scholarly journals Study on Regional Land Cover Patterns Derived From Multi-Scale Remotely Sensed Data

2003 ◽  
Vol 27 (5) ◽  
pp. 577-586
Author(s):  
LI Xiao-Bing ◽  
CHEN Yun-Hao ◽  
Li Xia
Keyword(s):  
Land Cover ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Multi Scale ◽  
Regional Land

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

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

2014 ◽  
pp. 269-283 ◽  
Author(s):  
Mohamed S. Dafalla ◽  
Elfatih M. Abdel-Rahman ◽  
Khalid H. A. Siddig ◽  
Ibrahim S. Ibrahim ◽  
Elmar Csaplovics
Keyword(s):  
Land Use ◽  
Data Analysis ◽  
Land Cover ◽  
Remotely Sensed ◽  
Land Cover Changes ◽  
Remotely Sensed Data

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

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.

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

2011 ◽  
Vol 31 (3) ◽  
pp. 1166-1172 ◽  
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

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

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 856 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document