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.

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.

scholarly journals Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications

2019 ◽  
Vol 8 (1) ◽  
pp. 43 ◽  
Author(s):  
Carlos Novillo ◽  
Patricia Arrogante-Funes ◽  
Raúl Romero-Calcerrada
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Type ◽  
Cover Type ◽  
Biogeographical Region ◽  
Cover Class ◽  
Statistical Trends ◽  
Earth System Models

The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to another, due to the change in vegetation and environmental conditions. Therefore, it is pertinent to understand the effect of climate change by land-cover type to understand the regions that are most vulnerable to climate change. Hence, in this study we analyzed the temporal statistical trends (2001–2016) of the MODIS13Q1 normalized difference vegetation index (NDVI) to explore whether there are differences, by land-cover class and phytoclimatic type, in mainland Spain and the Balearic Islands. We found 7.6% significant negative NDVI trends and 11.8% significant positive NDVI trends. Spatial patterns showed a non-random distribution. The Atlantic biogeographical region showed an unexpected 21% significant negative NDVI trends, and the Alpine region showed only 3.1% significant negative NDVI trends. We also found statistical differences between NDVI trends by land cover and phytoclimatic type. Variance explained by these variables was up to 35%. Positive trends were explained, above all, by land occupations, and negative trends were explained by phytoclimates. Warmer phytoclimatic classes of every general type and forest, as well as some agriculture land covers, showed negative trends.

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.

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 Parametric Methods to Multispectral Image Classification using Normalized Difference Vegetation Index

2019 ◽  
Vol 9 (2S) ◽  
pp. 611-615
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Algorithm ◽  
Urban Region ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Barren Soil ◽  
Day By Day ◽  
Manual Verification

The key to proper governance of the municipal bodies lies in knowing the geography of the region. The land cover of the region changes with respect to time. Also, there are seasonal variation in the layout of the waterbodies. Manual verification and surveying of these things becomes very difficult for want of resources. Remote Sensing Images play a very important role in mapping the land cover. In this paper, we consider such remotely sensed Multispectral Images, taken from Landsat-8. Parametric Machine learning algorithm like Maximum Likelihood Classifier has been used on those images to classify the land cover. Normalized Difference Vegetation Index (NDVI) has been calculated and integrates with the classification process. Four basic land covers have been identified for the purpose namely Water, Vegetation, Built-up and Barren soil. The area of study is Bangalore urban region where we find that the water bodies are decreasing day by day. An overall efficiency of 82% with a kappa hat 0f 0.67 has been achieved with the method. The user and the producer accuracies have also been tabulated in the Results part. The results show the land cover changes in a temporal manner

scholarly journals Competing Vegetation Structure Indices for Estimating Spatial Constrains in Carabid Abundance Patterns in Chinese Grasslands Reveal Complex Scale and Habitat Patterns

Insects ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 249 ◽  
Author(s):  
Noelline Tsafack ◽  
Simone Fattorini ◽  
Camila Benavides Frias ◽  
Yingzhong Xie ◽  
Xinpu Wang ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Spatial Scales ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Insect Communities ◽  
Landscape Characteristics ◽  
Species Abundances ◽  
Negative Impacts ◽  
Structure Indices

Carabid communities are influenced by landscape features. Chinese steppes are subject to increasing desertification processes that are changing land-cover characteristics with negative impacts on insect communities. Despite those warnings, how land-cover characteristics influence carabid communities in steppe ecosystems remains unknown. The aim of this study is to investigate how landscape characteristics drive carabid abundance in different steppes (desert, typical, and meadow steppes) at different spatial scales. Carabid abundances were estimated using pitfall traps. Various landscape indices were derived from Landsat 8 Operational Land Imager (OLI) images. Indices expressing moisture and productivity were, in general, those with the highest correlations. Different indices capture landscape aspects that influence carabid abundance at different scales, in which the patchiness of desert vegetation plays a major role. Carabid abundance correlations with landscape characteristics rely on the type of grassland, on the vegetation index, and on the scale considered. Proper scales and indices are steppe type-specific, highlighting the need of considering various scales and indices to explain species abundances from remotely sensed data.

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