and far infrared region, the abaxial spectral reflectance was higher whereas in the near infrared it was lower than the adaxial one. For more detailed investigation of spectral reflectance difference for these two species, spectral indices sensitive to chlorophyll and carotenoid were calculated and statistically analyzed for both surfaces. The value of adaxial NDI index was found to be higher than abaxial for both species. In contrast, the values of adaxial SIPI and PRI indices were lower than abaxial. The differences significant (?= 0.01, p< 0.0001) for both species

Spectral reflectance of rice canopies and red edge position (REP) as indicator of High yield varieties

Rice is the staple food in Iran. More than 80 percent of rice area is distributed in the two northern provinces of Mazandaran and Gilan, so that investment in increasing the quantity and quality can impact an effective role in economic independence and sustainable agriculture. Increased efficiency in rice production is possible through varietal technology, advances in yield enhancement, and the successful development of hybrid technology. Nondestructive methods such as study the spectral reflectance of rice fields is a reliable way in remote sensing study. In this study we tested the possibility to predict highyielding rice varieties based on the spectral reflectance data in the red edge position (REP). Spectral reflectance of rice canopies from 350 to 2500 nm were acquired under clear sky in rice filed. The obtained results indicate that REP of Hybrid, Tarom, Neda and Khazar varieties are at longer wavelength, so they are predicted as more productive rice varieties

Caspian Sea level fluctuation and determination of setback line

In the past 25 years, rising of the Caspian Sea level, part of a natural treat to the sea, has inundated and destroyed many buildings and arable lands and threatened many inhabitations in coastal areas. The main reason for these damages is that the law-setback has lost its efficiency and human activities have proceeded seaward. The goal of this study is to introduce a proper setback line for the southern coast of Caspian Sea on the basis of critical water elevation and the results of coastal vulnerability assessment to sea level rise. This setback contains vertical and horizontal buffers. The Coastal vulnerability index (CVI) method is used for coastal vulnerability assessment and is also used in the Geographic Information System. Five variables in two sub-indices were used in this method. The final map obtained from coastal vulnerability assessment divided the coastal zone into low, moderate, high and very high risk categories based on quartile ranges and visual inspection of data. A mean distance of very high risk category of vulnerability map from a second vertical buffer in each rural district was then proposed as a width of horizontal buffer in the same rural district

Object-Based Classification of UltraCamD Imagery for Identification of Tree Species in the Mixed Planted Forest

This study is a contribution to assess the high resolution digital aerial imagery for semi-automatic analysis of tree species identification. To maximize the benefit of such data, the object-based classification was conducted in a mixed forest plantation. Two subsets of an UltraCam D image were geometrically corrected using aero-triangulation method. Some appropriate transformations were performed and utilized. Segmentation was conducted stepwise at two levels and a hierarchical image object network was constructed. The classification hierarchy was developed and Nearest Neighbor classifier, using integration of different features was performed. Training samples and ground truth map were prepared through fieldwork. Accuracy assessment of the resulting maps in comparison with reference data showed overall accuracies and Kappa Index of Agreement of 90.2%, 0.82 (Area1) and 69.8%, 0.49 (Area2), respectively. Transformed images were advantageous to improve the results. The lower accuracy in Area2 can be attributed to high diversity and heterogeneous mixture of species. More detailed and accurate mapping of tree species would be fulfilled applying precise 3D data. The accuracy of detailed vegetation classification with very high-resolution imagery is highly dependent on the segmentation quality, sample size, sampling quality, classification framework and ground vegetation distribution and mixture

Lorey's height regression for ICESAT-GLAS waveforms in hyrcanian deciduous forests of Iran

IGARSS 2015, Milan, ITA, 26-/07/2015 - 31/07/2015International audienceSince Lidar technology provides the most direct measurements of 3D of phenomena, it plays a critical role in a variety of applications. Forest canopy height as a main factor in forest biomass estimation is costly and time consuming to be measured on the ground. This study aims to estimate Lorey's height “Hlorey” using GLAS data based on regression models. Different metrics like waveform extent “Wext”, trail-edge extent “Htrail” and lead-edge extent “Hlead” were extracted from waveforms and a terrain index “TI” was also calculated using a digital elevation model. Hlorey estimated using multiple regression models were compared to field measurements data. A 5-fold cross validation method was used to validate the results. Best model with lowest AIC (297.440) was resulted using combination of Wext and TI (R_a^2=0.72; RMSE= 5.04m). The results show capability of ICESat-GLAS to estimate Lorey's height in sloped area with a simple regression model. It is prospected to reach better result using other statistical methods and also improvement of processing techniques for LiDAR waveforms in the case of sloped terrai

Capability of GLAS/ICESat data to estimate forest canopy height and volume in mountainous forests of Iran

International audienceThe importance of measuring biophysical properties of forest for ecosystem health monitoring and forest management encourages researchers to find precise, yet low cost methods especially in mountainous and large area. In the present study Geoscience Laser Altimeter System (GLAS) on board ICESat was used to estimate three biophysical characteristics of forests located in north of Iran: 1) maximum canopy height (Hmax), 2) Lorey's height (HLorey), and 3) Forest volume (V). A large number of Multiple Linear Regressions (MLR) and also Random Forest (RF) regressions were developed using different set of variables: waveform metrics, Principal Components (PCs) produced from Principal Component Analysis (PCA) and Wavelet Coefficients (WCs) generated from wavelet transformation. To validate and compare different models, statistical criteria were calculated based on a five-fold cross validation. The best model concerning the maximum canopy height was an MLR with an RMSE of 5.0 m which combined two metrics extracted from waveforms (waveform extent "Wext" and height at 50% of waveform energy "H50"), and one from the Digital Elevation Model (Terrain Index: TI). The mean absolute error (MAPE) of maximum canopy height estimates is about 16.4%. For Lorey's height, a simple MLR model including two metrics (Wext and TI) represents the highest performance (RMSE=5.1 m, MAPE=24.0%). Totally, MLR models showed better performance rather than RF models, and accuracy of height estimations using waveform metrics was greater than those based on PCs or WCs. Concerning forest volume, employing regression models to estimate volume directly from GLAS data led to a better result (RMSE=128.8 m3/ha) rather than volume-HLorey relationship (RMSE=167.8 m3/ha)

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

Evaluation of spectral reflectance of seven Iranian rice varieties canopies

Rice cultivated areas and yield information is indispensable for sustainable management and economic policy making for this strategic food crop. Introduction of high spectral and special resolution satellite data has enabled production of such information in a timely and accurate manner. Knowledge of the spectral reflectance of various land covers is a prerequisite for their identification and study. Evaluation of the spectral reflectance of plants using field spectroradiometry provides the possibility to identify and map different rice varieties especially while using hyperspectral remote sensing. This paper reports the results of the first attempt to evaluate spectral signatures of seven north Iranian rice varieties (Fajr, Hybrid, Khazar, Nemat, Neda, Shiroudi and Tarom plots) in the experimental station of the Iranian Rice Research Institute (main station in Amol, Mazanderan Province). Measurements were carried out using a field spectroradiometer in the range of 350-2,500 nm under natural light and environmental conditions. In order to eliminate erroneous data and also experimental errors in spectral reflectance curves, all curves were individually quality controlled. A set of important vegetation indices sensitive to canopy chlorophyll content, photosynthesis intensity, nitrogen and water content were employed to enhance probable differences in spectral reflectance among various rice varieties. Analysis of variance and Tukey’s paired test were then used to compare rice varieties. Using Datt and PRI1 indices, significant differences (α= 0.01) were found among rice varieties reflectances in 19 out of 21 cases. This promises the possibility of accurate mapping of rice varieties cultivated areas based on hyperspectral remotely sensed data