Topographic Correction-Based Retrieval of Leaf Area Index in Mountain Areas

Leaf Area Index(LAI)is a key parameter in vegetation analysis and management,especially for mountain areas.The accurate retrieval of LAI based on remote sensing data is very necessary.In a study at the Dayekou forest center in Heihe watershed of Gansu Province,we determined the LAI based on topographic corrections of a SPOT-5.The large variation in the mountain terrain required preprocessing of the SPOT-5 image,except when orthorectification, radiation calibration and atmospheric correction were used.These required acquisition of surface reflectance and several vegetation indexes and linkage to field measured LAI values.Statistical regression models were used to link LAI and vegetation indexes.The quadratic polynomial model between LAI and SAVI (L=0.35)was determined as the optimal model considering the R and R2 value.A second group of LAI data were reserved to validate the retrieval result.The model was applied to create a distribution map of LAI in the area.Comparison with an uncorrected SPOT-5 image showed that topographic correction is necessary for determination of LAI in mountain areas.

EFFICIENT TOPOGRAPHIC CORRECTIONS FOR RESISTIVITY DATA

The development of topography effect correction on resistivity data was summed up.Mainly the correction technique based on angular domain superposition and ratio method was discussed to solve point source and 2-D topography problem.The nature of ratio topography correction was proved and the results were spread to point source.Based on systematically studying anomalies of angular domains with different angles,a fast method of topography correction was presented which can be run on personal computer and get resonable accuracy.

TOPOGRAPHIC CORRECTION OF ETM IMAGES BASED ON SMOOTHED TERRAIN

A new empirical topographic correction method is proposed in this paper. The main idea of the new method is smoothing the slope angle of terrain in the first place and then performing the cosine correction based on the smoothed terrain. A comparison is conducted among the new method and several other common methods using Landsat-7 ETM+ data. Visual analysis and statistical analysis are adopted to assess the performance of these methods from two aspects: overcorrection, homogeneity within a land cover class. Comparison results indicate that the new method is superior to the cosine correction, Gamma correction, Sun-Canopy-Sensor correction, and Minnaert correction. Compared with common methods, the proposed one can eliminate overcorrection better and is an effective topographic correction method.

Assessment of different topographic correction methods in ALOS AVNIR-2 data over a forest area

Because the removal of topographic effects is one the most important preprocessing steps when extracting information from satellite images in digital Earth applications,the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years.As there is no superior topographic correction method applicable to all areas and all images,a comparison of topographic normalization methods in different regions and images is necessary.In this study,common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area,and the results were evaluated through different criteria.The results show that the simple correction methods[Cosine,Sun-Canopy-sensor(SCS),and Minnaert correction]are inefficient in exceptionally rough forests.Among the improved correction methods(SCSC,modified Minnaert,and pixel-based Minnaert),the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used.Thus,this method should be considered for topographic correction,especially in forests with severe topography.

A correction technique for false topographic perception of remote-sensing images based on an inverse topographic correction techniqu

The false topographic perception phenomenon(FTPP)refers to the visual misperception in remote-sensing images that certain types of terrains are visually interpreted as other types in rugged lands,for example,valleys as ridges and troughs as peaks.For this reason,the FTPP can influence the visualization and interpretation of images to a great extent.To scrutinize this problem,the paper firstly reviews and tests the existing FTPP-correction techniques and identifies the inverse slope-matching technique as an effective approach to visually enhance remote-sensing images and retain the colour information.The paper then proposes an improved FTPP-correction procedure that incorporates other image-processing techniques(e.g.linear stretch,histogram matching,and flat-area replacement)to enhance the performance of this technique.A further evaluation of the proposed technique is conducted by applying the technique to various study areas and using different types of remote-sensing images.The result indicates the method is relatively robust and will be a significant extension to geovisual analytics in digital earth research.

Topographically derived subpixel-based change detection for monitoring changes over rugged terrain Himalayas using AWiFS data

Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards.Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data.However,the existing subpixel-based change detection(SCD)models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions.To overcome such issues,a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis(CVA)and hence,named as a subpixel-based change vector analysis(SCVA).This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor(AWiFS)and Landsat-8 datasets.To check the effectiveness of the proposed SCVA,the cross-validation of the results has been done with the existing neural network-based SCD(NN-SCD)and per-pixel based models such as fuzzybasedCVA(FCVA)andpost-classification comparison(PCC).The results have shown that SCVA offered robust performance(85.6%-86.4%)as comparedtoNN-SCD(81.6%-82.4%),PCC(79.2%-80.4%),and FCVA(81.2%-83.6%).We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps.This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets.

Auto-registration and Orthorecification Algorithm for the Time Series HJ-1A/B CCD Images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multi- spectral data of small constellation for environmental and disaster mitigation （HJ-1A/B） which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels （the average residuals were 37.8 m, and standard deviations were 19.8 m）. The accuracies of 45.96% validation points （VPs） were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, pereent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsuffcient precondition of the downstream in a new notion of Space Economy 4.0-Part 1:Problem background in Artificial General Intelligence(AGI)

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this two-part paper identifies an innovative,but realistic EO optical sensory imagederived semantics-enriched Analysis Ready Data(ARD)productpair and process gold standard as linchpin for success of a new notion of Space Economy 4.0.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,it is regarded as necessarybut-not-sufficient“horizontal”(enabling)precondition for:(I)Transforming existing EO big raster-based data cubes at the midstream segment,typically affected by the so-called data-rich information-poor syndrome,into a new generation of semanticsenabled EO big raster-based numerical data and vector-based categorical(symbolic,semi-symbolic or subsymbolic)information cube management systems,eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery.(II)Boosting the downstream segment in the development of an ever-increasing ensemble of“vertical”(deep and narrow,user-specific and domain-dependent)value–adding information products and services,suitable for a potentially huge worldwide market of institutional and private end-users of space technology.For the sake of readability,this paper consists of two parts.In the present Part 1,first,background notions in the remote sensing metascience domain are critically revised for harmonization across the multidisciplinary domain of cognitive science.In short,keyword“information”is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation.Moreover,buzzword“artificial intelligence”is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI.Second,based on a betterdefined and better-understood vocabulary of multidisciplinary terms,existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system.To overcome their drawbacks,an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

Innovative Analysis Ready Data(ARD)product and process requirements,software system design,algorithms and implementation at the midstream as necessary-but-notsufficient precondition of the downstream in a new notion of Space Economy 4.0-Part 2:Software developments

Aiming at the convergence between Earth observation(EO)Big Data and Artificial General Intelligence(AGI),this paper consists of two parts.In the previous Part 1,existing EO optical sensory imagederived Level 2/Analysis Ready Data(ARD)products and processes are critically compared,to overcome their lack of harmonization/standardization/interoperability and suitability in a new notion of Space Economy 4.0.In the present Part 2,original contributions comprise,at the Marr five levels of system understanding:(1)an innovative,but realistic EO optical sensory image-derived semantics-enriched ARD co-product pair requirements specification.First,in the pursuit of third-level semantic/ontological interoperability,a novel ARD symbolic(categorical and semantic)co-product,known as Scene Classification Map(SCM),adopts an augmented Cloud versus Not-Cloud taxonomy,whose Not-Cloud class legend complies with the standard fully-nested Land Cover Classification System’s Dichotomous Phase taxonomy proposed by the United Nations Food and Agriculture Organization.Second,a novel ARD subsymbolic numerical co-product,specifically,a panchromatic or multispectral EO image whose dimensionless digital numbers are radiometrically calibrated into a physical unit of radiometric measure,ranging from top-of-atmosphere reflectance to surface reflectance and surface albedo values,in a five-stage radiometric correction sequence.(2)An original ARD process requirements specification.(3)An innovative ARD processing system design(architecture),where stepwise SCM generation and stepwise SCM-conditional EO optical image radiometric correction are alternated in sequence.(4)An original modular hierarchical hybrid(combined deductive and inductive)computer vision subsystem design,provided with feedback loops,where software solutions at the Marr two shallowest levels of system understanding,specifically,algorithm and implementation,are selected from the scientific literature,to benefit from their technology readiness level as proof of feasibility,required in addition to proven suitability.To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers,the proposed EO optical sensory image-derived semantics-enriched ARD product-pair and process reference standard is highlighted as linchpin for success of a new notion of Space Economy 4.0.

Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images

The accuracy of topographic correction of Landsat data based on a Digital Surface Model(DSM)depends on the quality,scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image.A physics-based bidirectional reflectance distribution function(BRDF)and atmospheric correction model in conjunction with a 1-second DSM was used to conduct the analysis in this paper.The results show that for the examples used from Australia,the 1-second DSM,can provide an effective product for this task.However,it was found that some remaining artefacts in the DSM data,originally due to radar shadow,can still cause significant local errors in the correction.Where they occur,false shadows and over-corrected surface reflectance factors can be observed.More generally,accurate co-registration between satellite images and DSM data was found to be critical for effective correction.Mis-registration by one or two pixels could lead to large errors of retrieved surface reflectance factors in gully and ridge areas.Using low-resolution DSM data in conjunction with high-resolution satellite images will also fail to correct significant terrain components where they occur at the finer scales of the satellite images.DSM resolution appropriate to the resolution of satellite image and the roughness of the terrain is needed for effective results,and the rougher the terrain,the more critical will be the accurate registration.