Journal of Range and Watershed Managment

Abstract Vegetation canopy cover (VCC) and Above-Ground Phytomass (AGP) are the most important indicators of rangeland ecosystem’s structure and function, therefore their accurate evaluation and monitoring is vital for ecosystem welfare. Vegetation indices, are essential tools for assessing and monitoring VCC and AGP which have not been addressed under different managerial conditions, so far. In the present study, the effect of long-term grazing and enclosure (26-year) on the relationship between the VCC and AGP with vegetation indices were evaluated in the Sabzkouh protected area of Chaharmahal‌Va‌Bkhtiari province using Landsat-8 images. VCC and AGP were evaluated in both grazed and enclosed area by 10 plots of 30*30-m in each of which three quadrates of 2×2 square meters were applied (totally 60 quadrates) in the June, 2016. The results indicate significant differences between grazed and enclosed areas in terms of VCC (57% and 46 respectively) and AGP (with 1656 and 1011 kg per hectare, respectively).
The soil adjusted vegetation indices show a more significant prediction of VCC in enclosed and grazed areas (TSAVI1=0.828 and PVI3=0.884, respectively). The PVI2 index showed appropriate results for estimating AGP in both enclosed (R2=0.726) and grazed (R2=0.698) areas. The improved performance of these indies is mainly due to the adjustment of soil effects. Our results suggest that grazing caused a significant effect on the relationship between VCC and AGP with vegetation indices due to feasible changes in vegetation structure or composition. Therefore, using different indices is necessary to study and monitor different rangelands under management strategies.

Abstract Density is an important indicator of vegetation evaluation, which several methods have been developed for its assessment, but their accuracy is concerned. To reveal accuracy of each methods, a study site of 32000-m2 in the steppe rangeland of Marjan, Boroujen was selected and divided into eight macroplots of 4000-m2. Then, the individuals of Astragalus verus and Astragalus albispinus were counted in each microplots. A transects of 100-m established parallel to the length of the macroplots (40 × 100 mm) and density was measured using closest individual, nearest neighbor, random pairs, point-centered quarter, third closest individual, angle order and variable area transect methods, systematic-randomly in 10 sampling points in each macroplots. The results indicate that the real density of A. verus and A. albispinus were 0.1593±0.084 and 0.0622±0.0282/m2, respectively. Closest individual (0.1357±0.1315/m2), nearest neighbor (0.1368±0.1432/m2), point-centered quarter (0.1016±0.1664/m2), random pairs (0.0588±0.0536/m2), third closest individual (0.1107±0.0775/m2) and variable area transect (0.0221±0.0105/m2) for A. verus and angle order (0.0927±0.0523/m2), nearest neighbor (0.0424±0.0357/m2) and third closest individual (0.0524±0.0447/m2) for A. albispinus showed no significant difference with controls. The results revealed that the closest estimation to the controls belongs to the nearest neighbor (-0.141) and third closest individual (-0.0098) for A. verus and A. albispinus, respectively. Moreover, the nearest neighbor (RMSE=0.6877, SE=0.0026 and R=0.1147) and closest individual (RMSE=0.5609, SE=0.0007, R=0.0320) showed the most precise estimation of densities of A. verus and A. albispinus, respectively. Generally, the closest individual for estimating plant density of A. verus and the nearest neighbor's for A. albispinus are proposed.

Abstract One of the important factors threatening forests and rangeland is fires that leads to destruction of a large part of forests and rangelands. Study of this phenomenon and providing management strategies plays a major role to deal with and control such crisis. This study aimed to identify the affecting factors of fire occurrence. The identification of major criteria conducted using a questionnaire as well as gathering information from Natural Resources and Watershed Management Organization in addition to calculation some others. To do so, 3 category of variables including 1- Human factors, 2- Biophysical factors and 3- Instrumental and logistic factors that contained altogether 26 variables are studied. In this research, Geographically Weighted Regression (GWR) method was used for mapping and zonation of the burned areas in the province that were occurred from 2007 to 2013.
Results showed that amongst the 26 studied variables, monthly income (R=-0.61 and VIF=8.08) and number of rangeland and forest guardian members (R=-.56 and VIF=10.81), number of guard stations (R=-0.54 and VIF=2.2), guardsmen’s’ average age (R=0.53 and VIF=9.71 ), average of slope (R=0.5 and VIF=8.99) and number of voluntary rangeland and forest guards (R=-0.42 and VIF=15.11) are respectively the most affective variables on the occurred fires in range and forestlands. Finally, based on extracted predicted map Vardenjan, Mizdej-olia, Poshtkouh Ardal, and Aarmand are the most vulnerable counties for fire incident. Whilst, Monj and Mougooei encountered the least vulnerability of number of fire occurrence that is significantly in line with occurred fires.

Abstract The land use of the watersheds is one of the most affected and highly vulnerable due to developmental process which effect on the other variables such as the hydrological function. The purpose of this research is to monitor land use changes in the past and to investigate predictability of its future using Land Change Modeler (LCM) in the watershed of Farsan County of Chaharmahal-va-Bakhtiari province. For this purpose, the Landsat-5 TM images of 1986 and 2009 as well as the Landsat-8 OLI images of 2017 were analyzed. Land covers including residential areas, agricultural land, dryland farming, rangelands, rocks, water bodies, bare-land and snow were classified for the three periods. The prediction of land cover of 2017 was done using the LCM model based on Artificial Neural Network and Markov chain analysis after assessing model’s accuracy based on Kappa index. The land cover of 2027 was also predicted using a change probability table extracted from occurred changes from 1986-2017. The results show that the rangeland decreased by 4379-ha in the years 1986 to 2017, but the agricultural land increased by 1922-ha. This study proved that the LCM could accurately forecast future changes (85% overall accuracy). An increase of 149-ha of residential area and 100-ha decrease of rangelands in the study area was predicted for 2027. Therefore, while emphasizing the conservation of rangelands, it is necessary to study and use this technique to predict changes, its causes, as well as the consequences of land use changes at the broader scales.

Abstract Land cover mapping is essential for natural resource management. Satellite imagery can be used for mapping land cover. Several methods are available for land cover mapping whilst choosing the best method is one of the most important issue in this context. To compare pros and cons of three methods of classification including maximum likelihood, object-based segmentation and artificial neural network, first, the efficiency of these three methods were evaluated. Then the trend of land cover changes in Shahrekord basin was investigated for 1999, 2009 and 2015 using Landsat images of TM, ETM+ and OLI sensors, respectively. After geometric and radiometric corrections, the land cover map of 2015 was prepared based on the three methods. The results of the validation mapping methods revealed that object-based method was more promising than the others with 93 and 90% for total accuracy and Kappa coefficients of agreement, respectively. So, the object-based segmentation method is recommended for monitoring of land cover changes. The results of the land cover change indicated that residential areas increased from 1.727% in 1999 to 2.98% in 2015 and agricultural lands increased from 5.73% to 12.60% but rangelands were decreased by 9.05 in total. Moreover, bare-lands were increased from 1999 to 2009 by 6.19% but decreased from 2009 to 2015 by 5.27%. The result of this study showed that the object-based method is superior to pixel based method of Maximum-liklihood and neural network. So, object-based segmentation is recommended for estimating land cover changes.

Abstract Wetland meadows as a natural ecosystems plays an important role on sustainability of nature, although are enormously under drainage and changing in the recent years. Shahrekord meadow, which is located adjacent to the city, considered as a natural heritage due to its contribution to tourist visiting, balancing weather in addition to supplying forage for animals. However, this meadow is declining due to anthropogenic effects that this article aims at studying and evaluation of its change and prediction of future condition using Landsat TM5, ETM+7 and OLI/TIRS. To do so, first of all the images of 1987, 1994, 2001, 2010 and 2016 were gathered, then radiometric and geometrically were evaluated. After that, landuse/landcover of the study area was depicted using a maximum likelihood method in TerrSet (Ver. 18.31). Afterward, change detection of the study area was done using a cross-tabulation method and the future condition was predicted using a CA-Markov model. Results indicated that a significant change was occurred in this study area whereas in 1987 whole of the study area was covered by meadowland but land cover changes altered this valuable ecosystem to constructed area (3.33%), arable land (25.02%) and airport (19.65%) in 2016. Results of change prediction also depicted that 5.08% of the study area will be converted to other land cover in 2026. Therefore, we recommend that land use and land cover of this valuable ecosystem should be conserved due to the function and services that this meadowland offered.

Abstract The evaluation of utilization of key species is cornerstone of decision-making in rangeland management. Measuring utilization is essential for regulating grazing intensity, grazing pressure and distribution of animals. utilization of two key grass species of Dactylis glomerata and Bromus tomentosus, as two important rangeland species is determined using height-weight relationship. The research is done in two vegetative periods of prior to flowering and flowering stages in two region of grazing land and exclosure by 32 transects of 100-meter length. To do so, the height of species was measured and cut at 1-cm above soil surface and weighted freshly. The samples are clumped and moved to the laboratory and oven dried at 65°c after separating litter divisions Then, the whole individual plants were weighted and cut in 5-cm intervals and each part was re-weighted and recorded separately for each species. Height-weight relationships were analyzed by different regression models in SPSS v.18 software.
Result shows that sigmoid model significantly illustrate the height-weight relationship of both species of Dactylis glomerata and Bromus tomentosus with R2=0.994 and 0.997 at prior to flowering stage in the exclouser respectively. Similarly, sigmoid model also elucidate the height-weight relationship of both species of Dactylis glomerata and Bromus tomentosus with R2=0.975 and 0.998 at the flowering stage in the exclouser, respectively. The sigmoid model also best fitted for depicting height-weight relationship of both species at prior to flowering stage in grazing land for both species with R2=0.996, too.

Abstract In this study, three methods were evaluated for vegetation mapping. For remote sensing method, in addition to IRS data of LISSIII, Ddigital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) were used for classification of 14 classes of land covers mostly vegetation types using a maximum likelihood algorithm. After comparing of produced vegetation maps, overall accuracy and Kappa index were 82% and 79.43% respectively when only the IRS were used. Whereas, the overall accuracy and Kappa index were increased to 93% and 90.63% respectively, when ancillary data of DEM and NDVI were added.
Slope, slope direction, elevation above sea level, annual precipitation, temperature, and sun radiation were selected as the main physiographic after a broad literature review. Then the relationship between of these six factors with vegetation types was evaluated. so a multivariate logistic regression was used to draw vegetation map of the study area based on the sixth independent variables. The result showed a predicted vegetation map of 47.08% accuracy.Finally, in the morphological method, relationship between three maps of lithology, undulating form of geomorphology and faces with vegetation/land cover were determined using a neural network synthetic approach and predict vegetation map was drawn as the output. The accuracy of resulted map was 39.1%. Comparison of accuracy of vegetation mapping by three methods of RS, physiographic and geomorphological methods revealed that RS method of vegetation/land cover mapping is significantly promising due to a meaningfully higher accuracy even without using ancillary data such as DEM and NDVI in this method.

Abstract Canopy cover and forage production have always been two important indicators in rangeland assessment, which sometimes are applied as surrogates of each other. These two indicators are widely used in rangeland studies and have a vital role in evaluation of rangeland structure and functions. Occasionally, different sampling groups (estimators) evaluate forage production and canopy cover of different spaces and times. This research was aimed at investigation of different sampling groups and life-forms' effects on relationship between canopy cover and forage yield estimation. To do so, the impact of three sampling groups and five life forms (Fixed factors) on estimation of relationship between canopy cover (covariate) and forage yield (dependent variable) in a full factorial model in rangeland of Chahrtagh of Naghan, Chahrmhal-va-Bakhtiari Province, was estimated. Results shows that predictor variable of canopy cover is a god surrogate for forage production (P≤0.05) of different life-forms, but different sampling groups significantly (P≤0.05) effects on relationship between canopy cover and forage production estimation. Nevertheless, different life-forms do not significantly (P≤0.05) influence estimation of canopy cover and forage production relationship. By the way, interaction between sampling group and life forms considerably (P≤0.05) affects the relation. Therefore, we conclude that, although, the canopy cover is a good predictor of forage production, nonetheless, different sampling groups should not be engaged in sampling and monitoring vegetation cover and forage production estimation, specifically, if estimation of different life-forms' production is intended.

Abstract Determination of rangeland species palatability has some functions such as defining the rangeland grazing capacity and plant composition estimation. Despite the importance of palatability in rangeland management, no appropriate palatability indicator has been defined yet. Therefore, developing an accurate, applicable, general, and simple indicator for plant palatability estimation seems crucial. This research is an effort in this regard. To investigate the relationship between plant secondary compounds and palatability, plant species composition in the study area and in the sheep and goat diet as well as selection index were measured using chronometric and filming method. In the next stage, main plants’ secondary compounds in livestock diet were determined using GC/MSS. Then plants’ secondary compounds were ordinated using principle component analysis (PCA) method. Quantitative value of each plant species Eigen values on each of the main axis of PCA was regarded as a criterion for differentiation of plant species based on its secondary compounds. Finally, correlation between selection index of each plant species by sheep and goat (as dependent variable) with each plant species Eigen values on PCA axis (as independent variable) was determined. The results showed that there is a significant negative relationship between the selection index of the species by sheep and goat with its secondary compounds (P≤0.05). So, it is concluded that secondary compounds are effective factors in animal’s diet selection for grazing. Therefore, secondary compounds are recommended as an important factor for plant palatability determination.

Abstract The analysis of the relationship between spatial distribution of environmental factors and
vegetation types is crucial for understanding mountainous ecosystems. In this research a
GIS based approach was used to produce a vegetation map for Sabzkouh protected area in
the Chaharmahal-Va-Bakhtiari province. To identify environmental parameters affecting the
vegetation cover, 6 primary and secondary environmental parameters including hypsometric,
slope steepness, slope direction, annual precipitation, temperature and sun radiation maps were
derived from the study area DEM. To investigate the relationship between these factors and
the spatial distribution of vegetation cover, quantitative analyses using statistical techniques
like Principal Components Analysis(PCA) were undertaken. Then, the spatial distribution
of vegetation types was predicted using a multi-logistic regression. Results showed that
topographic variables derived from the DEM were very useful for indicating habitats of
range and forest types. Although lack of information on the anthropogenic effects led to some
uncertainties in the interpretation of spatial pattern of vegetation types, the topographic and
climatic variables, derived from the DEM, were considerably effective in modelling the spatial
distribution of vegetation types.