Land cover mapping
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Land cover maps are tools that provide vital information about the Earth's land use and cover patterns. They aid policy development,
urban planning Urban planning, also known as town planning, city planning, regional planning, or rural planning, is a technical and political process that is focused on the development and design of land use and the built environment, including air, water, ...
, and forest and agricultural monitoring. The systematic mapping of land cover patterns, including change detection, often follows two main approaches: *
Field survey Field research, field studies, or fieldwork is the collection of raw data outside a laboratory, library, or workplace setting. The approaches and methods used in field research vary across disciplines. For example, biologists who conduct fi ...
* Remote sensing satellite image processing. This cost-efficient approach employs several techniques for image pre-processing and processing to accurately map land cover patterns. These techniques detect changes at various spatial scales following a series of
machine learning Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine ...
simulations and statistical applications. Image pre-processing is normally done through
radiometric Radiometry is a set of techniques for measuring electromagnetic radiation, including visible light. Radiometric techniques in optics characterize the distribution of the radiation's power in space, as opposed to photometric techniques, which ch ...
corrections, while image processing involves the application of either unsupervised or supervised classifications and vegetation indices quantification for land cover map production.


Supervised classification

A supervised classification is a system of classification in which the user builds a series of randomly generated training datasets or spectral signatures representing different land-use and land-cover (LULC) classes and applies these datasets in machine learning models to predict and spatially classify LULC patterns and evaluate classification accuracies.


Algorithms

Several
machine learning algorithms The following outline is provided as an overview of and topical guide to machine learning. Machine learning is a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning ...
have been developed for supervised classification. * Maximum likelihood classification (MLC) – This approach classifies overlapping signatures by estimating the
probability Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speakin ...
that an image
pixel In digital imaging, a pixel (abbreviated px), pel, or picture element is the smallest addressable element in a raster image, or the smallest point in an all points addressable display device. In most digital display devices, pixels are the ...
with the maximum likelihood corresponds to a particular LULC type. It is also dependent on the mean and
covariance In probability theory and statistics, covariance is a measure of the joint variability of two random variables. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the ...
matrices of training datasets and assumes statistical significance of image pixels. * Minimum distance (MD) – A form of supervised classification that defines decision boundaries between image pixels to classify land cover. The decision boundaries are formed by calculating the mean distance between class pixels and using the standard deviation of the generated training datasets to generate a parallelepiped box. * Mahalanobis distance – A system of classification that uses the
Euclidean distance In mathematics, the Euclidean distance between two points in Euclidean space is the length of a line segment between the two points. It can be calculated from the Cartesian coordinates of the points using the Pythagorean theorem, therefor ...
algorithm to assign land cover classes from a set of training datasets. * Spectral angler mapper (SAM) – A spectral image classification approach that uses angular measurements to determine the relationship between two spectra, treating them as vectors in a ''q''-dimensional space, with the ''q''-dimensions representing the number of bands. *
Discriminant analysis Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features ...
(DA) – A system of classification in which the classifying algorithm separates groups of closely related image pixels into classes, minimizing the variance within classes, and maximizing the variance between classes following a maximum likelihood discriminant rule. * Genetic algorithm – A system of classification that applies genetic principles for selecting appropriate clusters of training data and classifying them under the influence of predictors (satellite image bands). * Subspace – A classification approach in which the classifier creates low dimensional subspaces of each land cover class selected from a cluster of training points. The approach of dimensional subspace creation involves performing a principal component analysis on the training points. Two types of subspace algorithms exist for minimizing land cover classification errors: class-featuring information compression (CLAFIC) and the average learning subspace method (ALSM). * Parallelepiped classification – A feature space classifier that assigns range of values for each land cover class within each image band and creates
bounding box In geometry, the minimum or smallest bounding or enclosing box for a point set in dimensions is the box with the smallest measure (area, volume, or hypervolume in higher dimensions) within which all the points lie. When other kinds of measure ...
es where pixels from each land cover class are selected for training the classifier. * Multi-perceptron artificial neural networks (MPANNs) – A system of classification in which the classifier uses a series of neural networks or nodes to classify land cover based on
backpropagation In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward artificial neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions gener ...
s of training samples. * Support vector machines (SVMs) – A classification approach in which the classifier uses support vectors to obtain optimal decision boundaries separating two or more land cover classes. * Random forest (RF) – An approach in which the classifier uses bootstraps to create several decision trees that classify training datasets based on a number of satellite image bands. * ''K''-nearest neighbors algorithm (''k''NN) – This approach draws ''k'' closest samples from training datasets and classifies land cover based on the distance between these samples. * Decision tree (DT) – Like RF, DT constitutes a set of connected nodes that partition training samples into a set of land cover clusters. Its advantages are that it is fast, easy to construct and interpret for smaller data, and good at excluding background or unimportant information. It is disadvantageous in that it can create overfitting, especially for large datasets. *
Fuzzy clustering Fuzzy clustering (also referred to as soft clustering or soft ''k''-means) is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters such that ...
(FZ)


Unsupervised classification

Unsupervised classification is a system of classification in which single or groups of pixels are automatically classified by the software without the user applying signature files or training data. However, the user defines the number of classes for which the computer will automatically generate by grouping similar pixels into a single category using a clustering algorithm. This system of classification is mostly used in areas with no field observations or prior knowledge on the available land cover types.


Algorithms

* Iterative self-organizing data analysis technique (ISODATA) – In this approach, the classifier automatically groups a number of closely related image pixels into clusters, and then computes the mean clusters and classifies land cover based on a series of repeated iterations. * ''K''-means clustering – An approach in which the computer automatically extracts ''k'' land cover features from satellite images, and classifies the overall image based on the calculated means of the extracted features.


Vegetation indices classification

Vegetation indices classification is a system in which two or more spectral bands are combined through defined statistical algorithms to reflect the spatial properties of a vegetation cover. Most of these indices make use of the relationship between red and
near-infrared Infrared (IR), sometimes called infrared light, is electromagnetic radiation (EMR) with wavelengths longer than those of Light, visible light. It is therefore invisible to the human eye. IR is generally understood to encompass wavelengths from ...
(NIR) bands of satellite images to generate vegetation properties. Several vegetation indices have been developed; scientists apply these via remote sensing to effectively classify forest cover and land use patterns. These spectral indices use two or more bands to accurately acquire surface reflectance of land features, thereby improving classification accuracy.


Vegetation indices

*
Normalized difference vegetation index The normalized difference vegetation index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, often from a space platform, assessing whether or not the target being observed contains live green veget ...
(NDVI) – Defined as the ratio between the red and near-infrared (NIR) bands of satellite images. It is calculated as: ::\text = :This index measures vegetation greenness, with values ranging between -1 and 1. High NDVI values represent dense vegetation cover, moderate NDVI values represent sparse vegetation cover, and low NDVI values correspond to non-vegetated areas (e.g., barren or bare lands). *
Enhanced vegetation index The enhanced vegetation index (EVI) is an 'optimized' vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background si ...
(EVI) – Defined as the ratio between the red, NIR, and blue bands, with a gain factor (G), soil brightness correction factor (L) and atmospheric aerosol correction factors (C). It is calculated as: ::G \times :with usually default values of L = 0.5 and G = 2.5. * Soil adjusted vegetation index (SAVI) – Defined as the ratio between the red and NIR values with a soil brightness correction factor (L). It is calculated as: ::\text = (1 + L) \times * Canopy shadow index (SI) – Defined as the square root of the red and green bands of satellite images. It evaluates the different shadow patterns of forest canopies based on age, structure, and composition, as well as easily differentiates dense forests from grass and bare lands. It is calculated as: ::\text = \sqrt[] :where both red and green range between 0 and 256. *Advanced vegetation index (AVI) – Used to differentiate forest cover from grassland and bare land areas. It is calculated as: ::\text = \sqrt /math> :where red ranges between 0 and 256. *Bare soil index (BSI) – Defined as the ratio between the NIR, red, and blue bands of satellite images. It measures the amount of bare soil and as such increases with decrease forest density. It is calculated as: ::\text = *Normalized differential water index (NDWI) – Developed for quantifying the water content of plants and other earth system features, using short-wave infrared (SWIR). It is calculated as: ::\text = *Normalized differential built-up index (NDBI) – Developed for quantifying built-up areas in satellite images. It is calculated as: ::\text =


See also

* Land cover * Vegetation Index


References

{{Reflist Land use Land surveying systems