جستجوی مقالات مرتبط با کلیدواژه « Satellite images » در نشریات گروه « جنگلداری »

The occurrence of fires is one of the important factors that determine the different characteristics of many terrestrial ecosystems. For a long time, fires have severely affected forest areas, and sometimes their negative effects remain for several years after the occurrence of the fire, so that the state of vegetation does not return to its previous state. The aim of this study is to model the restoration of vegetation in Zagros forests (Ilam province) following fire.

We used various climatic and environmental data as independent variables (vegetation at the time of fire (NDVI+1), burn severity index, temperature and precipitation anomaly, average temperature, annual precipitation, slope, aspect, and elevation) and NDVI +5 as dependent variable for the modeling (using random forest, decision tree and gradient boosting) the vegetation recovery following fire. Landsat satellite images were used to prepare indices indicating vegetation density status and burn severity, and after preprocessing the images, these indices were prepared by spectral ratio. Climatic variables (precipitation, average temperature, minimum temperature and maximum temperature) were also estimated according to the regression relationships between these variables and the elevation in the study area. Finally, three machine learning algorithms, including decision tree, random forest, and gradient boosting, were used for modeling, and also the accuracy of these models were evaluated.

The results showed that among the various variables investigated, the annual precipitation, average annual temperature, normalized vegetation difference index (NDVI) and burn intensity index at the time of fire were the most important factors affecting the vegetation restoration post fire in these forests. The precipitation and temperature were the most important factors affecting the restoration among the mentioned factors. Also, the results showed that among the different models, the gradient boosting algorithm with R2 = 0.66 models vegetation restoration better than other models. In this model, the climatic factors were the most important in the vegetation recovery.

According the relationships between the NDVI and other studied factors and the results of the modeling; it is possible to explain the effective role of climate factors in the vegetation restoration in the study area.

Fire severity mapping is very important for managing the fires in forest ecosystems. The extraction of spectral indices from optical sensors is recognized as one of the most effective bands for the classification of vegetation classes. In this study, the ability and sensitivity of some spectral indices extracted from Sentinel-2 and Landsat 8-OLI images with different spatial resolutions have been investigated for fire severity mapping using the Random Forest algorithm in a burned area located in the reforested area of Arabdagh, Golestan province. After necessary preprocessing on the bands, the appropriate mono and bi-temporal spectral vegetation indices were created. The optimal index values for bands in the bi-spectral spaces pre/post-fire were calculated to evaluate the sensitivity of bands to the changes occurring within the fire classes. The best results were obtained for the NIR-SWIR2 bands with an optimal index value of 0.77 for Sentinel-2 and 0.67 for Landsat8-OLI. The best indices were selected based on values of optimality index. The values of these indices were calculated after the fire as well as the differential (pre/post-fire) ones. The ground truth of fire severity classes map was prepared by a selective sampling method through field surveying. The classification was done with different indices by random forest (RF) algorithm and the results were assessed by the ground truth points. The result showed that the best results were obtained for a combination of many differential indices from all bi-bands of Landsat 8-OLI with kappa coefficient (0.96).

In this research, the status of forests in Ilam city with the aim of preparing the spatial distribution map of oak forests degradation using remote sensing and geographic information system was investigated. In order to achieve the extent and location of degradation in the studied area, the forest area map for 2001 using Landsat 7 ETM+ satellite images and forest area map for 2013 using OLI Satellite Landsat 8 images, Field visits and a method of determining educational samples were prepared. To investigate the factors affecting on degradation, first, the spatial variables including distance from residential areas, distance from roads, slope, aspect and altitude were extracted from topographic maps of 1: 50000. Then the degradation location data of the region forests with the physiographic and human spatial variables was entered the model. For modeling and estimating the spatial distribution of degradation in the study area forests, logistic regression and step-by-step method and forward step type were used. According to the results obtained from the statistical model, the development of human-made areas, population growth, reducing the distance of roads from the forest areas, the middle altitudes and reducing the slope percentage that triggered agroforestry activities and increase of agricultural land area had the highest impact on the degradation of the Ilam city forests, respectively. In fact, of 73349 hectares of forests in Ilam city, 5311 hectares (7.2%) have been reduced, of which 2125 hectares (2.9%) are related to the phenomenon of oak drying and 3186 hectares (4.3%). ) It is related to the construction of settlements, road construction, etc. Based on this, a spatial distribution map of the destruction of forests in Ilam city was prepared.

Getting informed about of land uses patterns and knowing the changes of each land use, during the time is the one of the prerequisites for the correct uses of land. During the last decades many methods of this regard have been developed. One of these methods is the use of landscape metrics. Landscape metrics are quantitative indices that describe compositional and spatial aspects of landscape. The most important impose on these studies is that changes into landscape patterns strongly affected landscape function. Due to the special ecological position of the Arasbaran biosphere, and some destructive factors such as over-grazing, high acreage of agricultural fields and encroachment of human-made structures into natural ecosystems, the area has witnessed a noticeable rate of land degradation during the last decades. For this reason the main objective of this study was analysis of land use and land cover changes using ecological landscape metrics. At first, the multi temporal Landsat images dated 1990, 2002 and 2014 were provided. Based on former knowledge from study area and study aim, satellite images were classified in seven classes including high forest, low forest, agriculture, grassland, barren land, water and urban area. For quantitative landscape metrics for each land use map, we used Fragstate software. Selected metrics included CA, PLAND, NP, LPI, MN_SHAPE, ENN, IJI, DIVISION and SHDI. The result of calculating CA, PLAND and NP metrics showed that forests were degraded and urban area and barren land were extended. Decreasing the index of the largest patch (LPI) and increasing indexes of contagion-interspersion (IJI), ENN and DIVISION for forest area indicates the destruction and disintegration of these lands. In landscape level the number of patches has increased over the time, which represents the intensity of negative and defamatory change during the study. Also, the increasing of Shannon's diversity index confirms the increasing diversity of patches in the regions as a result of increasing patch numbers and changes in landscape. Contribution of each category in altering other LULC categories was also calculated to provide an improved understanding regarding current LULC change processes in the area. Grassland is the most invasive category against low-density forest such that it occupies this category for 815 and 1,219 hectares during both time intervals, respectively. The result of calculating metrics and change detection showed the changes in the second period (2002-2014) were more intense than the first period (1990-2002). Based on the results can be mentioned that during of study the landscape in Arasbaran region completely fragmented. Also consider spatial diversity and dispersion of land uses, there is a possibility of degradation and land use change in the future. Accordingly, there is a need to develop a suitable program to prevent unwanted changes in the landscape and maintain its spatial continuity. Development in the future should be programmed based on sustainable development principles and attention be pay to protection, maintenance and ecosystem management.

Urban forests are an indicator for establishing cities and they have important aesthetic values due to environmental effects as well as providing green spaces. Trees have the most important effect on the weather of cities among green spaces and the larger the area of the forested land, the more positive outcomes would be obtained. The present study was done to compare ground survey method with Ikonos images in Google Earth Meta data for estimating various quantitative characteristics of urban forests in some parts of Sari, Iran. Using the ground survey method, we employed systematic random sampling design to estimate trees canopy cover on Jomhouri Eslami Street in Sari. The canopy of each tree was measured in two perpendicular directions and the diameter at breast height was also measured for each tree. The results of t-test showed that estimates resulting from the two methods were not significantly different (df = 118, t = 1.39). The results of regression analysis indicated that using Ikonos images (R2= 0.97) was suitable for estimating canopy area on the street. Based on our results, it can be concluded that considering the high accuracy and quick interpretation of satellite images, Ikonos images can be reliable alternatives for ground survey method in measuring tree canopies in cities with similar forest structure.

Moderate Resolution Imaging Spectroradiometer (MODIS) have been widely used for active fire detection in the world as the specific channels used for the fire detection have made the MODIS images applicable. The main objective was to explore the capability of MODIS images for active fire detection in the Zagross forests using the MODIS contextual algorithm. The study was performed in the Marivan forests located in the Zagross forests، west of Iran. Details of 20 reference fires occurred in the study areas were acquired and imported to GIS. MODIS images of fire days as well as the previous and past days of fires were collected. The Aqua images were obtained from the L1B level، which was suitable for using in this algorithm whereas Terra images were obtained from PDS format and then preprocessed to the L1B. MODIS images were processed for active fire detections using MODIS contextual algorithm. MODIS active fire detections were compared with reference fires. The results revealed that six reference fires were found. In addition، four fires were detected by images not included in the 20 reference fires. Finding the location of these four fires on the 1:25000 topographic maps showed that they have happened in urban areas. The shorter duration and lower temperature of fires during the data acquisition was probably the main reason in failing fire detection. Thirty percent of all reference fires were detected which may help to prevent more losses. It can be concluded that MODIS images provide a valuable source of information concerning the fire activity in the Zagross forests; however، the contextual algorithm was not successful in the detection of forest fires. We recommend that MODIS contextual algorithm can be used in the Zagross forests، however، complementary research، should be carried out for development a regional fire detection algorithm in this area.

This study aims at monitoring and predicting land use changes using LCM module in the Sarabeleh area. Land sat images of year 1988، 2001 and 2011 were employed to produce digital land use maps. The images were classified into five classes including forest، rangeland، barren land، agriculture and residential areas. LCM module in Idrisi GIS software was used to analyze the land use changes and predict the land uses status in 2011، based on artificial neural network (ANN) and Markov Chain analysis. ANN was trained with various influencing factors include distance from road، distance from residential areas، distance from forest edge، fragmentation index، elevation، slope and aspect. The results indicated that 14691 hectares of the forest cover have been degraded during the period 1988-2011. Furthermore، the barren land areas have been increased 9874 ha in comparison to initial situation. The results of transition power modeling using artificial neural network showed high accuracy in most of the sub modules (60-86%). The total error in modeling for the year 1390 image obtained as12/84% which represents conformity between the predicting of mode land ground truth image and acceptability of the model. Also، the result shows that forest area will decrease in the year 1400 compare to 1390 while، barren lands increase.

Monitoring of fire in natural resource using satellite imagery is the most effective method to detect active fires. The main objective of this study was to explore the capability of global active fire detection algorithm for active fire detection using the MODIS images. The study was performed in the Golestan national park located in the western Caspian forests. Totally 229 MODIS images acquired by Terra and Aqua satellites were processed for active fire detections. In order to investigate on capabilities of MODIS images in active fire detection, we evaluated the results of MODIS fire products with three reference fires occurred in the study area. The locations of the burned areas were surveyed by GPS. MODIS images from fire days as well as the previous and past days of fires were collected. The Aqua images were obtained from the L1B level, which was suitable for using in this algorithm whereas Terra images were obtained from PDS format and then preprocessed to the L1B. After processing of images, detected fires were compared with reference fires. The results revealed that the fires in July 2011 (Yekbarmakh) and November 2010 (Cheshme-Sardar) were detected, while the relative small fire (Gharangi forest) in March 2010 was not detectable. Based on the principle of active fire detection algorithm the higher temperature of fires increase the probability of detection. It can be concluded that MODIS images provide a valuable source of information about fire activity. However contextual fire detection algorithm didn’t detect high percent of fires. complementary researches and investigation are needed to develop a regional fire detection algorithm for the Caspian forests of Iran. It is recommended that active fire detection algorithm to be applied for fire detection for the Caspian forests of Iran using MODIS images before complementary researches.