جستجوی مقالات مرتبط با کلیدواژه « بازتاب طیفی » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Knowledge about soil texture is very important in agricultural studies due to its direct impact on other soil properties. However, determining the soil texture in vast areas requires a lot of time and money. For this reason, researchers are looking for ways to determine this important feature of the soil on a large scale. One of these methods is the use of surface soil spectrometry. In this method, the choice of calibration method significantly affects the accuracy of measuring the characteristics of the surface. In this study, the performance of two regression techniques, namely, partial least-squares regression (PLSR), principal component regression (PCR) were compared to identify the best method to assess sand, silt and clay. For this purpose, 50 soil samples from Tehran province were collected and used as a data set for Calibration and Validation. Soil samples with different moisture levels (oven dry, 5, 10, 15 and 20 w/w) were scanned using a FieldSpec Pro Spectroradiometer with a measurement range of 350–2500 nm. The spectra were subjected to three pre-processed techniques, e.g., Savitzky–Golay (SG) smoothing, first derivative with SG smoothing (FD-SG), Normalization with SG smoothing (Normal-SG). The R2 results from cross-validation indicated that the PLSR model had a better performance than PCR. Normal + SG pre-processing method for clay loam texture and SG method for sandy clay loam texture showed better estimation of measured properties. The amount of R2 for clay was 0.74, 0.81, 0.97 and 0.87, respectively, in moisture content of oven dry, 5, 15 and 20% in   clay loam texture And 0.95 and 0.61 at oven dry and 5% levels in sandy clay loam. Silt was better predicted by R2 0.67 in moisture content of 5% in clay loam texture and R2 0.97 in moisture content of 20% at sandy clay loam texture. Sand was also predicted (R2= 0.86 and 0.72) in moisture content of 5 and 10% in clay loam texture.

This study investigates the efficiency of IRS-p6 LISS-III data to discriminate the vegetative forms in Nojmeh rangeland of Mazandaran. The data of soil surface cover were collected based on dominant plants using quadrate (25×25 m2) and randomized sampling techniques. Then, the canopy cover of each species was extracted for all plants including grasses, forbs, shrubs, bushy trees, as well as the percentage of bare soil and gravel. To correct the LISSIII images, ETM+ fusion image was used as data-base with 18 selected points, extracted with 0.54 pixels RMSE. After that, vegetation indices were calculated from each bands and their digital number (DN) were extracted from the sampling points. The results of data analysis confirmed that among the plant groups (grasses, forbs, shrubs, and bushy trees) only grasses cover and bare soil had significant relationships with LISSIII data. Among the extracted indices of vegetation, the highest correlation belonged to the bare soil with MND (r= 0.41), GI (r=0.40) and OSAVI with grasses cover (r=0.38), respectively.