Reza Jafari

EVALUATION OF VEGETATION INDICES FOR ASSESSING VEGETATION COVER IN SOUTHERN ARID LANDS IN SOUTH AUSTRALIA

Vegetation indices are widely used for assessing and monitoring ecological variables such as vegetation cover, above-ground biomass and leaf area index. This study reviewed and evaluated different groups of vegetation indices for estimating vegetation cover in southern rangelands in South Australia. Slope-based, distance-based, orthogonal transformation and plant-water sensitive vegetation indices were calculated from Landsat thematic mapper (TM) image data and compared with vegetation cover estimates at monitoring points made during Pastoral Lease assessments. Relationships between various vegetation indices and vegetation cover were compared using simple linear regression at two different scales: within two contrasting land systems and across broader regional landscapes. Of the vegetation indices evaluated, stress related vegetation indices using red, near-infrared and mid-infrared TM bands consistently showed significant relationships with vegetation cover at both land system and landscape scales. Estimation of vegetation cover was more accurate within land systems than across broader regions. Total perennial and ephemeral plant cover was best predicted within land systems, while combined vegetation, plant litter and soil cryptogam crust cover was best predicted at landscape scale. These results provide a strong foundation for use of vegetation indices as an adjunct to field methods for assessing vegetation cover in southern Australia.

Comparison and evaluation of dust detection algorithms using MODIS Aqua/Terra Level 1B data and MODIS/OMI dust products in the Middle East

Dust storms have a major impact on air quality, economic loss, and human health over large regions of the Middle East. Because of the broad extent of dust storms and also political–security issues in this region, satellite data are an important source of dust detection and mapping. The aim of this study was to compare and evaluate the performance of five main dust detection algorithms, including Ackerman, Miller, normalized difference dust index (NDDI), Roskovensky and Liou, and thermal-infrared dust index (TDI), using MODIS Level 1B and also MODIS Deep Blue AOD and OMI AI products in two dust events originating from Iraq and Saudi Arabia. Overall, results showed that the performance of the algorithms varied from event to event and it was not possible to use the published dust/no-dust thresholds for the algorithms tested in the study area. The MODIS AOD and OMI AI products were very effective for initial dust detection and the AOD and AI images correlated highly with the dust images at provincial scale (p-value <0.001), but the application of these products was limited at local scale due to their poor spatial resolution. Results also indicated that algorithms based on MODIS thermal infrared (TIR) bands or a combination of TIR and reflectance bands were better indicators of dust than reflectance-based ones. Among the TIR- based algorithms, TDI performed the best over water surfaces and dust sources, and accounted for approximately 93% and 90% of variations in the AOD and OMI AI data.

Assessment of PDI, MPDI and TVDI drought indices derived from MODIS Aqua/Terra Level 1B data in natural lands

The present study aimed to evaluate the potential of remote sensing indices including the perpendicular drought index (PDI), modified perpendicular drought index (MPDI) and temperature vegetation dryness index (TVDI) in drought monitoring in the northeast of Iran during 2003–2013. The mentioned indices were extracted from 254 atmospherically corrected MODIS images, and their accuracy was determined against soil moisture data and also standardized precipitation index (SPI) at monthly and yearly scales. The 1-, 3-, 6-, 9- and 12-month SPI was obtained from 42 weather stations. According to the results, although the MODIS-derived drought indices had different efficiencies at different time scales, they could generally better reflect drought conditions at the yearly scale compared to the monthly scale. At the monthly scale, the PDI, MPDI and TVDI had the greatest correlations with the SPI during the 6-, 9- and 3-month periods ending in February, September and April (−0.60, −0.69 and −0.43, respectively). At the yearly scale, the PDI, MPDI and TVDI had the strongest correlation coefficients with the 6-, 6-, 3-month SPI in 2012 (−0.95), 2012 (−0.96) and 2007 (−0.72), respectively. Moreover, the high correlations (>80%) between the MODIS-derived drought indices and soil moisture data reflected their potential in the mapping of soil moisture in the study area. Considering the constant performance of the MPDI in different time scales, this index can be used as a suitable alternative to the SPI in agricultural drought mapping and monitoring in arid and semiarid areas where the SPI faces certain limitations.

Discriminating and monitoring rangeland condition classes with MODIS NDVI and EVI indices in Iranian arid and semi-arid lands

ABSTRACT Monitoring is essential for appropriate rangeland management. The present study aimed to examine the potential of moderate resolution imaging spectroradiometer (MODIS) satellite imagery in rangeland condition assessment and monitoring within and across vegetation types in the arid and semi-arid rangelands of central Iran. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from MODIS Aqua/Terra Level 1B data (related to 2003–2013). The obtained values were compared with vegetation cover measurements and rangeland condition classes at 110 sampling sites using linear regression, one-way analysis of variance (ANOVA), independent-samples t-tests, and Tukey’s pairwise comparisons. The results showed that two indices made stronger predictions of vegetation cover within a vegetation type (R2 > 0.87, P < 0.001) than across vegetation types (R2 > 0.51, P < 0.001). Both NDVI and EVI worked well across vegetation types (P ≤ 0.001) in predicting rangeland condition classes (poor, fair, and good), but their performance varied between vegetation types. The NDVI classified about 73, 19, and 7.5% of the rangelands in poor, fair, and good condition classes, respectively. The good performance of MODIS NDVI index at different landscapes indicates that this index has high potential in detecting vegetation cover and discriminating different condition classes, therefore, it can be used to aid field- based techniques in rangeland condition assessment and monitoring.

Assessing the performance of remotely sensed landscape function indices in semi-arid rangelands of Iran

Appropriate rangeland management requires rangeland function analysis at broad scales. This study aimed to examine the potential of remotely sensed function indices extracted from Landsat data to evaluate the function of semi-arid rangelands in central Iran at the sub-basin scale. Three replicate 30-m transects were randomly placed in the dominant slope direction of 14 selected sub-basins. Various structural properties of vegetation (e.g. number and size of vegetation patches and interpatch lengths) and soil surface were scored based on the landscape function analysis (LFA) procedure. The obtained structural and function indices of the LFA, as well as field percent vegetation cover, were compared with the perpendicular distance vegetation index and remotely sensed function indices including proximity, lacunarity, leakiness index, and weighted mean patch size (WMPS). Remotely sensed function indices were found to be capable of discriminating rangeland landscapes with different conditions. Results showed that the structural properties of vegetation considered in the LFA could also be obtained through WMPS and proximity indices (R >0.76; P < 0.01). All indices, except for lacunarity, had significant correlations with percent vegetation cover and the strongest correlation was observed between WMPS and proximity. Our findings highlight the usefulness and efficiency of function indices derived from satellite data in the estimation of structural and functional properties of rangeland landscapes at the sub-basin scale.

Evaluating the variability of ANPP in central Iranian arid and semi-arid rangelands using CASA model and its relationship with climatic factors

Aboveground Net Primary Production (ANPP) plays an important role in regulating ecological processes and carbon cycle in arid and semi-arid rangelands. Hence, this study aimed to investigate the spatio-temporal variability of rangeland ANPP in seven bioclimatic zones in Isfahan Province using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model fitted with MODIS-NDVI and climatic data during 2000–2016. The model evaluation indicated a good agreement between the estimated and observed ANPP (R2 = 0.917). In concomitant with the precipitation gradient, the mean annual ANPP increased from east towards west and southwest of the region from zero to 160 g C m–2 yr–1. The maximum and minimum ANPP values occurred in humid and cold as well as hyper-arid and warm zone, respectively. The annual temporal variability of ANPP was analyzed in response to climatic factors (precipitation and temperature), Temperature Vegetation Dryness Index (TVDI) and Standard Precipitation Index (SPI) drought indices. The drought was found as the most important factor affecting ANPP. The minimum and maximum ANPP values were observed in 2000 (23.23 g C m–2 yr–1) and 2014 (41.73 g C m–2 yr–1). The maximum ANPP occurred in May and June during which temperature, as an important factor in plant growth, reached its optimum and precipitation affected with a time lag. The annual rangeland ANPP was positively associated with precipitation and negatively with temperature.

Mapping and monitoring of the structure and function of rangeland ecosystems in central Zagros, Iran

This study sought to investigate the feasibility of using field data and remote sensing structural and functional indices in the evaluation and monitoring of semi-steppe rangelands of Isfahan Province, Iran. The study area was first divided into 40 sub-catchments, and rangeland conditions in each sub-catchment were classified into three classes using the four-factor method (FFM). Landsat TM and OLI images for 1987 and 2015 were obtained, and the normalized difference vegetation index (NDVI) was calculated. The structure of the area was evaluated using landscape function analysis (LFA) and rangeland landscape metrics. Rangeland function was also assessed and statistically compared using LFA and the leakiness index (LI). In order to clarify the effects of climate and management on rangeland function, changes in the standardized precipitation index (SPI) were computed and monitored at different intervals. The results indicated the reduction of structural indices, rangeland conditions, and patch sizes over time. Structural metrics suggested the fragmentation of the rangelands with 40–60% canopy cover and the development of rangelands with 0–20%. The structural changes affected rangeland function, and thus reduced the functions of the studied sub-catchments over the 28-year period (p < 0.05). The trend of SPI revealed several periods of drought with different intensities and durations. Reduced precipitation caused structural changes and further decreased function in 2015. According to the obtained results, the combined field-based and remotely sensed approach applied in this research can be used to assess and monitor the functionality and structure of semi-steppe rangeland ecosystems at sub-catchment scale.

SOIL SALINITY MAPPING USING SATELLITE TM AND FIELD DATA IN SOUTHEASTERN ISFAHAN

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 m 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 (R=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.