Land Use/Cover Classification Techniques Using Optical Remotely Sensed Data in Landscape Planning

Due to the growing advances in their temporal, spatial, and spectral resolutions, remotely sensed data continues to provide tools for a wide variety of environmental applications. This chapter presents the benefits and difficulties of Multi-Temporal Satellite Image (MTSI) for land use. Predicting land use changes using remote sensing is an area of interest that has been attracting increasing attention. Land use analysis from high temporal resolution remotely sensed images is important to promote better decisions for sustainable management land cover. The purpose of this book chapter is to review the background of using Hidden Markov Model (HMM) in land use change prediction, to discuss the difference on modeling using stationary as well as non-stationary data and to provide examples of both case studies (e.g. vegetation monitoring, urban growth).

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Measuring and monitoring land cover, land use, and vegetation characteristics

Remote Sensing for Ecology and Conservation ◽

10.1093/oso/9780199219940.003.0011 ◽

2010 ◽

Author(s):

Ned Horning ◽

Julie A. Robinson ◽

Eleanor J. Sterling ◽

Woody Turner ◽

Sacha Spector

Keyword(s):

Land Use ◽

Land Cover ◽

Deciduous Forest ◽

Land Cover Classification ◽

Remotely Sensed ◽

Land Cover Mapping ◽

Remotely Sensed Data ◽

Soil P ◽

Vegetation Characteristics ◽

Classification Project

In terrestrial biomes, ecologists and conservation biologists commonly need to understand vegetation characteristics such as structure, primary productivity, and spatial distribution and extent. Fortunately, there are a number of airborne and satellite sensors capable of providing data from which you can derive this information. We will begin this chapter with a discussion on mapping land cover and land use. This is followed by text on monitoring changes in land cover and concludes with a section on vegetation characteristics and how we can measure these using remotely sensed data. We provide a detailed example to illustrate the process of creating a land cover map from remotely sensed data to make management decisions for a protected area. This section provides an overview of land cover classification using remotely sensed data. We will describe different options for conducting land cover classification, including types of imagery, methods and algorithms, and classification schemes. Land cover mapping is not as difficult as it may appear, but you will need to make several decisions, choices, and compromises regarding image selection and analysis methods. Although it is beyond the scope of this chapter to provide details for all situations, after reading it you will be able to better assess your own needs and requirements. You will also learn the steps to carry out a land cover classification project while gaining an appreciation for the image classification process. That said, if you lack experience with land cover mapping, it always wise to seek appropriate training and, if possible, collaborate with someone who has land cover mapping experience (Section 2.3). Although the terms “land cover” and “land use” are sometimes used interchangeably they are different in important ways. Simply put, land cover is what covers the surface of the Earth and land use describes how people use the land (or water). Examples of land cover classes are: water, snow, grassland, deciduous forest, or bare soil.

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Impacts of land use/land cover on runoff and energy budgets in an East Asia ecosystem from remotely sensed data in a community land model

The Science of The Total Environment ◽

10.1016/j.scitotenv.2019.05.244 ◽

2019 ◽

Vol 684 ◽

pp. 641-656 ◽

Cited By ~ 5

Author(s):

Muhammad Umair ◽

Daeun Kim ◽

Minha Choi

Keyword(s):

Land Use ◽

Land Cover ◽

East Asia ◽

Remotely Sensed ◽

Energy Budgets ◽

Land Use Land Cover ◽

Remotely Sensed Data ◽

Community Land Model

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Historical spatiotemporal analysis of land-use/land-cover changes and carbon budget in a temperate peatland (Turkey) using remotely sensed data

Applied Geography ◽

10.1016/j.apgeog.2011.03.007 ◽

2011 ◽

Vol 31 (3) ◽

pp. 1166-1172 ◽

Cited By ~ 14

Author(s):

Fatih Evrendilek ◽

Suha Berberoglu ◽

Nusret Karakaya ◽

Ahmet Cilek ◽

Guler Aslan ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Carbon Budget ◽

Spatiotemporal Analysis ◽

Remotely Sensed ◽

Land Cover Changes ◽

Land Use Land Cover ◽

Remotely Sensed Data

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Estimating Land Use and Land Cover Change in North Central Georgia: Can Remote Sensing Observations Augment Traditional Forest Inventory Data?

Forests ◽

10.3390/f11080856 ◽

2020 ◽

Vol 11 (8) ◽

pp. 856 ◽

Cited By ~ 1

Author(s):

Gretchen G. Moisen ◽

Kelly S. McConville ◽

Todd A. Schroeder ◽

Sean P. Healey ◽

Mark V. Finco ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Forest Inventory ◽

Remotely Sensed ◽

Data Sets ◽

Inventory Data ◽

Remotely Sensed Data ◽

North Central ◽

Forest Inventory Data ◽

Long Time

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes.

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Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories

Remote Sensing ◽

10.3390/rs12111891 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1891 ◽

Cited By ~ 2

Author(s):

Ronald E. McRoberts ◽

Erik Næsset ◽

Christophe Sannier ◽

Stephen V. Stehman ◽

Erkki O. Tomppo

Keyword(s):

Remote Sensing ◽

Land Use ◽

Greenhouse Gas ◽

Good Practice ◽

Remotely Sensed ◽

Current Status ◽

Remotely Sensed Data ◽

Activity Data ◽

Greenhouse Gas Inventories ◽

Gain Loss

For tropical countries that do not have extensive ground sampling programs such as national forest inventories, the gain-loss approach for greenhouse gas inventories is often used. With the gain-loss approach, emissions and removals are estimated as the product of activity data defined as the areas of human-caused emissions and removals and emissions factors defined as the per unit area responses of carbon stocks for those activities. Remotely sensed imagery and remote sensing-based land use and land use change maps have emerged as crucial information sources for facilitating the statistically rigorous estimation of activity data. Similarly, remote sensing-based biomass maps have been used as sources of auxiliary data for enhancing estimates of emissions and removals factors and as sources of biomass data for remote and inaccessible regions. The current status of statistically rigorous methods for combining ground and remotely sensed data that comply with the good practice guidelines for greenhouse gas inventories of the Intergovernmental Panel on Climate Change is reviewed.

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