جستجوی مقالات مرتبط با کلیدواژه « Object-based classification » در نشریات گروه « کشاورزی »

Natural disturbances caused by the canopy gap an important role in forest dynamics. Analyzing the dynamics of the canopy gap provides an accurate understanding of the process of forest stands dynamics as well as natural disturbances that play an important role in the future management of the forest ecosystem. The present study examines the dynamics of the canopy gap in the Arab Dagh forestry in Golestan province.

The identification and preparation of the gaps map was done by the basic object classification method. The classification of the basic object can be done in three general stages, which include segmentation, classification and evaluation of classification accuracy.In the next step, the 1345 canopy gap in 2011 and the 1058 canopy gap in 2019 were extracted from the method that had the best performance compared to other methods of preparing gap maps. The characteristics of the gaps were obtained at each time period. Also, with the intersection of these two maps, the dynamics of the gaps was analyzed by calculating the characteristics Gap formation rate, Gap closure rate, Gap expansion rate, Gap reduction rate, and Gap number increase rate.

The results of the canopy gap dynamics during the 9 years showed that the number and density of the canopy gap decreased. But the average area canopy gap and the share of the area of the canopy gap in the total area increased.In both periods, 65% of the canopy gap have an area of less than 150 m2, and a large canopy gap (larger than 300 m2) covered the lowest frequency and share of the total area of the canopy gap. The highest rate of changes in the canopy gap source was related to the Gap expansion rate (1.09% per year). The Gap closure rate is almost equal to the Gap reduction rate. The lowest rate of changes in the light is the Gap formation rate (0.77% per year). The logarithmic Gap number increase rate (GNIR) was negative (-2.6) that in all canopy gap classes, the number of closed canopy gaps per year is more than It is the number of new canopy gap that is created.

With the passage of 9 years, the density of gaps and the number of gaps have decreased and the total surface of gaps has increased. This increase in the total level of gap can be attributed to the increase in the Gap expansion during the period. Because the Gap expansion rate is higher than the Gap formation rate, the Gap closure rate and Gap reduction rate.In both periods of time, the most abundant size was related to small holes. which indicates the predominance of small lights in the studied area. which shows the predominance of small gaps in the studied area, small gaps make up most of the dynamics of gaps and close faster than big gaps.Keywords: Gap, UAV, dynamic indicators, coniferous forestry, remote sensingps.

Understanding the suitability of the lands is very important in terms of the ability to cultivation a particular crop. Having information in this field helps us to act more intelligently in prioritizing land allocation for the cultivation of various crops. Also, adapting the current-grown lands under cultivation of a crop selected by the farmer to the final layer of land suitability can give us an overview of the right or wrong choice of land use. This information will help agricultural policymakers to replace crops when necessary and to replace crops that have been misallocated in disproportionately desirable lands with other crops or to improve their crop management. Therefore, this study was conducted to assess the land suitability of Golestan province agricultural lands for soybean cultivation and the degree of adaptation of current soybean-grown fields to the obtained suitability layers.

This study was carried out in the agricultural lands of Golestan province with an area of 821 thousand hectares. First, the real lands under cultivation of soybean were separated using 1674 land samples taken from different crops and object-based image analysis (OBIA) method. To separate the lands under soybean cultivation in Golestan province, sentinel 2 satellite images with a spatial accuracy of 10 meters related to planting to harvesting time in 2018 were used. Then, the layers of soil, climate, and topography characteristics were provided to investigate land suitability for soybean cultivation. Climatic components including minimum, optimum, maximum temperatures, and rainfall were estimated using long-term statistics of synoptic stations in the province (maximum available statistics). Data of soil texture, nitrogen, organic matter, phosphorus and potassium, soil pH, and salinity were also received from the provincial agricultural and natural resources research center, and from the data, the soil properties map was obtained. The digital land elevation map (DEM) of the province with a spatial resolution of 20 meters was used to extract slope, elevation, and aspect maps. The process of interpolation of climatic and soil layers was performed using ordinary kriging method. The relative importance of each factor was determined through the Analytic Hierarchy Process (AHP). This was done by designing questionnaires based on AHP paired matrices and completing it by agricultural specialists. After extracting the weights from the questionnaires and preparing the classified raster layers, these layers were imported in GIS version 10.3. Combining and overlaying the layers was done by assigning AHP weight to each layer. Finally, a land suitability map was prepared for the cultivation of the soybean in the study area which, in turn, was used to determine the adaptation of current soybean fields with determined suitability classes.

The accuracy of classification by object-oriented method using kappa coefficient and overall Accuracy coefficient (0.87 and 90%, respectively) shows the acceptable accuracy of soybean land separation in this study. In the study of land suitability for soybean cultivation, the results obtained from hierarchical analysis showed that the soil criterion had the greatest effect on the site selection of soybean cultivation with a coefficient of 0.52 with respect to both climate and topography factors. The results showed that most of the fields (about 87% of total) placed in suitable class and 13% placed in a relatively suitable class. In suitable areas for cultivation, despite having the best conditions for factors such as maximum temperature, average temperature, slope, aspect, height, soil texture, soil pH, phosphorus and soil salinity, soybean production is limited by factors such as precipitation (400 to 500 mm per year), minimum temperature (10 to 12 °C), phosphorus (8 to 10, 15 to 20 mg/kg soil). In these areas, maximum yield can be achieved by managing the mentioned factors and applying desirable agricultural management. In relatively suitable areas, limitations of nitrogen deficiency (less than 0.5 mg/kg soil), organic matter (less than 2%), salinity (above 6 dS/m), slope (more than 5%), restriction of soybean cultivation due to heavy soil texture (high percentage of soil clay), potassium (less than 100 mg/kg soil), phosphorus (more than 20 or less than 8 mg/kg soil), precipitation (less than 400 mm per year), minimum temperature (less than 10 °C), slope (more than 8%) and aspect (west and north) caused relatively high land restrictions for soybean cultivation. Compatibility analysis of the current soybean fields with the suitability maps indicated that about 99% of total cultivated lands are located in a suitable class, which demonstrates the proper selection of farm locations by the farmers.

By considering the position of Golestan province in the production and area under soybean cultivation in the country, if it is possible to identify suitable soybean cultivation areas according to the environmental requirements of this product and identify the limitations created by the environment, more yield per area can be achieved, which will improve the agricultural economy and the level of income of the country.

Determination of the spatial pattern of trees is one of the important structural parameters of the forest for identifying changes in forest communities and it's monitoring in recent years. On the one hand, extracting this structural parameter using satellite data has a significant impact on reducing cost of inventory and makes it easy to evaluate forests of forest managers. In this study, the spatial pattern of trees in the natural forest of Farim of Mazandaran province was determined by using SPOT7 satellite data. Thus, some tree`s characteristics such as species, diameter at breast height, the diameter of canopies, gaps and the position of each tree were measured in 12 plot with area about one ha (100 m x 100 m). On the other hand, after radiometric and geometric corrections of satellite data, the tree`s canopy and gaps were classified, using the object-based method, and tree position per plot also was determined by canopy gravity center. Then, the spatial distribution pattern of the trees and structure, distinguished and extracted by field inventory and satellite data was compared by O-ring function. The results showed that the overall accuracy of canopy and gaps extracted from satellite data by object-based classification was about 91% and kappa coefficient equaled 0.7. The spatial pattern of trees were random except of the small radius and similar to the pattern of trees in-ground inventory. In total, results showed that SPOT-7 data and the object-based classification have an acceptable potential for determining the spatial pattern of forest stands.

Land use is one of the important features of watersheds, so being aware of its situation and changes over time are needed for achieving sustainable development. Today, novel techniques based on remote sensing technology provides good opportunity for study on this topic. In this study, the current and past status of land use of one of the most critical areas of country in regards of water and environmental issues, Lake Urmia watershed, was targeted. To this aim, Landsat -TM images from the years 1987 and 2007 were used and for improving the accuracy of classification, two sets of images from different months were used for each year. First, land use map of the Urmia County in 2007 was prepared by using of pixel-based (maximum likelihood) and object-based (fuzzy) methods, which Object-based approach showed significant more accuracy in classification of land use. Then object-based method was used to classify land use of whole basin for the two selected time periods.Theresultsindicate32and23percent growth in irrigated and rain fed cultivated area, respectively, as well as 8 percent reduction of rangelands in these two time periods. Also, surveying of land use change at involved provinces showed that the patterns of agricultural development were not identical among them.

One of the most important issues to consider in conflicting trans-boundary river basins like Helmand is the potential for agricultural development. This paper evaluates the land use changes between in the Helmand basin between 1990 and 2013 using remote sensing images and an object-based method. The results show the irrigated land area has changed from 10,000 to 18,000 km2 during this period while rainfed areas decreased by 70%. Using the GEOMOD method and Markov chain evaluations, it is projected that agricultural areas can expand up to 25,465 km2, that means an additional 7465 km2 of agricultural land development. Considering water availability limitation, the maximum agricultural land development cannot exceed 4366 km2. The spatial evaluation of this progress revealed that development is mainly expected to take place in the Arghandab and Middle Helmand sub-basins that can seriously affect the river inflows to Iran and threaten the security and ecosystem in the region. However, considering the new policy of Iran on cross-border farming this can be turned to an opportunity for a win-win management of the Helmed trans-boundary river basin.

Land use/cover mapping is one of the most common applications of remote sensing data. Remote sensing data by providing updated digital information, repetitive coverage, reduce costs and the possibility of processing and high potential for the preparation of land use/cover maps in natural resources, is of paramount importance. In this study, the land use and cover map prepared using Google Earth and the Operational Land Imager image sensor (OLI) of Landsat 8 satellite and methods of visual interpretation (GE images), supervised classification, neural networks and object-based classification methods (Landsat 8 images), and compared with each other. In order to evaluate the accuracy of the classification, the overall accuracy, Kappa coefficient, producer’s accuracy and user’s accuracy were used. The results showed that the visual interpretation method with overall accuracy and Kappa coefficient of 99.4 and 0.99, in comparison to the object-based, supervised and artificial neural networks (with an overall accuracy of 94, 82 and 60.8, and a Kappa coefficient of 0.92, 0.77 and 0.50) are more reliable. According to the map of visual interpretation, the rangelands with an area of 946687 ha and water bodies in the area of 217.42 ha were the largest and smallest land use/covers, respectively. In terms of accuracy, the visual interpretation method using Google Earth images had the highest accuracy, but it is time-consuming and cost-effective. In contrast, object-based method with acceptable accuracy and with low cost and time is the best method for land use/cover mapping.

Gravel, cobbles and boulders as erodible parameters play significant role to control wind erosion. Therefore, our understandings of gravel, cobbles and boulders percentage variations help to analyze events in the landscape. Close-range photogrammetry as an accurate measurement tool based on photos analysis has been extraordinary improved in recent years and its usage is rapidly growing in environmental analyses. It seems that close range photogrammetry in mapping and measuring the shapes and surfaces have a great potential. Currently close range photogrammetry is mostly used for preparation of Digital Elevation Model (DEM) and Digital Terrain Model (DTM). Now, high resolution DEMS only can be created using 3D Laser Scanner and close range photogrammetry. Despite having a considerable potential, close range photogrammetry has been rarely used in quantitative natural resource studies. In the current assessment, we examined the ability of close range photogrammetry for a quantitative parameter (i.e. percentage of gravel, cobbles and boulder).
In this study, we tried to used the close range photogrammetry and assess its performance to estimate the percentage of gravel, cobbles and boulders. For this purpose, a specific quadrat was designed for close range photogrammetry and the required photography tools and techniques were determined. In order to prepare the mapping of gravel, cobbles and boulders percentage, a sampling plan using OLI data was designed for the plain of Tehran-Karaj and photography was performed accordingly. Photos were processed using the PhotoScan software and Orthophotos and Digital Terrain Models were then created. The photos were classified by two 1- Decision Tree Analysis using Digital Terrain Models that it was done using the ERDAS IMAGINE 2015 software; 2- Object-based Classification using Orthophotos and Digital Terrain Models that the eCognition Developer 9 software was used. Gravel, cobbles and boulders percentage of each quadrat was estimated based on more accurate method and used as the dependent variable for modeling process. To model gravel, cobbles and boulders percentage, OLI data was firstly preprocessed to extract reflectance of the bands and then spectral indices were used. Geometric correction and radiometric correction using ATCOR3 were carried out in preprocessing phase and spectral indices of soil characterize were used to enhance the image. Finally, the reflectance of the bands and the spectral indices were used to create a multiple regression model using IBM SPSS Statistics 22 software.
The results showed that the Close Range Photogrammetry software (PhotoScan) is able to fix the distortion in photos well. One-dimensional relief displacement error was removed by PhotoScan. Interior and exterior orientation was done very well using the software and measurements which were calibrated by it. High quality Ortho-Photos and high resolution Digital Terrain Models were created using PhotoScan.
Classification by Decision Tree Analysis using Digital Terrain Models was done by the ERDAS IMAGINE 2015 software. First-order and Second-order polynomial interpolation was applied to Digital Terrain Models and the uniform surfaces were created. Two surfaces (original one created by PhotoScan and Interpolated Surface) were then compared and the gravel, cobbles and boulders parts were separated using some thresholds. The results indicated that this method can create the gravel, cobbles and boulders map rapidly but the accuracy is moderate.
Comparing with Decision Tree Analysis, Object-based Classification by the eCognition Developer 9 software which uses Orthophotos and Digital Terrain Models was more accurate. However, the latter was time-consuming as it is needed to be done manually in many different steps and there were many options to be created for final layer.
Automatic linear modeling in IBM SPSS Statistics 22 software was used to create multiple regression model and Iron Oxide and Inferred indices and reflectance of the bands 1, 2, 3 and 7 of OLI Sensor were selected by the software. The coefficient of determination of the model was more than 0.9 showing the good potential of the close-range photogrammetry. This model was used to create maps of percentage and the final map was in full compliance with the field observations.
Our results showed that the Close Range Photogrammetry has a vast potential and it can be an important tool in the environmental studies in the future.

Oak is the dominant forest species in the Zagros mountains, it is seen as a semi dense forest in western provinces. Multiple droughts, development of cities and villages, land use change from forest to farmland, are the main causes of forest destruction in Ilam. The purpose of this research is to determine the change detection of forest land to other land uses, using multi temporal TM sensor images (April 1986) and OLI (April 2015). In object-based classification, the data segmentation as initial phenomena, with appropriate scale and compression parameters for each image, was performed. Two classifying phenomena, nearest neighbor method and fuzzy functions were used. Land use classes were prepared by specifying training points and using the normalized difference vegetation index (NDVI). The Overall accuracy of the TM image was 88% and OLI image 94%, and the kappa coefficient for the TM and OLI image was calculated 84% and 91%, respectively. By post classification comparison method, the change matrix for each districts and changes forest to other uses was formed. The results showed that all districts faced with the phenomenon of deforestation and forest wide change (-42%) have been made.