Land Use and Land Cover Changes in Northern Kordofan State of Sudan: A Remotely Sensed Data Analysis

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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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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Issues involved in the accuracy assessment of large scale land use/land cover mapping from remotely sensed data

IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120) ◽

10.1109/igarss.2000.858211 ◽

2002 ◽

Author(s):

S. Khorram ◽

J. Knight ◽

X.L. Dai ◽

Hui Yuan ◽

H. Cakir ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Large Scale ◽

Accuracy Assessment ◽

Remotely Sensed ◽

Land Cover Mapping ◽

Land Use Land Cover ◽

Remotely Sensed Data ◽

Large Scale Land

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Coastal geomorphological and land‐use and land‐cover study of Sagar Island, Bay of Bengal (India) using remotely sensed data

International Journal of Remote Sensing ◽

10.1080/01431160500500375 ◽

2006 ◽

Vol 27 (17) ◽

pp. 3671-3682 ◽

Cited By ~ 26

Author(s):

K. S. Jayappa ◽

D. Mitra ◽

A. K. Mishra

Keyword(s):

Land Use ◽

Land Cover ◽

Bay Of Bengal ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Sagar Island

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Local Perception of Drivers of Land-Use and Land-Cover Change Dynamics across Dedza District, Central Malawi Region

Sustainability ◽

10.3390/su11030832 ◽

2019 ◽

Vol 11 (3) ◽

pp. 832 ◽

Cited By ~ 9

Author(s):

Maggie G. Munthali ◽

Nerhene Davis ◽

Abiodun M. Adeola ◽

Joel O. Botai ◽

Jonathan M. Kamwi ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Land Management ◽

Agricultural Land ◽

Management Strategies ◽

Rural Livelihoods ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Barren Land ◽

Lulc Changes

Research on Land Use and Land Cover (LULC) dynamics, and an understanding of the drivers responsible for these changes, are very crucial for modelling future LULC changes and the formulation of sustainable and robust land-management strategies and policy decisions. This study adopted a mixed method consisting of remote sensing and Geographic Information System (GIS)-based analysis, focus-group discussions, key informant interviews, and semi-structured interviews covering 586 households to assess LULC dynamics and associated LULC change drivers across the Dedza district, a central region of Malawi. GIS-based analysis of remotely sensed data revealed that barren land and built-up areas extensively increased at the expense of agricultural and forest land between 1991 and 2015. Analysis of the household-survey results revealed that the perceptions of respondents tended to validate the observed patterns during the remotely sensed data-analysis phase of the research, with 57.3% (n = 586) of the respondents reporting a decline in agricultural land use, and 87.4% (n = 586) observing a decline in forest areas in the district. Furthermore, firewood collection, charcoal production, population growth, and poverty were identified as the key drivers of these observed LULC changes in the study area. Undoubtedly, education has emerged as a significant factor influencing respondents’ perceptions of these drivers of LULC changes. However, unsustainable LULC changes observed in this study have negative implications on rural livelihoods and natural-resource management. Owing to the critical role that LULC dynamics play to rural livelihoods and the ecosystem, this study recommends further research to establish the consequences of these changes. The present study and future research will support decision makers and planners in the design of tenable and coherent land-management strategies.

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