Showing 27 results for Remote Sensing
S Falhakar, A Saffianian, S.j Khajeddin, H Ziaei,
Volume 13, Issue 47 (4-2009)
Abstract
Remote sensing is the main technology for assessing expansion and rate of land cover changes. Knowing the different kinds of land cover changes and human activities in different parts of lands, as the base information for different planning is especially important. In this study, the land cover changes of Isfahan city that is consist of Isfahan and its` surrounded area was studied for the past 4 decades. For researching the study objectives, the aerial photos with scale of 1:50000 taken in 1955, MSS, TM and ETM+ images from Landsat satellite taken respectively in 1972, 1990 and 2001 and the topography maps of Isfahan city and its` surrounding were used. All of the aerial photos and satellite images with the nearest neighbor sampling were georegistered with the RMSe less than one pixel. For image processing, the best false colored composite image was first produced according to OIF index. Then land cover maps of the studied area were produced in 5 classes by using the combination of supervised and unsupervised classification and NDVI index. At the end, the produced maps compared with post-classification method. The results showed that the most urban area sprawl was occurred between 1972-1990 with the mean of 571 ha in a year and the least growth was come about between 1955-1972 with approximately 324 ha in a year. However, by declining the annual mean of green cover 1263 ha during 1955-1972, the most green cover demolition occurred in study area.
A Soffianian,
Volume 13, Issue 49 (10-2009)
Abstract
Monitoring Land Use and Land Cover Changes have a significant role in environmental programming and management. Satellite data is an essential tool for detecting and analyzing environmental changes. Many change detection techniques have been developed which have advantages or disadvantages. Change Vector Analysis (CVA) technique is one such a method. This method is based on radiometric changes between two dates of satellite imagery. Main advantage of this method is that it provides direction and magnitude image of change. The aim of this study was to describe change vector analysis technique and it applies to detect land cover change in Isfahan area during an 11-year period. The data used for this study were two images Landsat: TM 05 June 1987 and 03 June 1998. Correction radiometric was not carried out because of the similar sensor and acquisition time of the remote sensing data. After geometric correction, the study area was selected from Landsat images. Change vector technique was applied to analyze magnitude and direction of change. The change map showed Kappa and overall accuracy coefficient of 63.19% and 74.4%, respectively. The results showed that the changed land cover was 3340 ha during this period. Overall, the results show that 1325 hectares (especially agricultural lands) have been converted into urban areas, agricultural areas were increased up to1385 hectares, and 435 hectares of agricultural areas were converted to other land use over the period of study. This study showed that CVA is a robust approach for detecting and characterizing radiometric change in multi-spectral remote sensing data sets.
A. Soffianian, M. A. Madanian,
Volume 15, Issue 57 (10-2011)
Abstract
Land cover maps derived from satellite images play a key role in regional and national land cover assessments. In order to compare maximum likelihood and minimum distance to mean classifiers, LISS-III images from IRS-P6 satellite were acquired in August 2008 from the western part of Isfahan. First, the LISS-III image was georeferenced. The Root Mean Square error of less than one pixel was the result of registration. After creating false color composite and calculating transformed divergence index, the images were classified using maximum likelihood and minimum distance to mean classifiers into six categories including river, bare land, agricultural land, urban area, highway and rocky outcrops. The results of classification showed that the dominant land cover type is urban area, occupying about 6821.1 ha representing 38.86% of total area. The accuracy of maximum likelihood and minimum distance to mean classifiers was obtained using error matrix and Kappa analysis. According to the results, the maximum likelihood algorithm had an overall accuracy of 94.93% and the minimum distance to mean method was 85.25% accurate. The results illustrate that the maximum likelihood method is superior to minimum distance to mean classifier.
L. Khodakarami , A. Soffianian,
Volume 16, Issue 59 (4-2012)
Abstract
Precision farming aims to optimize field-level management by providing information on production rate, crop needs, nutrients, pest/disease control, environmental contamination, timing of field practices, soil organic matter and irrigation. Remote sensing and GIS have made huge impacts on agricultural industry by monitoring and managing agricultural lands. Using vegetation indices have been widely used for quantifying net annual production on different scales. The aim of this study was to find a rapid method with acceptable precision for the identification and classification of agricultural lands under cultivation (wheat and barley, alfalfa and potatoes). We used multi-temporal AWiFS data and applied Boolean logic and unsupervised classification.
Results indicated that Boolean logic approach had a higher accuracy and precision in comparison to unsupervised classification, although it is more complicated and time consuming.
Majid Vahdatkhah, Mohammad Hady Farpoor, Mehdi Sarcheshmehpoor,
Volume 17, Issue 64 (9-2013)
Abstract
Study of land use effects on soil quality indicators leads to sustainable management and preventing progressive land degradation. The TM (1987) and ETM+ (2000 and 2005) data were used to study land use change effects in Mahan-Joopar area on soil quality indicators. Fifty random soil samples from 0-30 cm depth of each land use were taken using provided maps. Organic matter, microbial respiration potential, bulk density, pH, EC, and soil texture were investigated as soil quality indicators. Eight land uses including fruit orchards, woodlands, pistachio orchards, cultivated, barren, bare land, fallowed, and haloxylone land were detected. Results showed overall accuracies of 89.4, 95.2, and 91.7 % with kappa coefficients of 85, 92, and 88% for maps provided in 1987, 2000, and 2005, respectively. Generally, the investigated quality indicators showed that woodlands, fruit orchards, cultivated land, and pistachio orchards enhanced soil quality better than other land uses.
F. Mahmoodi, R. Jafari, H. R. Karimzadeh, N. Ramezani,
Volume 19, Issue 71 (6-2015)
Abstract
This study aimed to evaluate the performance of TM satellite data acquired in June 2009 to map soil salinity in southeast of Isfahan province. Ground salinity data (EC) was collected within 9 pixels, covering an area of approximately 8100 m2 using stratified random sampling technique at 53 sample sites. Spectral indices including TM bands, BI, SI1, SI2 and SI3, PC1, PC2, PC3 and also multiple linear regression modeling and maximum likelihood classification techniques were applied to the geometrically corrected image. Results of regression analysis showed that the TM band 4 had the strongest relationship with EC data (R2=0.48) and also the relationship of the modeling image using TM 3, TM 4, TM5 and PC3 was significant at the 99% confidence level. The accuracy assessment of the stratified TM4 and modeling image into five classes including 0-4, 4-20, 20-60, 60-100 and EC>100 ds/m indicated that there was more than 86% agreement with the field measurements of EC data. Therefore, it can be concluded that the discretely classified salinity maps have higher accuracy than regression methods for identifying broad areas of saline soils, and can be used as appropriate tools to manage and combat soil salinization.
M. Mokhtari, A. Najafi,
Volume 19, Issue 72 (8-2015)
Abstract
Land use classification and mapping mostly use remotely sensed data. During the past decades, several advanced classification methods such as neural network and support vector machine (SVM) have been developed. In the present study, Landsat TM images with 30m spatial resolution were used to classify land uses through two classification methods including support vector machine and neural network. The results showed that SVM and neural network with the total accuracy of 90.67 % and 91.67% are superior. SVM had a better performance in separating classes with similar spectral profiles. In addition, SVM showed a better performance in delineating class borders in comparison with neural network method. In summary, both SVM and neural network showed satisfactory results but the method of support vector machine proved better with a difference of 1% and 2% in overall accuracy and kappa coefficient, respectively. This was an expected outcome because SVMs are designed to locate an optimal separating hyperplane, while ANNs may not be able to locate this separating hyperplane.
N. Moshtagh, R. Jafari, S. Soltani , N. Ramezani,
Volume 19, Issue 73 (11-2015)
Abstract
Spatial estimation of evapotranspiration (ET) rates is essential for agriculture and water resources management. This study aimed to estimate ET v an ET estimation algorithm called Surface Energy Balance Algorithms for Land (SEBAL) and also by using TM June 2009 satellite data in Damaneh region of Isfahan province. To calculate the ET, all the energy balance components and related parameters including net radiation, surface albedo, incoming and emitting shortwave and longwave radiation, surface emissivity, soil heat flux, sensible heat flux, NDVI vegetation index, Leaf Area Index(LAI), and surface temperature were extracted from the geometrically and radiometrically corrected TM images. Results showed that ET rate was about 7.2 mm day-1 in agricultural areas, which was almost equal to 6.99 mm day-1 extracted from the FAO Penman-Monteith method in the synoptic weather station of Daran. Results here indicate that the extraction of ET rate which is almost equal to plant water requirements from remote sensing data can be used in selecting appropriate plants for agriculture and rehabilitation purposes in extensive arid and semi-arid regions of Isfahan province where severe droughts and water shortage are major problems.
D. Dezfooli, S. M. Hosseini-Moghari, K. Ebrahimi,
Volume 20, Issue 76 (8-2016)
Abstract
Precipitation is an important element of the hydrologic cycle and lack of this data is one of the most serious problems facing research on hydrological and climatic analysis. On the other hand, using satellite images has been proposed by many researchers as one of practical strategies to estimate precipitation. The present paper aims to evaluate the accuracy of satellite precipitation data, provided by PERSIANN and TRMM-3B42 V7 in Gorganrood basin, Iran. To achieve this aim, two sets of daily precipitation ground-based data, 2003 to 2004 and 2006 to 2007, from six stations of Gorganrood basin, named; “Tamer”, “Ramian”, “Bahalkeh-ye Dashli”, “Gorgan Dam”, “Ghaffar Haji” and “Fazel Abad” have been used in this paper. The evaluation indices have been calculated and analyzed in different time scales, including daily, monthly and seasonal. The results indicated that the two above mentioned satellite models are not accurate in daily scale. However, they showed reasonable accuracy in monthly and seasonal scales. The highest correlations between satellites and recorded data in daily and monthly scales, for TRMM-3B42 V7 in “Gorgan Dam” and “Bahlke Dashlei” stations, are 0.397 and 0.404, respectively. The comparison of measured and satellite data of winter showed better agreement for PERSIANN model. However, TRMM-3B42 V7 shows better correlation in other seasons. The results also indicated that while TRMM-3B42 data displays higher correlation with measured data, PERSIANN provids better results in predicting the number of rainy days.
H. Adab,
Volume 21, Issue 2 (8-2017)
Abstract
A limited number of agricultural weather stations measure moisture in the soil surface. Furthermore, soil moisture information may be required in areas where there is no weather station. The aim of the present study was to use Landsat 8 satellite images to estimate soil surface moisture in an area without agricultural meteorological stations. Gravimetric soil moisture for a total of 14 samples was calculated in the cold season in depths of 0-10 cm when Landsat 8 satellite was overpassing poor rangeland of North of Sabzevar. Furthermore, the first four principal components were extracted from seven Landsat-derived vegetation indices and bio-physical factors affecting soil moisture. Afterwards, the first four components were used to estimate soil surface moisture at the moment of the satellite passing the region using a multivariate linear regression and neural networks. The obtained results of instantaneous soil surface moisture showed that the neural networks had mean absolute percentage error of while classical regression analysis had mean absolute percentage error of 40%. The results also showed the benefits of using both in-situ soil moisture data and Landsat 8 satellite images to model instantaneous soil surface moisture content for areas lacking meteorological networks.
R. Ziaee, M. Moghaddasi, S. Paimozd, M. H. Bagher,
Volume 22, Issue 4 (3-2019)
Abstract
Evaporation is one of the important components in water body’s management, leading to changes in the water level and water balance. Also, its accurate estimation is faced with certain difficulties and complexities. Because of the limitations of physical and empirical methods based on the meteorological data, remote sensing technology can be widely used for evaporation calculation due to its capabilities for spatial data estimation and minimization of the meteorological data application. Many models have been developed to estimate evapotranspiration using remote sensing technology. Regarding the use of these algorithms for estimating evaporation from water surface, a few studies have been done; however, there is yet no comparison between them to estimate evaporation from the water surface. For this purpose, in this study, the output from two models estimating spatially distributed evaporation of water surfaces from remotely sensed imagery is compared. In order to implement these models, Terra/MODIS Images for four months including June, July, August and September in of 2006, 2007, 2008 and 2009 were prepared. Comparisons were made using pan data from Urmia synoptic station. In general, there was a reasonable agreement between the evaporation outputs from both models versus a pan data observation. The statistical analysis also showed that the SEBS algorithm (by applying the salinity factor), despite being simple in its implementation, has higher accuracy than the SEBAL algorithm.
B. Noori, H. Noori, Gh. Zehtabian, A. H. Ehsani, H. Khosarvi, H. Azarnivand,
Volume 23, Issue 4 (2-2020)
Abstract
Due to the impact of climate change on the plant water demand and the availability of water, especially in drylands, it is vital to estimate the evapotranspiration rates accurately. In this study, the vegetation status in the marginal desert areas of Varamin Plain was studied, and the actual evapotranspiration and water demand of intercropped farms were assessed. This study also evaluated the potential relationship between the evapotranspiration of different agricultural lands and their vegetation index using remote sensing techniques. A collection of satellite images from Landsat 7 in consecutive seasons was used to determine the greenness rate of marginal desert areas during 2013 and 2014. ENVI software was used for the image processing, which included geometric corrections and atmospheric corrections, to develop NDVI maps. Also, weather data and crop properties of Varamin Plain were collected, and the actual evapotranspiration rate of plant cover was estimated using CropWat. The correlation between NDVI extracted from satellite images and the evaluated evapotranspiration rate was assessed. The results showed a strong relationship between evapotranspiration of heterogeneous agricultural lands and NDVI. This confirmed that the NDVI derived by remote sensing approach could be a useful index to evaluate vegetation status and water demand of farmlands in the desert borders.
M. Farokhi, H. Ansary, A. R. Faridhosseini,
Volume 24, Issue 1 (5-2020)
Abstract
Estimation of soil moisture at various temporal and spatial scales is a key to the strategic management of water resources. Satellite-based microwave observations have coarse spatial resolution despite widespread and continuous of the provision surface soil moisture (SSM). In this study, the SSM data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) 25km resolution were used and these products were downscaled by three parameters retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) to 1km resolution. In the next step, the integration of the SSM downscaling model with SMAR model was used to monitor the root zone soil moisture(RZSM) in the study area (Rafsanjan plain). In order to evaluate the performance of the proposed method, the SSM and the soil profile moisture were measured at 10 points in the Rafsanjan plain. Comparison of AMSR2 25k SSM and downscaled SSM with the field measurement data showed that the mean of total stations for the correlation coefficient(R) was increased from 0.540 to 0.739 and the mean absolute error(MAE) and the root mean square(RMSE) were reduced from 0.039 and 0.040 to 0.018 and 0.020, respectively. Moreover, the results obtained from the validation of the RZSM values showed that the proposed method could estimate the RZSM with high accuracy and indicate the variations.
M. Pajoohesh, M. Mohammad Yousefi, A. Honarbakhsh, H. R. Reyahi Bakhtyari,
Volume 24, Issue 1 (5-2020)
Abstract
In order to plan and manage the land and its changes, it is necessary to identify and evaluate the factors affecting it. Land use / cover changes are one of the main factors in global environmental change that is defined as a change in the type of land use; it is one of the major factors changing hydrologic flow, land erosion and destruction of biodiversity. The main purpose of this study was to assess the trends of land use changes in Beheshtabad Watershed of Chaharmahal and Bakhtiari Province with an area about 3847 square kilometers by using remote sensing and GIS during a 25-year period. In this research, first, analyzing and pre-processing the satellite images of Landsat 5 TM sensors from 1991 and 2008 were done, and Landsat 8 of OLI sensor of 2016 was applied. Then, by using the hybrid classification method, 5 land use classes including pasture lands, urban-building lands, agricultural lands, garden lands and bare lands, land use maps for the three time periods were prepared. The overall accuracy of the obtained land use maps for 1991, 2008 and 2016, was 92.17%, 94.29% and 93.41%, respectively, indicating the acceptable accuracy of the maps. Then, the process of land use change and the contribution of each land use classes and the percentage of changes in each land use class were determined in two study periods. The results of this study showed some changes occurred in the studied watershed. The total area of pasture lands during two periods indicated the decreasing trend, but urban-building and garden lands during two periods represented the increasing one. Agricultural lands during the first period indicated the decreasing trend and during the second period showed the increasing trend, while bare lands during the first period showed the increasing trend and during the second period, reflected the decreasing trend. In general, it should be noted that in the Beheshtabad watershed, we could see an increase in the replacement of pastures by urban-building class, rainfed agriculture, gardens, and bare lands, the incidence of destruction in the region.
J. Jalili, F. Radmanesh, A. A. Naseri, M. A. Akhond Ali, H. A. Zarei,
Volume 24, Issue 3 (11-2020)
Abstract
Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models.
M. Kaffash, H. Sanaei Nejad,
Volume 25, Issue 2 (9-2021)
Abstract
Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.
A. Norouzi, M.r. Ansari,
Volume 25, Issue 3 (12-2021)
Abstract
At present, the occurrence of dust storms is one of the most important environmental problems in Khuzestan Province, and the south and southeast regions of Ahwaz have been recognized as one of the interior dust sources and are the priority of corrective operations. Given that land use change is one of the desertification factors in the mentioned region, therefore, modeling its changes is necessary and provides useful information for planners to control and revive the degraded lands. The objective of this study was to evaluate the efficiency of the CA-Markov model in predicting land use changes in the dust source of south and southeast of Ahwaz based on two long-term and short-term approaches. In the long-term approach, land use maps of 1986 and 2002 years and in the short-term approach, land use maps of 2002 and 2007 years have been used to predict land use for the year 2016 and then the simulation results were validated. The results showed that the values of allocation error, quantity error, and kappa coefficient for the long-term approach were 42.55%, 13.95%, and 0.08 respectively, and for the short-term approach were 12.56%, 10.42%, and 0.22 respectively, which indicates the weak ability of the CA-Markov model to evaluate the desertification trend in the dust Source of south and southeast Ahwaz. Use of uniform transition rule throughout the simulation period without considering the factors and processes affecting land use change, the non-same trend of land use change during study periods, changes due to human activities, drought, and long forecast period can be the reasons for the poor performance of the CA-Markov model to predict the desertification trend the dust Source of south and southeast Ahwaz.
Miss S. Bandak, A.r. Movhedei Naeani, Ch.b. Komaki, M. Kakooei, J. Verrlest,
Volume 27, Issue 3 (12-2023)
Abstract
Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that have an important role in sustainable production in agriculture and preventing soil degradation and erosion. Data mining approaches and spatial modeling besides machine learning techniques to investigate the amount of soil organic carbon using remote sensing data have been widely considered. The objective of the present study was the evaluation of SOC using the remote sensing technique compared with field methods in some areas of the Gonbad Kavous and Neli forests of Azadshar. The soil samples were collected from the soil surface (0-10 cm depth) to estimate the SOC. Data were categorized into two categories: 70% for training and 30% for validation. Three machine learning algorithms including Random forest (RF), support vector machine, extra tree decision, and XGBoost were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI and sentinel 2 measurement images, topography, and climate. The results showed that the extraction of the components related to the bands along with the calculation of indicators such as normalized vegetation difference, wetness index, and the MrVBF index as auxiliary variables play an important role in more correct estimation of the amount of soil organic matter. Comparison of different estimation regressions showed that the Sentinel 2 random forest model and in Landsat8 with the values of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MEA) of 0.64, 0.05, and 0.17, respectively, was the best performance ratio compared to other approaches used in the study to estimate the organic carbon content of surface soil in the study area. In general, the results of this study indicated the ability of remote sensing techniques and learning models in the spatial estimation of soil organic carbon. So, this method can be used as an alternative to laboratory methods in determining soil organic carbon.
B. Ebrahimi, M. Pasandi, H. Nilforoushan,
Volume 27, Issue 4 (12-2023)
Abstract
The different land uses in the irrigation water area of the eleven streams of Khansar city during 1969, 1995, 2014, and 2019 have been identified and their area has been determined by analysis of the aerial photos as well as the satellite images of QuickBird, and Landsat in the Google Earth Engine (GEE) environment. Then, the net and gross areas of land under irrigation water, area of non-agricultural land uses, location and area of agricultural land uses under irrigation of the streams are separated according to the type of agricultural activity (orchard or farmland) for each stream. Aerial photos of the study area dated 1969 are the basis for the assessment of agricultural conditions before the law of Fair Water Allocation. The results showed that non-agricultural and particularly urban and residential land uses have increased since 1969. In other words, land use of part of the agricultural lands has been changed to residential and urban land uses. Despite the decreasing trend of agricultural land uses in the last 50 years, these changes have not been the same between the farm and orchard land uses and the area under orchard plantation showed an increasing trend. These changes have dramatically influenced on water demand of the streams. Land use has not significantly changed from 2014 to 2019 and no noticeable change was observed in the area of the agricultural and green agricultural lands as well as the percentage of the orchard and farming lands during these years. The results of this study confirmed the significant changes in agricultural land use and consequently water consumption in the district of the eleven streams of Khansar in recent decades. This study also highlighted the high efficiency of the combined use of aerial photos, spectral satellite images with medium spatial resolution, and visible spectral satellite data with high spectral resolution, as well as using cloud system capabilities of the Google Earth Engine to study changes in agricultural land uses during last decades.
S. Koohi, B. Bahmanabadi, Z. Partovi, F. Safari, M. Khajevand Sas, H. Ramezani Etedali, B. Ghiasi,
Volume 27, Issue 4 (12-2023)
Abstract
Water supply remains a significant challenge in arid and semi-arid regions, and in addressing this concern, unconventional water sources have gained prominence. Notably, the extraction of water from air humidity, classified as an unconventional water source has seen increased adoption. Diverse techniques have been developed to achieve this goal, with the utilization of mesh networks being particularly prevalent. Consequently, this study assesses the evaluation of the performance of the ERA5 dataset in the simulation of atmospheric variables that influence the ability to assess water harvesting from air humidity (including temperature, wind speed, and water vapor pressure). Also, the possibility of water harvesting from air humidity was investigated in Qazvin Province. The outcomes demonstrated the benefit of incorporating adjustment coefficients in estimating temperature and wind speed using the ERA5 dataset. Based on these findings, the northwestern and southern regions of the province (Kuhin and Takestan) exhibit notable potential during spring and summer for water harvesting from the atmosphere. The peak water harvesting for these stations in the summer is estimated at 10.2 and 9.7 l/day.m2, respectively. Using the ERA5 reanalysis dataset, the annual average potential for water harvesting in the stations was evaluated at 7.9 and 4.6 l/day.m2, respectively. Notably, the minimum water harvesting capacity during the summer season recorded in Qazvin is equal to 3.39 l/day.m2, which can be planned for use in irrigation requirements of green spaces, fields, or gardens.
