Vegetative Area Research Articles

Combining Geographical Information System (GIS) and machine learning to monitor and predict vegetation vulnerability: An Empirical Study on Nijhum Dwip, Bangladesh

Vegetation loss has become a global concern as it is directly and indirectly harmful to all living beings, specifically to humans. By realizing the dimension of this issue, we have developed an Artificial Neural Network (ANN) based model by combining GIS and machine learning to investigate and predict the vegetation vulnerability with reliable accuracy. This model incorporates a sequence of geostatistical analyses, i.e., Normalized Difference Vegetation Index (NDVI), Hot Spot Analysis (Gi*) and Inverse distance weighted (IDW). 8 drivers, used in this study, were shuffled differently to obtain the highest accuracy possible and investigate their influence on land shift. However, the model was implemented on Nijhum Dwip island to quantify its credibility and evaluate the ecological stability and vulnerability status of this island. According to the findings, the island has undergone significant change between 2001 and 2021. The overall vegetative area has increased in this time as a result of the ecological reforestation projects undertaken after 2001. Then, our developed hybrid model was used to simulate the hot spot map of 2021 to quantify the accuracy of the model. Anyway, the kappa statistics was found more than 0.86 with 88.75% overall correctness, and the same weight values were utilized to predict the hotspot map of 2026 and 2031. The predicted maps showed a gradual increase in the vulnerable zones, which is the outcome of the uncontrolled extracting of natural resources. Eventually, the methodological knowledge of this study can help researchers as well as policymakers to estimate vegetation vulnerability and legislate new policies that support sustainable development, and the quantitative knowledge on Nijhum Dwip can facilitate future planning of this island.

Spatiotemporal Variation of Land Use Pattern and Land Cover Changes in Gidan Kwano Watershed Area in North Central Nigeria

Land Management Systems (LMS) are institutional frameworks complex by the tasks they must achieve, national, cultural, political, and judicial settings and technology. The urbanisation process in Nigeria has increased since the 1960s because of the crude oil boom era and other government-backed industrial initiatives and investments. This study employed the use of both primary and secondary sources of data. Preliminary data comprise methods of land use, types of agricultural activities carried out and the process of working on the land by the farmers within a 10 km radius. The secondary data involved the interactive digital and visual techniques of the Geographical Information system for five seasons, with each having a ten-year interval span within 1975 to 2015. Statistical analysis was carried out with SPSS 2013 and XLSTAT 2015. Five land use and land cover types were observed within the Gidan Kwano watershed, which includes wetlands (WL), water bodies (WB), bare grounds (BG), vegetation (VG), and settlements (SL). The most prevalent landform in the study area during the 1975 period was the vegetative area which was 50% of the total landmass. Thus, the vegetation (VG) covered half of the Gidan Kwano watershed. However, the vegetative area decreased substantially during the study period of 1975 to 2015. It was observed that the vegetation (VG) within the study area had the highest percentage of coverage, of 34%. During the study period, a significant decrease was observed in the WL, WB and VG areas. It was also concluded that due to the built-up places, the infiltration of surface runoff from rainwater would be drastically reduced as most of the sections are paved for construction activities while a section of the study area is covered with rock outcrops and farmlands.

Fenotipagem de alto rendimento por análise de imagens RGB e multiespectral de genótipos em milho doce

ABSTRACT Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.

Estimating NDVI and LAI as a precursor for monitoring air pollution along the BBN industrial corridor of Himachal Pradesh, India

The research conducted is to establish the role of Normalized Difference Vegetative Index (NDVI) and Leaf Area Index (LAI) as a tool for monitoring air pollution through resolution multispectral images for Baddi Barotiwala Nalagarh (BBN Industrial Corridor). The air pollution is estimated in terms of Respirable Suspended Particulate Matter (RSPM). The LAI was estimated through high-resolution Multi-spectral imageries for the years (2001 to 2021). LAI analysis reveals that the majority of the study is covered under moderate to very high plant canopy density region. Analysis of the 2016 image revealed vegetation indices ranging between −0.9 to 2. Decrease of the forest vegetation quality was observed between 2001 and 2021 also causes the increase in the absorption of air pollutants while at the same time. In the year 2009 the RSPM of the areas has been registered at an average of 76 ug/m3. Between 2010 and 2011 the average air quality registered at 106.67, 105.50, 83.33. The results indicate the air quality has been worsening through the years (2009 and 2020) RSPM of these regions has doubled increasing the toxicity of the pollutants. Analysis of the decadal change in NDVI and LAI shows the reduction of the annual forest coverage of the BBN industrial corridor coupled with the decrease in the air quality of the region. The study shows a clear relation between decreasing vegetation cover and the air quality of the region.

Effect of Boron and Nitrogen Level under Different Plant Spacings on some Growth and Yield Parameters of Broccoli (Brassica oleracea var. italica Plenck) in a gypsiferous soil

A field experiment was conducted in the fields of the Faculty of Agriculture, Tikrit University for the 2019-2020 agricultural season to study the effect of planting distances and the level of nitrogen fertilization and boron on the growth and yield of broccoli in gypsum soil. The study included three factors, the first factor was the distance between plants, and it included four distances (35, 45, 55, and 65 cm) (D1, D2, D3, and D4), respectively. The second factor was the level of nitrogen application, which includes two levels of nitrogen 70 and 140 (kg N ha-1) (N1 and N2), respectively. The third factor was the level of boron addition and it includes two levels of boron, which are (without boron application and 6 kg B. ha-1) (B0 and B1), respectively. Results showed that the nitrogen level N2 gave a clear significant increase compared to the N1 level in dry matter yield (the first cut), reaching (21.58 g. Plant-1), while the first level N1 reached (17.94 g(. Dry matter yield (the second cut) reached (84.39 g. Plant-1) in the second level N2, while in the first level it reached (74.36 g. Plant-1) and the N2 level was significantly superior over N1 level for some indicators of vegetative growth, plant height (cm) and leaf area (cm2). -1) and the number of leaves (leaf-plant-1) and thus the yield increased significantly in this level of nitrogen at the N1 level, as the total yield of floral heads at the N2 level was (25.865 tons. Ha-1) and at the N1 level (22.6 ton ha-1). The addition of boron fertilizer at a rate of (6 kg B. ha -1) led to a significant increase in the dry matter yield (the first and second cuts) and to a significant increase in the yield, as well as in the boron concentration in the shoot and the floral heads, and the vegetative growth indicators of broccoli. Planting at a 65 cm among broccoli plants (D4) led to a significant increase in dry matter yield (first and second cut) and an increase in plant yield. The superiority was significant over other distances except for the distance D1, which gave the highest total yield of broccoli floral heads, reaching 26.63 (tons ha-1). 1), whereas the distances D2, D3, and D4 gave a total yield of 25.38 , 22.71, and 22.21 (ton ha-1), respectively. The triple interaction between nitrogen (N) and planting distances (D) and boron (B) gave the highest value at the treatment (D4N2B1) which was significantly superior over other treatments for most of the studied traits, except the total yield of the floral heads where the superiority was in favor of treatment D1N2B1.

Diversity in Phenological and Agronomic Traits of Miscanthus sinensis Collected in Korea and Eastern Asia

Four-year field experiments were conducted to investigate phenotypic traits associated with the biomass yield of 173 Miscanthus sinensis accessions collected from Korea and neighboring East Asian countries. Nine phenological and agronomic traits associated with biomass yield were assessed to investigate their phenotypic diversity and relationships with biomass yield as well as the latitudes of the M. sinensis accessions collection sites. Correlation analyses among phenological and agronomic traits, biomass yield, and collection site revealed that heading date, vegetative growth duration, leaf area, and stem growth traits (stem height, stem diameter, and stem dry weight) were closely related to biomass yield. The latitude of collection site exhibited a significant negative correlation with heading date, and heading date showed a significant positive correlation with biomass yield, indicating the high biomass potential of the accessions originating from lower latitude due to longer vegetative growth. The best biomass yield was mainly observed in M. sinensis accessions from the southern parts of Korea, such as Jeolla and Jeju provinces, with over 20 Mg DM ha−1. Agronomic traits measured in the second year after planting also showed a high correlation with biomass yield measured in the fourth year after planting. In particular, vegetative growth duration, leaf area, stem diameter, and stem dry weight measured in the second year were significantly related to the fourth-year biomass yield. Therefore, these findings suggest that agronomic traits measured in the second year can be used for screening M. sinensis genetic resources and breeding lines with high biomass yield potential.

Effect of Foliar Spray of Seaweed (Alga300) and Licorice Extracts on Growth, Yield and Fruit Quality of Pomegranate Trees Punica Granatum L. Cv.Salimi

This study was conducted in a private-orchard in the Kumptler village of Kirkuk / Iraq during the growing season of (2019), to study the effect of foliar spraying with seaweed extract (Alga300) in three concentrations (0, 5, 10) mg.L1 and licorice extract in three concentrations (0, 5, 10) g.L −1 in some growth traits and yield for pomegranate trees, cv.Salimi. The factorial experiment within randomize complete block design was used. The results obtained can be summarized to the fact that foliar spraying pomegranate trees with a concentration of 10 mg.L−1 of seaweed extract represented by Alga300 led to a significant increase in most of the study characteristics (average length of vegetative growth, leaf area, average size and weight of fruit, average number of fruits, TSS% and total yield/tree) while the spray was superior to the concentration of 5 mg.L1 of Alga300 in the relative chlorophyll content and leaf dry matter which reached (45.91 CCI ،64.10 %) Respectively. The foliar spray with a concentration of 10g.L1 of licorice extract was significantly higher in all studied parameters (average length of vegetative growth, leaf area, chlorophyll content in leaves, percentage of dry matter in leaves, average fruit size and weight, average number of fruits and TSS% and total yield/tree) which reached (25.57 cm, 7.01 cm2, 44.57 CCI, 62.93%, 202.33 cm3, 396.55 g, 112.79 62 fruit.tree−1, 14.34%, 16.37 kmtree−1). Respectively.

Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.

Application of remote sensing in mapping hydrothermally altered zones in a highly vegetative area - A case study of Lolgorien, Narok County, Kenya

Objective: To map areas of possible hydrothermal alteration using remote sensing technology; To map geological structures controlling mineralisation using remote sensing; To carryout field mapping to ground truth the features identified by remote sensed data. Methods: Landsat 8 Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) remote sensed images were downloaded from USGS website. Landsat images were processed using band ratios, band composites, principal component analysis techniques in ArcGIS to map areas of possible hydrothermal alteration. SRTM image was analysed using hillshade analysis technique in ArcGIS to map geological structural features controlling mineralisation. Findings: The study found that in several areas especially Southern, South Eastern, Central and North Western part of Lolgorien, there is a possibility of hydrothermal alteration as spectral signatures associated with iron oxide and clay minerals were identified. It was also found that areas with possibility of hydrothermal alteration are also associated with relatively large number of lineaments. It was also found that Southern and South-Eastern part of Lolgorien are also associated with numerous artisanal mines proving the fact that indeed gold mineralisation may be found in these places. However, due to thick vegetation cover, mapping of different types of lithological units found in Lolgorien was not possible. Novelty/Application: The application of remote sensed technology helped in identification of new areas of possible mineralisation such as Central and North-Western parts of Lolgorien which despite having similar geological properties (in terms of lineament density and hydrothermal alteration) have never been exploited for gold or other minerals. Keywords: Hydrothermal alteration; Landsat 8 (OLI); SRTM; Iron oxide; clay minerals; remote sensing

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

An early investigation of spatial correlation between Sentinel-2 based rice growth stages maps with satellite-based precipitation data to support digital agriculture development in Indonesia

Sustainability of rice production is a critical issue to ensure food security and needs to be monitored on time. Therefore, the online monitoring system using Sentinel-2 has been introduced to monitor rice fields in Indonesia. However, the system needs to be coupled with precipitation to improve user usage. In this study, the spatial correlation using Pearson’s correlation analysis and linear regression between the floods and the vegetative stages area of the rice monitoring maps and the precipitation data from CHIRPS was investigated. The analysis was conducted with the two datasets with 439 regencies in Indonesia on a monthly basis from December 2019 until May 2020. The result shows that 96 regencies have a highly positive correlation (r>0.6, p>0.05, n=6) with 57 regencies have a high R2 (R 2 >0.6). Also, there are 83 regencies has a profoundly negative correlation (r≤-0.6, p<0.05, n=6) with 32 regencies with high R2 (R 2 >0.6), On the other hand, there are 79 regencies have medium R2 (0.4≤R 2 <0.6), and 271 regencies have the lowest R2 value (R 2 ≤0.4). These early-stage results show an opportunity to combine two datasets to produce early warning systems or recommended cropping calendar in a timely and accurate manner to the stakeholders or the farmers.

A study of thermal controls in rapidly developing city using remotely sensed satellite data: spatiotemporal perspective

The unsusual rise in the land surface temperature is playing a vital role toward the rapid and intense changes in global climate. Occurrence of certain land use land cover and alternative changes in them is the prime cause of bringing extreme changes in temperature. In this research, using 30-year long time series (1988–2018) data from Landsat satellites for understanding relation of mean temperature with the two largest and main controlling land use classes (vegetation and built-up) in a rapidly urbanizing district, Lahore. For this purpose, multivariate statistical approaches of scatter plot and correlation coefficient were employed. Temperatures, vegetative and built-up areas were derived using a combination of different spatiotemporal tools in a specifically designed model. Critical analysis suggests breaking up of investigation timeline in two portions based on changing trend. A 23-year period from 1989 to 2011 (Temporal Window-1) and a 6-year period from 2013 to 2018 (Temporal Window-2) were tested separately for the same arguments. Vegetative area showed an increase throughout the temporal window-1 and then a rapid decrease from 2013 to 2018, while built-up area and mean temperature presented an ever-increasing trend during both temporal windows but with much higher rates in second window. Correlation of temperature with the both the investigated thermal controls has found getting strong in Temporal Window-2 showing that relation of these landcover areas with temeperature is not linear and severity is increasing with time. Moreover, temperature is found to be strongly dependent upon changes in built-up areas than that of vegetative areas. So, an increase in built-up area has much more devastating effects over the temperature rather than decrease in vegetative area. It was concluded that Lahore district is contributing in global warming more rapidly than it had ever done before.

Вплив густоти рослин на ріст і урожайність гібриду помідору індетермінантного типу

Вплив густоти рослин на ріст і урожайність гібриду помідору індетермінантного типу

Automatic Extraction of Tree Crown for the Estimation of Biomass from UAV Imagery Using Neural Networks

The biomass content of vegetation has great significance for efficient balancing of biomass cycle. Estimation of biomass in vegetation requires quantification of tree canopy. Automatic extraction of tree canopy helps in faster and efficient estimation of the biomass content of a vegetative area in comparison to manual mapping methods. An agricultural farm at Papparapati Village in Dharmapuri, District, Tamil Nadu, with an Area of 1.2 sq.km was chosen as study area and multispectral UAV imagery at 4-cm spatial resolution with 4 Bands viz., NIR, Green, Red Edge, RED was acquired. In this study, an attempt has been made to automatically extract tree canopy using simple deep learning convolution neural network (CNN) using training sites as input from the band spectral information. The extracted tree canopy was refined using linear cluster algorithm to delineate the exact tree crown. The biomass of the tree is estimated using the tree properties (crown diameter, height) derived from UAV data and using the allometric equations. A field survey was conducted to validate the classification results achieved from UAV data of individual sample trees. It is observed that the accuracy of extracted tree properties was in agreement with the field measurements of sample trees.

Water requirements of cowpea (Vigna unguiculata (L.) Walp.). calculated with the cultivation coefficient using drainage lysimeter

The objective is to estimate the water needs of the cowpea ( Vigna unguiculata L.) var. INIAP-463 from the culture coefficient using drainage lysimeter. It was carried out in the months of January to April 2018, three plots of 25m2 were implemented, and drainage lysimeters were located in the center of them. The plots were placed near the meteorological station located at the Pontificia Universidad Católica del Ecuador Sede Manabí. The data of the climatological variables were used to estimate the reference evapotranspiration (ETo) by the Penman Monteith method modified by the FAO. Daily monitoring of the drainage sheet of the lysimeter and soil moisture present in it. The irrigation sheet was applied daily, based on the daily ETo and the rainfall of the place. The calculation of the Kc was carried out for each stage of the culture, determining the ratio between the evapotranspiration of the culture (ETc) and ETo. The Kc values ​​for the bean were: first stage with a duration of 15 days a Kc of 0.45 was obtained, second stage 20 days a Kc of 0.88, third stage 25 days a Kc of 1.28, fourth stage 20 days a Kc of 0.70. The water requirement of the cowpea was greater in the third stage with 114.33 mm and the one with the lowest consumption was the first stage with 20.46, the total water need was 254 mm for 80 days after transplantation. The results found allow us to conclude that the water needs of the cowpea var. INIAP-463 were higher in the third stage of cultivation due to the presence of organs of vegetative reproduction and abundant leaf area.

Intraspecific Variations in Functional and Molecular Traits of Near-Endemic Onopordum alexandrinum Boiss. in Natural and Anthropogenic Habitats along the Western Mediterranean Coast of Egypt: Implications for Conservation.

Onopordum alexandrinum is a near-endemic to Egypt and neighboring countries. Its habitats are designated as priority sites for conservation in the south and east Mediterranean regions. We hypothesize that variation in morphological, reproductive, and molecular traits could provide a survival strategy that allows the species to occupy landscapes including anthropogenic habitats (barley and abandoned fields) and natural habitats (sand dunes and desert plateau) with different soil resources along the western Mediterranean coast of Egypt. The results indicated that plant functional traits associated with high rates of resource acquisition and growth (e.g., high values of vegetative height and specific leaf area, and low values of leaf dry matter content) occurred in populations located in abandoned fields with high soil resources. The genetic diversity analyses indicated similarity in genetic diversity of the present populations of O. alexandrinum in barley and abandoned fields with those of sand dunes. However, the genetic structures of these populations were different from those of natural desert plateau, which suggests reduced rates of gene flow. In this framework, it is essential to monitor and reduce the anthropogenic activity which will not only support the conservation of genetic diversity within populations but will also help ensure the resilience of O. alexandrinum in the face of environmental and climatic changes.

Effect of plant bio-regulators on vegetative growth and flowering of gerbera (Gerbera jamesonii B.) cv. Goliath in open field condition

The present investigation entitled “Impact of Plant Bio-regulator on growth and flowering of gerbera (Gerbera jamesonii B.) cv. Goliath” was carried out in premises of Bio-technology cum Tissue Culture Centre, Odisha University of Agriculture and Technology, Bhubaneswar during 2015-16 and 2016-17. The objective of the study was to standardize suitable bio-regulators on flower production in gerbera in open field condition. Apart from control eight treatments of growth regulators were used like GA3 @ 100 ppm, and 150 ppm; Cycocel @ 700 ppm and 800 ppm with or without amino acids as foliar spray. Among vegetative parameters maximum plant spread, leaf length, petiole length, leaf width and leaf area was found in GA3 @ 150 ppm + Amino Acid while maximum leaf number/plant, number of suckers/plant and chlorophyll content in leaf was found in treatment with cycocel @ 700 ppm+ Amino acid. In flowering parameters maximum number of flowers/plant/m2 in a year was found in treatment with cycocel @ 700 ppm + Amino acid.

Spatial-Temporal Analysis of Land Use and Land Cover Change Detection Using Remote Sensing and GIS Techniques

Remote sensing has become a central factor in approaches to managing natural resources and observing environmental fluctuations. Urban development has brought severe losses of farming land, vegetation land and water bodies. Urban sprawl is responsible for a variety of urban environmental issues including reduced air quality, increased local temperature and reduction in water quality. In this study, we have taken the city of Baghdad as a case study and explore the land use and land cover variation that took place over approximately 28 years from 1990 to 2018. Remote sensing practice was implemented to analyse the city of Baghdad’s land cover and land use changes throughout the study period. Landsat TM and OLI 8 images of Baghdad were collected from the USGS Earth Explorer website. Having pre-processed the image, we used supervised classification to categorize the images in different land cover classes. The study region was classified into five categories: urban area, water bodies, vegetative area, barren land and wetland. The accuracy assessment of classification we obtained was 85.11% and 88.14%. From these results, change detection analysis shows that urban area and soil land levels have gone up by 3% and 20%, respectively. In another area the vegetation has diminished, wetlands and water bodies have also decreased by 5%, 17%, and 1% respectively.

Economic analysis of land use changes in forests and rangelands: Developing conservation strategies

Forests and rangelands are economically and environmentally important due to the production of goods and ecosystem services, and any changes in their nature requires a comprehensive evaluation and analysis. The objectives of this study include: a) conducting a cost-benefit analysis of land use change in the forests and rangelands of the Caspian vegetative area with regard to the environmental costs along with other costs and benefits, b) estimating the environmental damage of land use change, and c) developing appropriate strategies for the conservation of the forests and rangelands. The value of costs and benefits of the change was calculated from 2005 to 2016 using the average annual inflation rate up to 2035. The results of the evaluation model show that among the 6 scenarios of land use change, 4 scenarios are completely economically non-viable and two scenarios (forest and rangeland change to residential) are viable with regard to environmental considerations. Moreover, during this period, the change of forest and rangeland for other uses were respectively 43,732 and 33,466 ha. Accordingly, considering the average annual value of hectares of forest and rangeland in terms of producing goods and ecological services, the loss incurred was about 334 and 160 million USD.

Numerical evaluation of urban green space scenarios effects on gaseous air pollutants in Tehran Metropolis based on WRF-Chem model

Although positive environmental aspects of increasing urban vegetative spaces are undeniable, using predefined or unmanaged long-term green programs may cause undesirable and unexpected impacts on the air quality in megacities. In this study, the mitigation potential of three green spaces development scenarios (expansion of surface vegetative area, Green Roof development and combination of surface and roof greenery) on three gaseous air pollutants (O3, SO2 and NO2) concentrations in Tehran Metropolis in an early summertime period is analyzed using coupled Weather Research and Forecasting model with chemistry (WRF/Chem), single-layer urban canopy model (SLUCM) and biogenic emission from Model of Emissions of Gases and Aerosols from Nature (MEGAN). Current results indicate that general patterns of diurnal distribution of SO2 and NO2 are totally controlled by the location of anthropogenic sources of pollutants in the south and the city center, beside the prevailing wind field over the city of Tehran. Ozone distribution follows the diurnal advection from the northern and eastern sub-urban areas and distribution of primary pollutants such as NO2. By conducting green scenarios, reduction in the wind speed, variations in pollutants dry deposition velocities which are directly affected by the boundary layer and surface properties, and pollutants deposition fluxes are observed. Alterations in deposition fluxes are not uniform across the city area and clearly conform to changes in pollutants concentrations. In addition, the shallower (deeper) boundary layer height and the buoyancy forcing due to the reduction (increase) in near-surface air temperature cause less (more) vertical mass transport during the day (night), which in both situations could have negative impacts on the air quality since under lower wind speed condition and less convective upward transfer, polluted air near the surface can be trapped. The defined scenarios have shown different impacts over the city of Tehran with the complex urban morphology so, suggestion of the most efficient summertime green policy with the minimum negative feedbacks is not conceivable.