Relative efficiency of larsys classifiers in soil mapping

Launching of Landsat series and flow of data within and beyond the visual spectrum furnished a potent tool for data acquisition to the earth resources scientists for expanding the teritories of knowledge. Increased capability of computer technology made many advancements possible in the field of Remote Sensing. LARS, Purdue, USA has developed several methodologies for abstracting information from Landsat products in various fields of application. The methods employing algorithms of maximum likelihood and minimum distance have been compared applying the techniques of pooling and deleting of LARS to classify soils of Hapur area, Uttar Pradesh, India. It was found that the maximum likelihood yielded a map with better dispostion of soil-scape but the minimum distance method, by deleting, is seen to be very efficient in class combination and CPU time. The results are discussed in this paper with illustrations.     

Relative performance of landsat MSS,TM and IRS LISS-I data for acreage estimation of Rabi sorghum

Acreage estimation of Rabi sorghum crop in Ahmadnagar, Pune and Solapur districts of central Maharashtra has been attempted using synchronously acquired Landsat MSS and TM data of 1987–88 season and IRS LISS-I data of 1988–89 season; in conjuction with near-synchronous ground truth data. The remote-sensing-based acreage estimations for the districts were compared with the respective estimates by Bureau of Economics and Statistics (BES). As the acreages were underestimated with the classification of standard four-band MSS data, the atmospheric correction of fourband MSS data and normalised differencing (ND) of the atmospheric-corrected MSS data were attempted. The main observations are: (1) the use of Landsat MSS data results in underestimation of sorghum acreage in comparison with BES estimation, (2) the atmospheric correction and ND transformation of MSS data are necessary for bringing acreage estimates in agreement with BES estimates, (3) Mid-IR data in band 1.55 to 1.75 μm are useful in improving the separability of land-use classes, and (4) remote sensing data with radiometric sensitivity comparable to LISS-I or Landsat TM and Signal-to-Noise ratios comparable to LISS-I data are suitable for accurate acreage estimation of sorghum.     

Physiographic analysis for small scale soil and landuse mapping of Mewat area,Haryana, using LANDSAT imagery

Visual interpretation of LANDSAT imagery of 1∶250,000 scale (band 5 and 7) and 1∶1 M (FCC) covering 1611 km² in Mewat area, Haryana was carried out for delineating the physiographic units. The physiographic units viz. hills, piedmont plain, intermontane basin and Yamuna alluvial plain were identified and delineated using interpretation elements. Soils and land use in relation to the physiographic units were studied during the field visit and are described in the paper.     

Interpretation of an apollo space photo for small scale soil mapping in respect of an area in bihar

A section of an Apollo space photo relating to the Magadh area of Bihar state was monoscopically interpreted resulting in six delineations based upon tone and texture variations. Small scale aerial photographs were used for the preparation of soil map of a part of the area using a systematic air photointerpretation procedure; this served as a basis for defining the soil composition of four out of the six space photo analytical units. In respect of the remaining two units-soil information was obtained by reference to an existing small seale soil map of Bihar State. The data thus obtained have been used to prepare a small scale soil map of the selected section of the Apollo space photo. The soil map of the part of the space photo area that is based on support ph: to-interpretation has been found to have the quality and accuracy expected of very small scale soil maps.     

Use of remotely sensed data for soil mapping

Soil maps have been prepared hitherto by conventional ground surveys and by using aerial photographs. In this paper, the use of LANDSAT data for preparation of small scale soil maps upto association of sub-group level has been discussed. Typical spectral curves for various soils and landuse categories were given based on the mean spectral levels for each LANDSAT band obtained from Multispectral Data Analysis System (M-DAS). Soil characterization using the multispectral data could be done by both image oriented and numerically oriented approaches. The small scale soil maps thus prepared by using the satellite data could be used for regional planning and as map base for further detailed surveys.     

Forest cover and landuse mapping in Karbi Anglong and North Cachar Hills districts of assam using landsat MSS data

Karbi Anglong and North Cachar Hills districts of Assam are endowed with rich and diverse vegetation resources. Increased human pressure due to shifting cultivation and raw material extraction for industrial purposes are heavily altering the forested landscape. The present study deals with mapping of forest types in the two districts using LANDSAT-MSS digital data. The maps thus generated provide spatial distribution of bioclimatic vegetation types. Supervised maximum likelihood classification has been performed using training sets collected during field work. The spectral behaviour of vegetation types have been studied for optimising classification scheme. The classification accuracy of classes mapped has been calculated.     

An approach to use of aerial photos in small scale soil mapping based on experience of Jodhpur district,Rajasthan

Photocharacteristics of some of the dominant soils were already known from experience within and outside the survey area. Using these known photocharacteristics an interpretation was carried out enabling a coverage of nearly 65% of the total area of the district. Simultaneously, the remaining 35% area split into various sized patches was photo-analysed and the characteristics noted. Thereafter field traversing was taken to carry out checking of the already interpreted area and interpretation of photo-analytical units and a few uncertain areas. This method of partial interpretation-cum-analysis in the first stage itself was found fully workable for the present area. Interpretations of some of the new dominant analytical units are reported.     

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.     

星地多源数据的区域土壤有机质数字制图

土壤有机质(SOM)是全球碳循环、土壤养分的重要组成部分,精确估算土壤有机质含量具有重要意义。本文以中国东北—华北平原为研究区,收集了1078个土壤样本,以遥感数据(MODIS,TRMM和STRM数据)与土壤地面光谱数据为预测因子,运用基于树形结构的数据挖掘技术构建土壤有机质-环境预测因子模型进行数字土壤制图。通过不同建模样本数建模精度比较,选择300个样本数时的模型为最优模型。建模结果表明土壤光谱和气候因子是研究区SOM变异的主控因子,生物因子次之,而地形因子影响最小。预测结果经检验,RMSE为7.25,R2为0.69,RPD为1.53制图结果与基于第二次全国土壤普查数据的土壤有机质地图具有相似的分布规律,呈现SOM自东北向西南递减的趋势。通过比较分析发现,经过20年左右的土地开发与利用,研究区低SOM和高SOM含量土壤面积减少,而中等SOM含量土壤面积增加。     

利用高分辨率立体测绘影像进行中小比例尺地貌要素更新

利用高分辨率立体测绘卫星遥感影像进行地貌更新,因其覆盖面积大、更新周期短等优势,在中小比例尺地貌要素更新中具有极大的应用潜力.本文分别利用WorldView-2和资源三号卫星影像,探索基于高分辨率立体测绘卫星影像的中小比例尺地貌更新技术方案流程,并在不同的试验区开展试验验证.试验结果表明:本文方法能够满足1:50000...     

Towards decadal soil salinity mapping using Landsat time series data

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985–2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985–2000 (−0.74 g kg⁻¹/10a, p < 0.001), and increased within 2000–2015 (0.79 g kg⁻¹/10a, p < 0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.     

Airborne SAR and Landsat MSS as complementary information source for geological hazard mapping

A comparative study has been made of the usefulness of Landsat and airborne radar images. The study area is situated in the Middle Magdalena Valley of Colombia. It consists of a folded sedimentary sequence of Upper Cretaceous to Lower Tertiary rocks, partially covered by extensive volcanic lahar and alluvial fan material.To obtain the full benefit of the spectral information from Landsat and the textual and pattern information from radar, a combined image was produced using the hue and saturation information from Landsat data and the intensity values from radar data.A clear differentiation between old lahar deposits and the recent one caused by the Nevada del Ruiz eruption of 1985 was possible on SAR images. The synergistic radar imagery, particularly used in stereo, is very useful for prediction of future lahar routes and volcanic risk evaluation.     

Comparison of small scale soil maps prepared by conventional methods and air photo-interpretation procedure

A small scale soil map for a part of Barnagar Tehsil prepared by the adoption of a systematic air photo-interpretation procedure has been compared with the soil map for the same area resulting from a previous reconnaissance soil survey using toposheets as base maps. Tt has been shown that the soil boundaries of the photo-interpretation map are both more accurate and natural in shape; delineation of gullied lands has been achieved more accurately by the A.P.I. procedure; soil erosion could be mapped more consistently and accurately through photo-interpretation. Additionally. the photo-internretation procedure achieves accurate delineation of distinctive facets of land forms because of the advantages arising from the viewing of a stereo model of the landscape; this paves the way for easier and better soil mapping in view of the correlation between landscape elements and soil patterns. Apart from the increased accuracy and diminished costs the photo-interpretation procedure will be helpful in modernising previously prepared reconnaissance soil maps.     

Large area soil moisture estimation and mapping using space-borne multi-frequency passive microwave data

The paper reports the estimation of surface soil moisture (SM) using surface wetness Index (SWI) retrieved from multi-frequency passive microwave radiometer. A change detection algorithm was followed which transforms SWI variations in to SM variations using per pixel soil property of field capacity and air-dry status. Estimated soil moisture was compared with the point measurements made at the Monmouth and De Kalb sites of Illinois (USA) for the validation. Sensitivity of the SWI to the variations of rainfall at various vegetation fractions is analyzed. RMS error of volumetric soil moisture is found to be in the range of 6.35 to 8.85 %. The method works well up to the vegetation fraction of 40 %. Applications of the technique are demonstrated by the spatio-temporal analysis of estimated soil moisture maps for India. Characteristic increase in soil moisture was observed with the progress of monsoon from 25 to 32 week in northern India and 46 to 52 week in the costal parts of Tamil Nadu in south.     

Landsat photo-interpretation for preparation of small scale soil maps through a multistage approach

Landscape units and their sub-divisions were delineated on two LANDSAT photos by visual interpretation on the basis of uniqueness of patterns comprising tone, texture and drainage. Sub-divisions of basalt and granite landscape units could be delineated more precisely with Band 5 photo, while Band 7 photo was found to be superior in respect of the laterite plateau. The soil composition of most of the units was assigned by using data previously collected for the preparation of small scale soil maps adopting a procedure of systematic aerial photo-interpretation with limited selective ground check and authors’ personal knowledge in respect of the remaining units. The resulting soil map has been shown to be superior to existing small scale soil maps for the study area.     

Soil resource survey of silent valley and its environ using landsat MSS data

This paper presents a case study of the utility of Landsat MSS imagery for soil resoruces mapping in Silent Valley and its environs covering about 33,000 sq. km. area. A collective approach involving monoscopic visual interpretation of Landsat imagery in conjunction with the lithological and topographical information supported by limited field check has been followed to prepare a soil map on 1:250,000 scale showing sub-groups/association of sub-groups. Future prospect of using spaceborne data for soil mapping has also been discussed.     

Timing of erosion and satellite data: A multi-resolution approach to soil erosion risk mapping

Erosion reduces soil productivity and causes negative downstream impacts. Erosion processes occur on areas with erodible soils and sloping terrain when high-intensity rainfall coincides with limited vegetation cover. Timing of erosion events has implications on the selection of satellite imagery, used to describe spatial patterns of protective vegetation cover. This study proposes a method for erosion risk mapping with multi-temporal and multi-resolution satellite data. The specific objectives of the study are: (1) to determine when during the year erosion risk is highest using coarse-resolution data, and (2) to assess the optimal timing of available medium-resolution images to spatially represent vegetation cover during the high erosion risk period. Analyses were performed for a 100-km² pasture area in the Brazilian Cerrados. The first objective was studied by qualitatively comparing three-hourly TRMM rainfall estimates with MODIS NDVI time series for one full year (August 2002–August 2003). November and December were identified as the months with highest erosion risk. The second objective was examined with a time series of six available ASTER images acquired in the same year. Persistent cloud cover limited image acquisition during high erosion risk periods. For each ASTER image the NDVI was calculated and classified into five equally sized classes. Low NDVI was related to high erosion risk and vice versa. A DEM was used to set approximately flat zones to very low erosion risk. The six resulting risk maps were compared with erosion features, visually interpreted from a fine-resolution QuickBird image. Results from the October ASTER image gave highest accuracy (84%), showing that erosion risk mapping in the Brazilian Cerrados can best be performed with images acquired shortly before the first erosion events. The presented approach that uses coarse-resolution temporal data for determining erosion periods and medium-resolution data for effective erosion risk mapping is fast and straightforward. It shows good potential for successful application in other areas with high spatial and temporal variability of vegetation cover.     

A numerical technique for delineation of soil mapping units using multi-spectral remote sensing data

A numerical technique for transformation of ground based sail spectral information into soil mapping — unit information, in terms of the total information content index has been proposed. The study carried out on 14 surface soil samples. widely differing in their physical appearance of colour and collected from different parts of India, revealed that total information content index could distinctly discriminate between the contrasting soil physiographic units with black cotton, red and sandy soil types. A comparison of the proposed index with the conventionally used two or three waveband specific indices (e.g. NIR/Red and NIR-Red/Red-Green) showed that the proposed index was more characteristic of the various soil types studied. Further, unlike the conventional 2-D indices, the proposed, numerical technique lead to the complete compression of the information contained in the entire reflectance spectrum (irrespective of the number of wavebands) to a single characteristic value in 1-D Space and a simplified 1-D clustering analysis.     

Colour infrared aerial photographs as data base for reconnaissance soil mapping and land suitability grouping

Colour infrared and panchromatic B & W aerial photographs at 1∶25,000 scale over part of Anantapur district of Andhra Pradesh were interpreted stereoscopically for delineating soilscape units. Soils and land properties were evaluated for suitability of the land for agriculture i.e. paddy and wheat. The results indicate that the relative case in delineating physiographic units offered by colour infrared air-photos does not commensurate with their cost. In the study area, 19.57 and 14.78 per cent area have been found to be suitable for paddy and wheat, respectively.     

Predictive mapping with small field sample data using semi‐supervised machine learning

Existing predictive mapping methods usually require a large number of field samples with good representativeness as input to build reliable predictive models. In mapping practice, however, we often face situations when only small sample data are available. In this article, we present a semi‐supervised machine learning approach for predictive mapping in which the natural aggregation (clustering) patterns of environmental covariate data are used to supplement limited samples in prediction. This approach was applied to two soil mapping case studies. Compared with field sample only approaches (decision trees, logistic regression, and support vector machines), maps using the proposed approach can better capture the spatial variation of soil types and achieve higher accuracy with limited samples. A cross validation shows further that the proposed approach is less sensitive to the specific field sample set used and thus more robust when field sample data are small.