Evaluation and Calibration of an Agent Based Land use Model Using Remotely Sensed Land Cover and Primary Productivity Data

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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Change Detection of Land Cover at Zheng Lanqi County of Inner Mongolia

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.518-523.1371 ◽

2012 ◽

Vol 518-523 ◽

pp. 1371-1374

Author(s):

Chu La Sa ◽

Gui Xiang Liu ◽

Mu Lan Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Change Detection ◽

Inner Mongolia ◽

Remotely Sensed ◽

Landsat Tm ◽

Remotely Sensed Images ◽

Second Period

In the study we mapped and analyzed the land use/cover changes in Zheng Lanqi county by visual interpreting the 3 sets of Landsat TM and ETM remotely sensed images received in 1990, 2000 and 2005.The 6 broad types of land use/ cover were interpreted for the study area. Through analyzing land use/cover changes, our study indicated that the grassland and built-up area is dominant landscape in the study area.The grassland in the study area shrank 588.68km2 for urbanization and farmland cultivation for first periods. The unchanged land is 10639.75km2 and 10743.18km2 for the two periods (1990-2000 and 2000-2005), respectively. This indicated that the landscape conversion in second period became stable than that of first period, and environment is improved since 2000.

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Environmental Differences between Migratory and Resident Ungulates—Predicting Movement Strategies in Rocky Mountain Mule Deer (Odocoileus hemionus) with Remotely Sensed Plant Phenology, Snow, and Land Cover

Remote Sensing ◽

10.3390/rs11171980 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1980

Author(s):

Benjamin Robb ◽

Qiongyu Huang ◽

Joseph Sexton ◽

David Stoner ◽

Peter Leimgruber

Keyword(s):

Land Use ◽

Land Cover ◽

Snow Cover ◽

Large Scale ◽

Mule Deer ◽

Life History Strategy ◽

Odocoileus Hemionus ◽

Remotely Sensed ◽

Land Use Impacts ◽

Human Land Use

Migration is a valuable life history strategy for many species because it enables individuals to exploit spatially and temporally variable resources. Globally, the prevalence of species’ migratory behavior is decreasing as individuals forgo migration to remain resident year-round, an effect hypothesized to result from anthropogenic changes to landscape dynamics. Efforts to conserve and restore migrations require an understanding of the ecological characteristics driving the behavioral tradeoff between migration and residence. We identified migratory and resident behaviors of 42 mule deer (Odocoileus hemionus) based on GPS locations and correlated their locations to remotely sensed indicators of forage quality, land cover, snow cover, and human land use. The model classified mule deer seasonal migratory and resident niches with an overall accuracy of 97.8% and cross-validated accuracy of 81.2%. The distance to development was the most important variable in discriminating in which environments these behaviors occur, with resident niche space most often closer to developed areas than migratory niches. Additionally, snow cover in December was important for discriminating summer migratory niches. This approach demonstrates the utility of niche analysis based on remotely sensed environmental datasets and provides empirical evidence of human land use impacts on large-scale wildlife migrations.

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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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