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Ecological land classification refers to an integrated survey in which areas of land, as ecosystems, are classified according to their ecological unity. In Canada, the approach was first advanced, nationally, in 1969 and was termed 'Bio-physical Land Classification'. This approach, which was derived from several foreign and domestic precedents, has been employed by various independent survey organizations throughout Canada to secure an ecological data bases for resource planning and management consideration. Because coordination was lacking between these organizations, modifications of this approach have taken place independently and often have been weighted according to the investigator's personal interests or capabilities. As such, the approach currently possesses a disparate character which is difficult to define singularly. To identify the current status in methodology, Canadian works in this field were comparatively analyzed. One result which stands out prominently from the analysis is that there are multifarious forms of ecological land classification. While they tend to achieve the same results, and demonstrate numerous commonalities land ecosystems have been manifested by combinations of criteria which are not always the same. Considerable confusion surrounds the nomenclature, the criteria for definitions and the criteria for recognition. Based on the analysis, hierarchical categories eco- province, ecoregion, ecodistrict, ecosection and ecotype are proposed. These are land ecosystems which possess a common recognized identity based on a unified pattern of biological and physical land characteristics. Each category coincides with a different order of generalization. Based largely on material extracted from past studies, criteria for recognition are stated. / Land and Food Systems, Faculty of / Graduate

Pedology, like most other sciences, is facing a data explosion in which it is becoming increasingly difficult to organize, summarize and interpret large quantities of data. Coupled with this is an unprecedented demand for soils information for consideration in resource management and environmental considerations. Since decision making in these areas increasingly has to be justified by economic criteria, the need for evaluation of soils information for land use considerations in economic terms is paramount. In this study, the British Columbia Soil Survey Data File was used. The file contains only routine soil survey data. It was improved and modified to correct problems arising from earlier experiences. The identification, organization and coding techniques used in the data file are presented. In view of the interest in establishing national data bank systems a modified hierarchical organizational and identification system is proposed, which should be equally applicable at regional, provincial and national levels. The Data File was also used to explore by statistical techniques the inter-relationships between soil properties, the feasibility of predicting the values for dependent variables by multiple regression equations and to study the modal concept of soil which is basic to soil classification and subsequent statistical analysis. Numerical taxonomy techniques were used to determine the feasibility of using objective statistical techniques in the development of a model by which soils could be rated for a specific land use, as well as, determine on the basis of correlation and regression analysis an estimate of hypothetical treatments and costs that would make a given soil behave more like an ideal soil for the use in question. The study showed that, using cash cropping and road bed construction as two contrasting soil uses as examples, the derivation of cost estimates for soil manipulation is feasible. However, the derivation of the ideal soil (model) is critical. / Land and Food Systems, Faculty of / Graduate

This study is concerned with the problem of environmental data collection, interpretation and presentation for regional land use planning. A landscape classification was carried out for the watershed of the southern Okakagan Valley by collecting and integrating data on surficial deposits, vegetation, soil and bedrock geology. Thirty nine land systems are described and mapped, and shown on a base map at a scale of 1:125,000. The land systems are relatively homogeneous landscape units, characterized by a particular landform (or patterns of landforms) with associated vegetation and soil. Interpretive guidelines were developed for determining the suitability of the land systems for selected engineering (urban development), recreation and wildlife interpretations. The interpretive guidelines with the derived suitability ratings provide planning information for the region, show how the classification system can assist regional land use planning and form a framework for similar studies in other areas. Methods of data presentation were used to facilitate the understanding and application of this information by planners, technical experts,scientists and the concerned public. Specifically this was accomplished through the use of an expanded legend, stereo-pair and colour photographs, and by having separate sections for referencing information. The methods used in this study provide a rapid and relatively inexpensive framework for collecting, presenting and interpreting environmental baseline information. The information can be of valuable assistance to technical and non-technical people in the land use planning and decision making processes. / Land and Food Systems, Faculty of / Graduate

Chan, King-Chong. / Thesis (M.Phil.) -- Chinese University of Hong Kong, 1991. / Bibliography: leaves 150-163. / Abstract --- p.i / Acknowledgements --- p.ii / Figures --- p.vii / Tables --- p.x / Chapter Chapter 1 --- Introduction --- p.i / Chapter 1.1 --- Background --- p.2 / Chapter 1.2 --- Objectives --- p.5 / Chapter 1.3 --- Hypotheses --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.6 / Chapter Chapter 2 --- Literature Review --- p.8 / Chapter 2.1 --- Land Use and Land Cover --- p.10 / Chapter 2.2 --- Informational Classes and Spectra I Classes --- p.11 / Chapter 2.3 --- Simple Per-Pixel Classification Method --- p.12 / Chapter 2.4 --- Scene Noise and Boundary Effect --- p.14 / Chapter 2.5 --- Using Filtered Data --- p.16 / Chapter 2 .6 --- Textura1 Classifier --- p.18 / Chapter 2.7 --- Contextual Classifier --- p.22 / Chapter 2.8 --- Geographic Information System (GIS) --- p.24 / Chapter 2.9 --- Expert System and Artificial Intelligence (AI) --- p.25 / Chapter 2.10 --- Concluding Remarks --- p.27 / Chapter Chapter 3 --- Methodology --- p.30 / Chapter 3.1 --- Spectral Class Composition Method (SCCM) --- p.32 / Chapter 3.1.1 --- The Concept of the Spectral Class Composition Method --- p.32 / Chapter 3.1.2 --- Unsupervised Classification Process --- p.39 / Chapter 3.1.3 --- Training Process --- p.39 / Chapter 3.1.4 --- Proportion Counting --- p.40 / Chapter 3.1.5 --- Number of Spectral Class --- p.41 / Chapter 3.1.6 --- Window Size --- p.42 / Chapter 3.1.7 --- Transect Process --- p.43 / Chapter 3.1.8 --- Classification Task --- p.45 / Chapter 3.1.9 --- Summary --- p.47 / Chapter 3.2 --- Research Design --- p.49 / Chapter 3.2.1 --- Study Area --- p.49 / Chapter 3.2.2 --- Data and Instruments Used --- p.51 / Chapter 3.2.3 --- C1assification Scheme --- p.51 / Chapter 3.2.4 --- Accuracy Assessment --- p.52 / Chapter Chapter 4 --- Results and Discussion I--- Examining the Relationship Between Land Use and Spectral Classes --- p.55 / Chapter 4. 1 --- Unsupervised Classification --- p.57 / Chapter 4.1.1 --- Unsupervised Classification Process --- p.57 / Chapter 4.1.2 --- Unsupervised Classification Results --- p.58 / Chapter 4.1.3 --- Difference Between Spectral Class Maps --- p.65 / Chapter 4.2 --- Training Process --- p.68 / Chapter 4.2.1 --- Definition of Training Process --- p.68 / Chapter 4.2.2 --- Selection of Training Sites --- p.69 / Chapter 4.2.3 --- Spectral Class Composition Data Extracted from the Training Sites --- p.70 / Chapter 4.2.4 --- Spectral Heterogeneous Characteristics of Land Use Types --- p.73 / Chapter 4.2.5 --- Different Number of Spectral Classes --- p.77 / Chapter 4.2.6 --- Similar Composition Results in Some Land Use Types --- p.80 / Chapter 4.2.7 --- Using Spectra1 Class Composition Data as Rules of Classification --- p.81 / Chapter 4.3 --- Proportion Counting --- p.83 / Chapter 4.3.1 --- Window-Based Proportion Counting Process --- p.83 / Chapter 4.3.2 --- Transect Process --- p.85 / Chapter 4.3.3 --- Variation of Spectra I Class Proportion within a Land Use Type --- p.91 / Chapter 4.3.4 --- Variation of Spectral Class Proportion among Land Use Types --- p.95 / Chapter 4.4 --- Summary --- p.103 / Chapter Chapter 5 --- Resu1ts and Discussion II --- Classification and Accuracy Assessment --- p.104 / Chapter 5.1 --- Rule-Based Land Use Classification --- p.106 / Chapter 5.1.1 --- Derivation of Rules for Classification --- p.106 / Chapter 5.1.2 --- Using Rules for Classification --- p.106 / Chapter 5.1.3 --- Modification of the Rules --- p.109 / Chapter 5.1.4 --- C1assification Resu11s --- p.109 / Chapter 5.2 --- Accuracy Assessment --- p.118 / Chapter 5.2.1 --- Accuracy Assessment Process --- p.118 / Chapter 5.2.2 --- Analysis of Error Matrices --- p.123 / Chapter 5.2.3 --- Comparison Between Spectral Class Composition Method and Simple Per-Pixel Method --- p.126 / Chapter 5.2.4 --- Discussion on the Resui.ts of Producer's and User's Accuracy --- p.130 / Chapter 5.2.5 --- Discussion on Number of Spectral Classes --- p.132 / Chapter 5.2.6 --- Discuss i on on Window Size --- p.134 / Chapter 5 .3 --- Summary --- p.136 / Chapter Chapter 6 --- Conclusion --- p.138 / Chapter 6.1 --- Summary --- p.139 / Chapter 6.2 --- Limitations and Problems --- p.142 / Chapter 6.3 --- Contribution --- p.147 / Chapter 6.4 --- Further Research --- p.148 / Bibliography --- p.150

The problems of land resources degradation as a result of misuse of arable land for non agricultural development and lack of appropriate methods and guidelines for land resources assessment are currently evident in Eritrea. These problems, have called for an urgent need for an appropriate land resources assessment in Eritrea. In response to this, a land capability classification in the areas around Asmara city that covers about 11742.7 ha was conducted. The intended aim was to properly assess the potential of the land resources in the study area and classify the capability of the land so as to designate the land according to its capability and foster appropriate land use. All the available natural resources in the study area were carefully assessed. A detailed soil survey was conducted and soil units were examined, described, classified and mapped out. Several criteria for the limitations were selected from the reviewed literature mainly USDA and RSA Land Capability Classification systems and in consultation with the soil survey and natural resources experts of the Ministry of Agriculture in Eritrea. In formation on land and soil characteristics, and the specified limitations and criteria were captured in a spatial digital format and then analysed within a GIS. Based on the specified parameters, different land capability units, subclasses, classes and orders were identified and mapped out. Finally, the sub classes were grouped to create,land capability classes ranging from Class I to Class VII and consequently the capability classes were grouped and mapped out at the level of land capability orders. The results revealed seven land capability classes (Class I to VII). Class III land in the study area covers 4149.43 ha (36.9 percent of the total area). The largest portion of this class is found in the central, southern and south eastern parts of the study area. However, classes I and II are very limited and cover 1562.95 ha (13.9 percent) of the study area. These classes are found mainly in the southern and central parts of the study area. Most of the gentle and steep sloping lands in the north and north eastern parts of the study area are classified as classes IV and VI. These classes have an area of 2652.08 ha (23.6 percent) and 2594.87 ha (23.1 percent) of the study area, respectively. Classes V and VII are very limited. These classes cover 221.53 ha (2 percent) and 57.55 ha (0.5 percent), respectively. The largest portion of class V land is found in the central part of the study area. Class VII land is mainly confined to the north eastern, western and southern corners of the study area. Four land capability orders were arrived at ranging from (high to moderate potential to non-arable land). The high to moderate potential arable lands are largely found in the southern and central parts of the study area. These lands cover 5715.39 ha (50.8 percent) of the study area. However, low potential arable (marginal productive) and non-arable lands have a considerable area of 2652.08 ha (23.6 percent) and 2652.42 ha (23.1 percent) of the study area, respectively. The largest portion of these lands is found in the north, north eastern and eastern parts of the study area. A small portion of the lands in the study area is classified as seasonally wetland. This land has an are~\ of 221.53 h~{2 percent) of the study area and is mainly found in the central part of the study area. It was concluded that nearly 50 percent of the land in the study area is classified as of moderately to high agricultural potential whereas the rest of the land is classified as marginal to non-arable land. However, the steady growth of demand for land for nonagricultural development due to the increasing population that depend on farm production in the study area, renders the prime arable lands as too limited to support the current population in the study area. Hence, protecting the prime arable lands and properly using such lands based on their sustained capacity can only secure the livelihood of the community. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2004.

Land use/land cover (LULC) information is essential for a plethora of applications including environmental monitoring and natural resource management. Traditionally, field surveying techniques were the sole source of acquiring such information; however, these methods are labour intensive, costly and time consuming. With the advent of remote sensing, LULC information can be acquired in an economical, less tedious and non-time consuming manner at shorter temporal cycles and over larger areas. The aim of this study was to assess the utility of multispectral remote sensing data and the Random Forest (RF) algorithm to improve accuracy of LULC maps in heterogeneous ecosystems. The first part of this study used moderate resolution SPOT-5 data to compare the performance of the RF algorithm to that of the commonly used Maximum Likelihood (ML) classifier. Results indicated that RF performed significantly better than ML (66.1%) and yielded an overall accuracy of 80.2%. Moreover, RF variable importance measures were able to provide an insight on the bands that played a pivotal role in the classification process. Due to the fact that moderate resolution satellite data was used, both classifiers seemed to experience some difficulties in discriminating amongst classes that exhibited similar spectral responses such as Eucalyptus grandis and Pinus tree plantations, young sugarcane and mature sugarcane, as well as river and ocean water. In that regard, the next section attempted to address this shortfall. The second part of the study used high resolution multispectral data acquired from the WorldView-2 sensor to discriminate amongst six spectrally similar LULC classes using the advanced RF algorithm. Results suggested that the use of WorldView-2 data together with the RF ensemble algorithm is a robust and accurate method for separating classes exhibiting similar spectral responses. The classification process yielded an overall accuracy of 91.23% and also provided valuable insight into WorldView-2 bands that were most suitable for discriminating the LULC categories. Overall, the study concluded that: (i) multispectral remote sensing data is an effective tool for obtaining accurate and timely LULC information, (ii) moderate resolution multispectral data can be used to map broad LULC categories whereas high resolution multispectral data can be used to separate LULC at finer levels of detail, (iii) RF is a robust and effective tool for producing LULC maps that are less prone to error. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

By organically fusing the methods of convolutional neural network (CNN) and recurrent neural network (RNN), this dissertation focuses on the application of optical character recognition and image classification processing. The first part of this dissertation presents an end-to-end novel receipt recognition system for capturing effective information from receipts (CEIR). The main contributions of this research part are divided into three parts. First, this research develops a preprocessing method for receipt images. Second, the modified connectionist text proposal network is introduced to execute text detection. Third, the CEIR combines the convolutional recurrent neural network with the connectionist temporal classification with maximum entropy regularization as a loss function to update the weights in networks and extract the characters from receipt. The CEIR system is validated with the scanned receipts optical character recognition and information extraction (SROIE) database. Furthermore, the CEIR system has strong robustness and can be extended to a variety of different scenarios beyond receipts. For the convolutional recurrent structure application of land use image classification, this dissertation comes up with a novel deep learning model for land use classification, the convolutional recurrent land use classifier (CRLUC), which further improves the accuracy in classifying remote sensing land use images. Besides, the convolutional fully-connected neural networks with hard sample memory pool structure (CFMP) is invented to tackle the remote sensing land use image classification tasks. The CRLUC and CFMP algorithm performances are tested in popular datasets. Experimental studies show the proposed algorithms can classify images with higher accuracy and fewer training episodes compared to popular image classification algorithms.

Accurate and current land use information for urban areas is important for effective management and planning. Over the years, researchers/planners have relied heavily on aerial photographs for land use information of urban areas because of the limitations of deriving more accurate land use estimates from satellite remote sensing data. The main problem involved in producing accurate land use maps of cities and towns from satellite images is that urban areas consist of a complex assemblage of different land cover types, many of which have very similar spectral reflectance characteristics. This is because land use is an abstract concept- n amalgam of economic, social and cultural factors-that is defined in terms of functions rather than forms. The relationship between land use and the multispectral signals detected by a satellite sensor is therefore both complex and indirect. In many European cities, residential areas are characterized by a complex spatial assemblage of tile roof, slate roof, glass roof buildings, as well as tarmac, concrete and pitch roads, and gardens (comprised of grass lawns, trees and plants). In North American cities, roofing materials are more commonly composed of wood and shingles. In both settings all these "objects" together form the residential areas or residential districts of town or city, but each of them has a different spectral reflectance. So, in generating a land use map from remotely sensed image, buildings, roads, gardens, open spaces will be identified separately. Keeping this in mind, this thesis evaluates eight selected land use classification methods for the Vancouver metropolitan area, identifies the most accurate and suitable method for urban land use classification, and produces a land use map of the study area based on the most suitable method. The study area is a part of Greater Vancouver Regional District (GVRD). It includes Vancouver, Burnaby, Richmond, Delta, and parts of seven other municipalities. The whole area is highly urbanized and commercialized. Agricultural lands are present in the southern part of the study area (which includes parts of Richmond, Delta and Surrey). For this study four sources of data have been used. The 1996 Greater Vancouver regional District (GVRD) land use map is the basic source of land use information. A remotely sensed image of May 1999 (Landsat 7) has been used for the identification of land cover data, Vancouver and Fraser valley orthophotos (May/July 1995) have been used to locate sample sites, and aerial photos of May 1999 (1:30,000) have been used for ground verification.

Thesis (MSc)--Stellenbosch University, 2005. / ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further. / AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.