Determining iron content in Mediterranean soils in partly vegetated areas,using spectral reflectance and imaging spectroscopy

The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho Mountain complex in Southern Spain using imaging spectroscopy is investigated. Laboratory, field and airborne spectrometer (ROSIS) data are acquired, in combination with soil samples, which are analysed for dithionite extractable iron (Fe_d) content. Analysis of the properties of two iron related absorption features present in laboratory spectra demonstrates good relations, especially between the standard deviation (S.D.) of the values in an absorption feature and the Fe_d content (R² = 0.67) as well as the ratio based Redness Index (R² = 0.51). Such derived relations are less strong for the ROSIS data (R² for S.D. = 0.26 and R² for Redness Index = 0.22). The spatial distribution of iron in vegetated areas shows a strong sensitivity of these relations with the presence of vegetation. A combination of both methods shows that the overestimation of the Fe_d content with the one method is (partly) compensated by the underestimation with the other method.     

Effect of a photosynthesis inhibitor on the spectral reflectance of rice leaves

A laboratory study of evaluation of suitable spectral wavelengths for remote detection of damaged rice crop is presented. Causes for the spectral changes associated with a stress/damage are discussed.     

Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area

The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1 m) and multispectral component (4 m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images.The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45–55% burned area and 45–55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR.     

Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, R_v² = 0.54; root mean square error in validation, RMSE_v = 12.99 mg kg⁻¹; and residual prediction deviation in validation, RPD_v = 1.35) was slightly better than using the soil spectra (R_v² = 0.42, RMSE_v = 13.35 mg kg⁻¹, and RPD_v = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (R_v² = 0.63, RMSE_v = 11.94 mg kg⁻¹, RPD_v = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra.     

Derivation of an urban materials spectral library through emittance and reflectance spectroscopy

Recent advances in thermal infrared remote sensing include the increased availability of airborne hyperspectral imagers (such as the Hyperspectral Thermal Emission Spectrometer, HyTES, or the Telops HyperCam and the Specim aisaOWL), and it is planned that an increased number spectral bands in the long-wave infrared (LWIR) region will soon be measured from space at reasonably high spatial resolution (by imagers such as HyspIRI). Detailed LWIR emissivity spectra are required to best interpret the observations from such systems. This includes the highly heterogeneous urban environment, whose construction materials are not yet particularly well represented in spectral libraries. Here, we present a new online spectral library of urban construction materials including LWIR emissivity spectra of 74 samples of impervious surfaces derived using measurements made by a portable Fourier Transform InfraRed (FTIR) spectrometer. FTIR emissivity measurements need to be carefully made, else they are prone to a series of errors relating to instrumental setup and radiometric calibration, which here relies on external blackbody sources. The performance of the laboratory-based emissivity measurement approach applied here, that in future can also be deployed in the field (e.g. to examine urban materials in situ), is evaluated herein. Our spectral library also contains matching short-wave (VIS–SWIR) reflectance spectra observed for each urban sample. This allows us to examine which characteristic (LWIR and) spectral signatures may in future best allow for the identification and discrimination of the various urban construction materials, that often overlap with respect to their chemical/mineralogical constituents. Hyperspectral or even strongly multi-spectral LWIR information appears especially useful, given that many urban materials are composed of minerals exhibiting notable reststrahlen/absorption effects in this spectral region. The final spectra and interpretations are included in the London Urban Micromet data Archive (LUMA; http://LondonClimate.info/LUMA/SLUM.html).     

Retrieval of leaf area index in alpine wetlands using a two-layer canopy reflectance model

In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρ_nir–LAI and ρ_red–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R²) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R² reached 0.82 and RMSD was 0.25 for early July 2011.     

Remote detection of fluid-related diagenetic mineralogical variations in the Wingate Sandstone at different spatial and spectral resolutions

Well-exposed eolian units of the Jurassic system on the Colorado Plateau including the Wingate Sandstone, show prominent color variations throughout southeastern Utah due to diagenetic changes that include precipitation and/or removal of iron oxide, clay, and carbonate cement. Spatially variable characteristic diagenetic changes suggest fluid-rock interactions through the sandstone. Distinctive spectral signatures of diagenetic minerals can be used to map diagenetic mineral variability and possibly fluid-flow pathways. The main objective of this work was to identify characteristic diagenetic minerals, and map their spatial variability from regional to outcrop scale in Wingate Sandstone exposures of Lisbon Valley, Utah. Laboratory reflectance spectroscopy analysis of the samples facilitated identification of diagnostic spectral characteristics of the common diagenetic minerals and their relative abundances between altered and unaltered Wingate Sandstone. Comparison of reflectance spectroscopy with satellite, airborne, and ground-based imaging spectroscopy data provided a method for mapping and evaluating spatial variations of diagenetic minerals. The Feature-oriented Principal Component Selection method was used on Advanced Spaceborne Thermal Emission and Reflection Radiometer data so as to map common mineral groups throughout the broader Wingate Sandstone exposure in the area. The Minimum Noise Fraction and Spectral Angle Mapper methods were applied on airborne HyMap and ground-based hyperspectral imaging data to identify and map mineralogical changes. The satellite and airborne data showed that out of 25.55 km² total exposure of Wingate Sandstone in Lisbon Valley, unaltered sandstone cover 12.55 km², and altered sandstone cover 8.90 km² in the northwest flank and 5.09 km² in the southern flank of the anticline. The ground-based hyperspectral data demonstrated the ability to identify and map mineral assemblages with two-dimensional lateral continuity on near-vertical rock faces. The results showed that 39.71% of the scanned outcrop is bleached and 20.60% is unbleached while 6.33% remain unclassified, and 33.36% is masked-out as vegetation. The bleached and unbleached areas are alternating throughout the vertical face of the outcrop. The relative hematite abundance observed in the unbleached areas are somewhat symmetrical. This indicates fairly similar reaction intensities along the upper and lower reaction fronts observed in the vertical section. The distribution geometry and relative abundances of diagenetic minerals not only suggest multiple paths of fluid-flow in Wingate Sandstone but also provides some insight about relative direction of past fluid-flow.     

Differentiation of oil seed crops by remote sensing based on their spectral reflectance pattern

The feasibility of differentiating four oil-seed crops viz., mustard, toria, yellow sarson and sunflower, based on their spectral reflectance in the visible and near infra red region was studied in a field experiment, The spectral vegetative index profiles, generated during the growth period of different oil seed crops indicated two vegetative growth peaks and a depression between the two peaks, due to the conspicuous yellow colour of flowers, which masked the green leaves. The magnitude of such depression in the spectral vegetation indices viz., ‘Greenness’ and ‘Perpendicular Vegetation Index’ (PVI), were of higher magnitude in yellow sarson. The flowering period parameters viz., flowering time, duration and intensity, deduced from the spectral vegetation indices were found to be beneficial in differentiating different oil-seed crops by remote sensing. A plot of ‘Brightness’ vs. ‘Greenness’ values determined during the growth of the crops formed typical clusters. The cluster representing toria crop was significantly different from the other crops, thereby making toria identifiable from others by remote sensing.     

Spectral reflectance characteristics of soils and its correlation with soil properties and surface conditions

In-situ spectral reflectance of soils was measured at various test sites of India in four spectral bands within the visible and near-infrared wavelength comparable to Landsat Multispectral Scanner (MSS). Reflectance behaviour of soils under different field conditions was analysed and the spectral reflectance curves for different soil types were obtained. Soil samples pertaining to each test site were analysed for mechanical composition, physioco-chemical properties to identify their relationship with soil reflectance. These spectral reflectance curves were further examined as to their usefulness in discriminating various soil types. Five distinct soil types namely, Black cotton soils (Typic Pellusterts), Marine Soils (Typic Halaquepts), Lateritic Soils (Plinthic Tropohumults), Alluvial Soils (Typic Ustochrepts), Coastal Sandy Soils (Typic Psammaquents), were discriminated on the basis of significant relationships between the spectral reflectance data and soil properties.     

Variations in soil spectral reflectance related to soil moisture,organic matter and particle size

Spectral reflectance measurements in the visible and near infrared wavelengths of alluvial, black cotton and lateritic soils under different conditions show that reflectance has negative association withsoil moisture and organic matter in all the three soils. In lateritic soils reflectance increases with decrease of particle size. Variations in reflectance due to changes in concentrations of parameters were generally restricted to certain concentration levels. The generally superior discriminant capability of band 4 (0.8 to 1.1 urn) is indicative of its utility in soil and soil characteristics mapping.     

Spectral reflectance of some typical Indian soils as affected by tillage and cover types

Measuring spectral reflectance of soils in situ which simulates measurements made from aircraft and by satellite scanner system has become an integral part of soil mapping using remote sensing techniques. A preliminary study has been conducted to measure the spectral reflectance of some typical red and black soils of India using a field radiometer (EXOTECH-100-A) and to study the changes in specrtral reflectance patterns due to tillage and crop and non-crop cover, The spectral reflectance were measured in four different pands of electromagnetic spectrum—two in visible (0.5-0.6μ and 0.6-0.7μ) and two in infrared (0.7-0.8μ and 0.8-1.0μ) region. Spectral reflectance curves were drawn from these values which helped in understanding the spectral separability and mixing of various red and black soil types. Black soils having grass cover showed maximum reflectance value followed by ploughed one and bare counterparts whereas, the order of decrease in spectral reflectance of red soils was bare soils> ploughed soil> soils with grass cover.     

Effects of solar and view angles on reflectance for paddy field canopies

It is very important to know the spectral characteristics for the sake of understanding the remote sensing data. The reflectance characteristics of paddy field canopies vary with time or observational conditions (solar zenith angle, solar azimuth angle, and view zenith angle). A number of field studies have clarified the effects of these conditions on grain canopy reflectance. Most of the field data used in these study, however, were conducted only through the growing season in one year or by grains planted in pots. A series of authors’ experiments were initiated in 1982 and continued from the spring to the autumn every year to 1987. In this study we describe that the remotely sensed spectral data measured on the ground are influenced not only by the grain type, observational conditions, and growing season but also by the solar zenith angle, solar azimuth angle and view zenith angle in relation to scene. In this paper we report the results from the investigation of these various fundamental properties.     

Mapping forest leaf area index using reflectance and textural information derived from WorldView-2 imagery in a mixed natural forest area in Florida,US

The leaf area index (LAI) of plant canopies is an important structural parameter that controls energy, water, and gas exchanges of plant ecosystems. Remote sensing techniques may offer an alternative for measuring and mapping forest LAI at a landscape scale. Given the characteristics of high spatial/spectral resolution of the WorldView-2 (WV2) sensor, it is of significance that the textural information extracted from WV2 multispectral (MS) bands will be first time used in estimating and mapping forest LAI. In this study, LAI mapping accuracies would be compared from (a) spatial resolutions between 2-m WV2 MS data and 30-m Landsat TM imagery, (b) the nature of variables between spectrum-based features and texture-based features, and (c) sensors between TM and WV2. Therefore spectral/textural features (SFs) were first selected and tested; then a canonical correlation analysis was performed with different data sets of SFs and LAI measurement; and finally linear regression models were used to predict and map forest LAI with canonical variables calculated from image data. The experimental results demonstrate that for estimating and mapping forest LAI, (i) using high resolution data (WV2) is better than using relatively low resolution data (TM); (ii) extracted from the same WV2 data, texture-based features have higher capability than that of spectrum-based features; (iii) a combination of spectrum-based features with texture-based features could lead to even higher accuracy of mapping forest LAI than their either one separately; and (iv) WV2 sensor outperforms TM sensor significantly. However, we need to address the possible overfitting phenomenon that might be brought in by using more input variables to develop models. In addition, the experimental results also indicate that the red-edge band in WV2 was the worst on estimating LAI among WV2 MS bands and the WV2 MS bands in the visible range had a much higher correlation with ground measured LAI than that red-edge and NIR bands did.     

Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance

The objective of this research is to select the most sensitive wavelengths for the discrimination of the imperceptible spectral variations of paddy rice under different cultivation conditions. The paddy rice was cultivated under four different nitrogen cultivation levels and three water irrigation levels. There are 2151 hyperspectral wavelengths available, both in hyperspectral reflectance and energy space transformed spectral data. Based on these two data sets, the principal component analysis (PCA) and band-band correlation methods were used to select significant wavelengths with no reference to leaf biochemical properties, while the partial least squares (PLS) method assessed the contribution of each narrow band to leaf biochemical content associated with each loading weight across the nitrogen and water stresses. Moreover, several significant narrow bands and other broad bands were selected to establish eight kinds of wavelength (broad-band) combinations, focusing on comparing the performance of the narrow-band combinations instead of broad-band combinations for rice supervising applications. Finally, to investigate the capability of the selected wavelengths to diagnose the stress conditions across the different cultivation levels, four selected narrow bands (552, 675, 705 and 776 nm) were calculated and compared between nitrogen-stressed and non-stressed rice leaves using linear discriminant analysis (LDA). Also, wavelengths of 1158, 1378 and 1965 nm were identified as the most useful bands to diagnose the stress condition across three irrigation levels. Results indicated that good discrimination was achieved. Overall, the narrow bands based on hyperspectral reflectance data appear to have great potential for discriminating rice of differing cultivation conditions and for detecting stress in rice vegetation; these selected wavelengths also have great potential use for the designing of future sensors.     

高光谱目标探测中的空间和光谱尺度效应

高光谱遥感目标探测主要利用目标和背景的光谱特征差异进行目标识别。一般情况下,影像的空间和光谱分辨率越高,探测效果越好。但多数情况下空间和光谱分辨率难以同时满足需求。针对该问题,本文利用Field Imaging Spectrometer System(FISS)地面高光谱成像仪器,通过在稀疏草地上布设人工绿色目标,研究了目标和背景光谱相似情况下,单一均匀背景下小目标探测问题,提出空间和光谱尺度定量分析方法,得到目标探测适用的空间和光谱尺度。结果表明:(1)利用FISS高光谱仪器进行人工目标探测,所需的空间分辨率约为目标尺寸的2倍以内;(2)当光谱分辨率优于40 nm时,目标和背景的两个主要特征:反射峰的位置和波段趋势差异均可被描述,在原始空间分辨率5倍(0.85 cm)以内,探测精度可以达到0.94以上。由于反射峰间距20 nm,当光谱分辨率低于40 nm时,该特征消失,造成探测精度的下降;(3)当光谱分辨率低于40 nm时,选取目标、背景光谱特征差异较大的波段可提高探测的有效性,在舍弃目标背景相似波段后,探测精度上升,得到本实验的最佳波段组合为红、绿、蓝、黄及红边波段。     

Sensitivity of narrowband vegetation indices to boreal forest LAI,reflectance seasonality and species composition

There is growing evidence that imaging spectroscopy could improve the accuracy of satellite-based retrievals of vegetation attributes, such as leaf area index (LAI) and biomass. In this study, we evaluated narrowband vegetation indices (VIs) for estimating overstory effective LAI (LAI_eff) in a southern boreal forest area for the period between the end of snowmelt and maximum LAI using three Hyperion images and concurrent field measurements. We compared the performance of narrowband VIs with two SPOT HRVIR images, which closely corresponded to the imaging dates of the Hyperion data, and with synthetic broadband VIs computed from Hyperion images. According to the results, narrowband VIs based on near infrared (NIR) bands, and NIR and shortwave infrared (SWIR) bands showed the strongest linear relationships with LAI_eff over its typical range of variation and for the studied period of the snow-free season. The relationships were not dependent on dominant tree species (coniferous vs. broadleaved), which is an advantage in heterogeneous boreal forest landscapes. The best VIs, particularly those based on NIR spectral bands close to the 1200 nm liquid water absorption feature, provided a clear improvement over the best broadband VIs.     

Yield estimation of summer crops grown in a mixed cropping area,a spectral growth curve profile approach

Estimation of crop production in advance of the harvest has been an intensively researched field in agriculture. Spectral parameters derived from the spectral growth profile being indicator of growth and development characteristics of the crop have a direct utility in crop-yield modeling. The present study is undertaken in a mixed cropping area of Karveer taluka, Kolhapur district, Maharashtra, to assess feasibility of multi-date moderately coarse WiFS data in developing spectral growth curves following Badhwar model (1980) for summer groundnut and paddy. The analysis highlighted potential of moderately coarse resolution WiFS data in discriminating the crops grown in fragmented conditions, provided detailed and adequate ground truth is used. The regression models using spectral parameters explained 94 % variation in paddy yield. However, model using ground information as peak LAI in addition to spectral variables, could explain 91 % variation in groundnut yield; thus for prediction of low-yielding and poorly managed crop a convergent model is essential. Vegetative growth rate during the pre-heading phase and total growing season absorbed photosynthetically active radiation (APAR) indicated by the area under the curve are the main predictors.     

Retrieval of wheat leaf area index using price approach based on inversion of canopy reflectance model

This paper reports estimation of the Leaf Area Index (LAI) of wheat crop from IRS-LISS-III data using Price (1993) approach. Empirical approach for LAI estimation with different NDVI estimation procedures viz. radiance, apparent reflectance and dark object subtraction (DOS) based atmospheric correction were also evaluated. Validation of LAI retrieval and NDVI normalizations were carried out using field level measurements of crop LAI and spectral property using canopy analyzer and spectro-radiometer, respectively over selected fields in Bhopal District, Madhya Pradesh. It was observed that empirical relations are sensitive to the NDVI estimation approach and DOS method performed better as compared to other two approaches. It was also observed that LAI estimation from Price algorithm is sensitive to the crop attenuation coefficients. Crop specific attenuation coefficients reported in literature for Indian cultivars gave higher accuracy. The root mean square (RMS) error of 0.77 for LAI estimation was achieved using above described approach.     

Mapping of waterlogged areas and salt affected soils in the IGNP command area

Visual interpretation of IRS LISS-II data authenticated by ground truth was carried out for detection of waterlogged areas and characterization of salt-affected soils. The deep blue tone depicting surface ponding (stagnant ponded zone) resulted from the seepage and accumulation of irrigation water through the course sandy mass. Such unit was mostly confined to the localized low-lying areas. These data have also revealed interdunal seepage lake within the buried paleo-channel of Saraswati possibly due to submerging of excess Ghaggar floodwater. Flood irrigation, sandy soils, cultivation of high water requirement crops and presence of hard gypsiferrous pans in the shallow depths were responsible for development of waterlogged conditions in the area. The grey to yellowish white patch around the waterlogging features represented surface salt efflorescence. The grey to greyish red represented the potential waterlogging zone. Based on the analytical data, soils were characterized as moderate to highly saline and showed the presence of significant amount of CaCO₃ (>2 mm) throughout the solum. The chemical analysis of water samples revealed the presence of high to very high quantity of soluble salts dominated by chlorides and sulfates of sodium, calcium and magnesium.     

Physiography and soils in an area of miocene clays,merida, spain

Studies were undertaken to study physiography and soils in Merida area of Spain with the help of aerial photographs in order to make morphogenetic interpretation of the landscape for a clearer understanding of the distributional pattern of soils. Physiography of the area presents a picture of flat to convex low table lands in step like succession. These were termed as mesitas. The mesitas do not slope regularly from summit to the valley bottom, but show at least three to four changes in the gradient. The diflerent physiographic units and associated soils are discussed. The morphometry of the landscape, especially sequential occurrence of mesitas in step like succession, asymmetric valleys, presence of promontories on lower slopes transverse to the general gradient, idealised alterations of petrocalcic and calcic horizons from mesita summit to valley bottom indicated a major physiographic process of mass movement in the form of rotational slips. The distributional pattern of the soil is explainable on this basis. From the logical sequence of climatic changes and the geologic history of the area, it is believed that after the large scale sedimentation during Miocene period, followed by large scale climatic changes and accompanied by tectonic activities the process of mass moveme. it http://moveme.it could have started sometime in Pliocene.