object-oriented classification

Awareness of changes process, as well as the proper management of land use in natural ecosystems, is of great importance in conservation natural resources. In this regard, the use of remote sensing has become a common approach due to the provision an extent spatial and temporal information. In this research, in order to land use mapping, first, the accuracy of three common methods of pixel-based (maximum likelihood), machine learning (support vector machine) and object-oriented methods were compared. Then, the spatial and temporal changes of land use in a period of 26 years (1997-2023) assessed using six Landsat satellite imagery. The accuracy of image classification methods was evaluated using Kappa coefficient and overall accuracy indices and the change trend was evaluated using crosstab and spatial evaluation methods. Based on the results, the support vector machine method had the highest kappa coefficient (0.71 to 0.98) and overall accuracy (86 to 99%) for all studied courses. According to the results, poor rangeland had a decreasing trend, and the land uses of very poor rangeland, bare soil, and rainfed agriculture had increasing trends. The area of poor rangeland decreased from 962 hectares (44.36%) in 1997 to 489 hectares (22.57%) in 2023, while very poor rangeland increased from 1138 hectares (52.48%) to 1606 hectares (74.05 percent) in the same period. The results of this research indicated that the trend of land use changes in Jayransoo rangeland is towards the destruction of rangelands and with the passage of time this trend is intensifying. Also, based on the results obtained from this research, it is suggested to use machine learning based classification method to prepare land use mapping in future research.

IntroductionOver the past 60 years, the use of plastic covers as a tool to increase the harvest and increase the yield of horticultural crops has steadily increased worldwide. Plastic greenhouses have turned desert areas into areas with modern agricultural development, the province of Almera in the south of Spain is a good example. It now has many greenhouses, making it a global model of agricultural development; but greenhouses contain large quantities of phthalates and cause harm to people, like hormonal disturbances, heart problems, cancer, etc. However, plastic greenhouses are widely built to produce vegetables and fruits near cities. As a result, several remote sensing methods have been developed to identify and monitor the distribution of plastic covered greenhouses in order to manage water resources, identify sites and quantities of plastic greenhouses. Remote detection is the only practical method to monitor plastic greenhouses in a vast geographic area. In the past few years, there have been few studies using high spatial resolution images. In one study, three main absorption ranges were identified that are unaffected by dust, washing and surface factors. In Spain, an artificial intelligence neural network has been proposed to identify greenhouse using Quick Bird images with a resolution of 1.5m. Studies based on medium spatial resolution imagery were also conducted, resulting in different results. In search on land cover classification using Landsat TM images, no favorable results were found. Research has proposed a new method for mapping greenhouses using Sentinel-2 dual-time images and 1D-CNN deep learning. The aim of the current research is to identify the length of the cultivation period of greenhouse crops using Sentinel-2 satellite images. Researchers used a variety of satellite imagery to identify and classify greenhouses. The innovative objective of the current research is to evaluate the Sentinel-2 satellite images in determining the length of the cultivation period of greenhouse crops; which was done for the first time within the country. Material and MethodsIn the first step, the cultivated area of greenhouses was identified using an aerial photograph. Then, useful bands were extracted in greenhouse studies using Sentinel-2 time series images. Next, vegetation and plastic cover indices were calculated for the greenhouse growing period. 1) Identify and determine the area under cultivation in greenhouses: By comparing the pixel-based classification algorithms and the object-oriented classification algorithms, the cultivated area of greenhouses was identified. ENVI and eCognition software were used for pixel-based classification and object-oriented classification, respectively. It should be noted that only one aerial photograph with three bands, blue, green and red, has been used in object-oriented classification. In order to classify the base pixel, learning samples were selected using expert knowledge from the study area. In some instances, Google Earth was used as well. Learning samples were selected scattered across the image to improve accuracy. For validation of the maps obtained from these algorithms, ground control points were used. To reduce human error, these points were also entered into Google Earth. 2) Identifying functional bands in the greenhouse study: The spectral behavior curve of different earth surface covers in Blue, Green, Red, SWIR, NIR bands was drawn in an image of the Sentinel-2 satellite. These spectral signatures were compared on May 20, 2020 i.e., when outdoor vegetation and greenhouse cultivation were at their peak. 3) Determining the duration of the greenhouse growing season: Vegetation indices of the NDVI, SAVI, OSAVI, MSAVI, GNDVI, RVI, DVI, RGVI, IPVI and plastic cover indices PGI, RPGI, PI, PMLI were compared using Sentinel-2 satellite time series images. Results and DiscussionGreenhouse cultivation is used to improve quality and increase food production. However, their development has many negative effects on the environment. Therefore, obtaining accurate and timely information on the distribution of greenhouses and the time of planting and harvesting crops under plastic covers can make a significant contribution to agricultural management, water management and soil protection. The present study is the first study to identify the length of the greenhouse cultivation period using Sentinel-2 satellite time series images. The results showed that among the object-oriented classification algorithms, two classification algorithms, Bayes and KNN, were more precise for the identification and determination of the cultivated area of greenhouses. The reflection of the plant below the plastic cover of the greenhouse in the NIR, Narrow NIR, Red Edge and SWIR bands increases by an equal amount in comparison with the reflection from the vegetation in the open space. Comparing vegetation cover and greenhouse cover indices showed that the indices designed based on SWIR and Red bands showed greater reflection during the hot season of the year. Indices based on NIR, Narrow NIR, and Red Edge bands were more reflective from October to late February. Based on the results obtained from the ground truth data, greenhouses are plastered during the warm season. This caused an increase in the reflectance in indices designed based on SWIR and Red bands and a decrease in reflectance in indices designed based on NIR, Narrow NIR, Red Edge bands, during the hot season. Therefore, combining all indices, two crop periods were observed: the first started in early March, peak cover was reached in April, and harvest continued. Until the middle of August. The second harvest started at the end of August and peaked in December, until the end of harvest in mid-February.

Human activities such as land-use changes have a significant effect on the Earth’s surface temperature and the creation of heat islands. This study aimed to investigate the impact of land-use changes on the temporal-spatial pattern of the surface temperatures and heat islands in Shooshtar township between 2002 to 2020. First, the Landsat 2002, 2013, and 2020 images were classified using object-oriented processing methods, and then the Earth surface temperature was extracted by the split-window algorithm. Having measured the temporal-spatial changes in the surface temperature and the heat islands, the indices NDVI, UHII, and UHIII were implemented. The results showed an increase in Built-up areas as well as Barrenlands by 21895.40 and 26761.04 hectares, individually. By contrast, the Vegetation cover and Waterbodies decreased down to 43790.79 and 4865.64 hectares, respectively. According to the results, the maximum temperatures index revealed an uptrend in the temperature of all the landuses; there has been a constant increase with an amount of 7.40, 7.42, 6.82, and 6.87 degrees Celsius in the temperatures of the landuses the Vegetation cover, Barrenlands, Built-ups, and Waterbodies, respectively, during the 2002-2020 study period. The UHII and UHIII indices showed that the low land-cover class had a higher temperature than the classes with medium and large land-covers. Furthermore, the heat islands were distinguishably formed by 2013 and 2020 most in the northern, southeastern, and northeastern areas of Shooshtar township.

The main objective of the present study is to delineate all-natural resources of Iran with the priority of desert and semi-desert areas using indicators and criteria extracted from remote sensing data and new satellite image classification techniques. Accordingly, desert, semi-desert, and salinity areas of Iran in conjunction with other natural resources areas (such as forests, rangelands, water bodies, and farmlands) were studied using time-series MODIS satellite images and different indices and parameters such as Albedo, NDVI, and surface temperature during day and night along with the temperature difference between day and night. Here, unlike the classical classification methods, based on using one-single satellite image and features such as vegetation density or surface temperature, the behavior of different natural resources over time, extracted from satellite images, was analyzed. Accordingly, the temporal behavior of each of the mentioned natural resource areas during 2019 was studied and analyzed using the remote sensing criteria. The basic object classification method was used to classify the country using the mentioned indicators using the least distance technique based on fuzzy logic. Based on results, about 41.2%, 14.8%, and 3.9% of Iran (totally about 60% of Iran) are classified as desert, semi-desert, and salty areas, respectively. By considering the percentage of rocky mountainous areas (11.3%), about 71.2% of Iran has no biological conditions (unsuitable for agricultural activities). As expected, desert and semi-desert areas are concentrated in central, eastern, southeastern, and southern regions of Iran, and no signs of such areas are found in the northern, northwestern, and western of the country.

In recent years, due to the expansion of industrial activities, the concentration of heavy metals in the environment as well as foods has increased. Heavy metals are dangerous because of their bioaccumulation. Regarding the importance of contamination of heavy elements of the soil, the present study aimed to provide the spatial distribution of heavy metals and its relationship with land use in the Yazd-Ardakan plain, Iran. First, 201 soil samples from depths of 0 to 20 cm were sampled using the hypercube method, and the total concentration of iron, manganese, nickel, lead, and zinc elements were determined using Analytical Jena-novAA300 atomic absorption device. Then, to convert point data to surface data, geostatistical methods of IDW, GPI, RBF, LPI, and Kriging were used. The land cover/use map of the Yazd-Ardakan plain in 2016 was mapped using an object-oriented classification method. Results of the relationship between heavy metals concentration and land cover/use showed that the agricultural lands and gardens and sand dunes with the mean of 0.950 and 0.836 ppm had the highest and lowest iron concentrations. The highest mean concentration of manganese was related to the residential land (1.821 ppm) and the lowest mean of rocky terrains (1.083 ppm), the most average for poor rangelands and bare land was (0.302 ppm), Residential areas had the lowest nickel concentration (0.192 ppm). The highest mean of lead metal in agricultural land and gardens, as well as residential areas (1.465 and 1.373 ppm, respectively) and the lowest, mean in rocky terrains (0.925 ppm). Agricultural and gardens areas, and residential lands, have the highest mean of zinc concentration (0.583 and 0.552 ppm, respectively), and the rocky terrain has the lowest zinc concentration (0.342 ppm).