Analysis of Land Cover Type Using Landsat-8 Data

2021 ◽  
pp. 78-89
Author(s):  
V. Samuktha ◽  
M. Sabeshnav ◽  
A. Krishna Sameera ◽  
J. Aravinth ◽  
S. Veni
Keyword(s):  
Land Cover ◽  
Landsat 8 ◽  
Land Cover Type ◽  
Cover Type

scholarly journals Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool

2021 ◽  
Author(s):  
Kristofer Lasko ◽  
Elena Sava
Keyword(s):  
Land Cover ◽  
Decision Support Tool ◽  
Support Vector ◽  
Landsat 8 ◽  
Terrain Analysis ◽  
Land Cover Type ◽  
Support Tool ◽  
Cover Type ◽  
On Demand ◽  
Sentinel 2

Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.

Relationship between land cover type and evapotranspiration on the basis of Landsat 8 and ZY3 data fusion approach for a desert oasis in the middle Hexi corridor area of the arid regions of northwestern China

2019 ◽  
Vol 39 (19) ◽  
Author(s):  
焦丹丹 JIAO Dandan ◽  
吉喜斌 JI Xibin ◽  
金博文 JIN Bowen ◽  
赵丽雯 ZHAO Liwen ◽  
张靖琳 ZHANG Jinglin ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Arid Regions ◽  
Hexi Corridor ◽  
Landsat 8 ◽  
Land Cover Type ◽  
Cover Type ◽  
Desert Oasis ◽  
The Arid Regions ◽  
Fusion Approach

scholarly journals Testing NDVI, tree cover density and land cover type as fuel indicators in the wildfire spread capacity index (WSCI): case of Montenegro

2020 ◽  
Vol 48 (4) ◽  
pp. 2368-2384
Author(s):  
Artan HYSA ◽  
Velibor SPALEVIC
Keyword(s):  
Land Cover ◽  
Impact Factor ◽  
Vegetation Index ◽  
Tree Cover ◽  
Landsat 8 ◽  
Land Cover Type ◽  
Ignition Probability ◽  
High Resolution Data ◽  
Cover Type ◽  
Cover Density

This paper presents an updated version of our previous GIS-based method developed for indexing the forest surfaces by their wildfire ignition probability (WIPI) and wildfire spreading capacity (WSCI). The previous study relied on a multi-criteria approach including a variety of factors of social, hydro-meteorological, and geo-physical character of the context. However, this study is challenging the drawbacks of the previous work, by introducing three new criteria regarding the vegetation properties in the area. Normalized Difference Vegetation Index (NDVI), Tree Cover Density (TCD), and land cover type are launched as indicators of fuel properties of the forest being indexed. The materials and software utilized here belongs to different open sources. CORINE Land Cover (CLC), Open Street Map (OSM), TCD via Copernicus high resolution data, and multispectral satellite images via Landsat 8 (Semi-Automatic Classification Plugin- SCP) are utilized as raw materials in a workflow in QGIS software. At this stage, the study area is the territory of Montenegro. Following the inventory stage, the indexing method relies on a normalizing procedure in QGIS and the assignment of weighted impact factor to each criterion via analytical hierarchy process (AHP). The WSCI value is derived as the sum of the products between the normalized class and the respective weighted impact factor of each criterion. Besides the methodological improvements the results of this work deliver tangible outputs in support of forest fire risk reduction in disaster risk management and fire safety agendas.

scholarly journals Land Use and Land Cover Change Assessment in the Context of Flood Hazard in Lagos State, Nigeria

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1105
Author(s):  
Dorcas Idowu ◽  
Wendy Zhou
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Flood Hazard ◽  
The State ◽  
Land Cover Type ◽  
Lulc Change ◽  
Cover Type ◽  
Lulc Changes ◽  
Lagos State ◽  
Very High

Incessant flooding is a major hazard in Lagos State, Nigeria, occurring concurrently with increased urbanization and urban expansion rate. Consequently, there is a need for an assessment of Land Use and Land Cover (LULC) changes over time in the context of flood hazard mapping to evaluate the possible causes of flood increment in the State. Four major land cover types (water, wetland, vegetation, and developed) were mapped and analyzed over 35 years in the study area. We introduced a map-matrix-based, post-classification LULC change detection method to estimate multi-year land cover changes between 1986 and 2000, 2000 and 2016, 2016 and 2020, and 1986 and 2020. Seven criteria were identified as potential causative factors responsible for the increasing flood hazards in the study area. Their weights were estimated using a combined (hybrid) Analytical Hierarchy Process (AHP) and Shannon Entropy weighting method. The resulting flood hazard categories were very high, high, moderate, low, and very low hazard levels. Analysis of the LULC change in the context of flood hazard suggests that most changes in LULC result in the conversion of wetland areas into developed areas and unplanned development in very high to moderate flood hazard zones. There was a 69% decrease in wetland and 94% increase in the developed area during the 35 years. While wetland was a primary land cover type in 1986, it became the least land cover type in 2020. These LULC changes could be responsible for the rise in flooding in the State.

Topsoil microbial community structure responds to land cover type and environmental zone in the Western Pacific region

2021 ◽  
Vol 764 ◽  
pp. 144349
Author(s):  
Yu-Pin Lin ◽  
Rainer Ferdinand Wunderlich ◽  
Chiao-Ming Lin ◽  
Norman Uphoff ◽  
Dirk S. Schmeller ◽  
...  
Keyword(s):  
Community Structure ◽  
Microbial Community ◽  
Land Cover ◽  
Microbial Community Structure ◽  
Western Pacific ◽  
Land Cover Type ◽  
Pacific Region ◽  
Western Pacific Region ◽  
Cover Type ◽  
The Western Pacific

Land Cover Type and Fire in Portugal: Do Fires Burn Land Cover Selectively?

2005 ◽  
Vol 20 (6) ◽  
pp. 661-673 ◽  
Author(s):  
Maria C.S. Nunes ◽  
Maria J. Vasconcelos ◽  
José M.C. Pereira ◽  
Nairanjana Dasgupta ◽  
Richard J. Alldredge ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Type ◽  
Cover Type

scholarly journals The effect of land cover type and structure on evapotranspiration from agricultural and wetland sites in the Sacramento–San Joaquin River Delta, California

2018 ◽  
Vol 256-257 ◽  
pp. 179-195 ◽  
Author(s):  
Elke Eichelmann ◽  
Kyle S. Hemes ◽  
Sara H. Knox ◽  
Patricia Y. Oikawa ◽  
Samuel D. Chamberlain ◽  
...  
Keyword(s):  
Land Cover ◽  
River Delta ◽  
Land Cover Type ◽  
Cover Type ◽  
San Joaquin River
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