scholarly journals Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3456 ◽  
Author(s):  
Herrero ◽  
Southworth ◽  
Bunting ◽  
Kohlhaas ◽  
Child
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Wet Season ◽  
Rule Based ◽  
Modis Ndvi ◽  
In Situ Data ◽  
Over Time

Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass–shrub–tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

scholarly journals Assessing and Simulating Impacts of Land Use Land Cover Changes on Land Surface Temperature in Mymensingh City, Bangladesh

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Tahmid Anam Chowdhury ◽  
◽  
Md. Saiful Islam ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Bare Soil ◽  
Land Use Land Cover ◽  
Remarkable Change

Urban developments in the cities of Bangladesh are causing the depletion of natural land covers over the past several decades. One of the significant implications of the developments is a change in Land Surface Temperature (LST). Through LST distribution in different Land Use Land Cover (LULC) and a statistical association among LST and biophysical indices, i.e., Urban Index (UI), Bare Soil Index (BI), Normalized Difference Builtup Index (NDBI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI), this paper studied the implications of LULC change on the LST in Mymensingh city. Landsat TM and OLI/TIRS satellite images were used to study LULC through the maximum likelihood classification method and LSTs for 1989, 2004, and 2019. The accuracy of LULC classifications was 84.50, 89.50, and 91.00 for three sampling years, respectively. From 1989 to 2019, the area and average LST of the built-up category has been increased by 24.99% and 7.6ºC, respectively. Compared to vegetation and water bodies, built-up and barren soil regions have a greater LST each year. A different machine learning method was applied to simulate LULC and LST in 2034. A remarkable change in both LULC and LST was found through this simulation. If the current changing rate of LULC continues, the built-up area will be 59.42% of the total area, and LST will be 30.05ºC on average in 2034. The LST in 2034 will be more than 29ºC and 31ºC in 59.64% and 23.55% areas of the city, respectively.

scholarly journals Seasonal Variation of Surface Temperature–Modulating Factors in the Sonoran Desert in Northwestern Mexico

2012 ◽  
Vol 51 (8) ◽  
pp. 1519-1530 ◽  
Author(s):  
Iryna Tereshchenko ◽  
Alexander N. Zolotokrylin ◽  
Tatiana B. Titkova ◽  
Luis Brito-Castillo ◽  
Cesar Octavio Monzon
Keyword(s):  
Surface Temperature ◽  
Sonoran Desert ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Threshold Value ◽  
Radiation Mechanism ◽  
Modis Ndvi ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Northwestern Mexico ◽  
Definition Of

AbstractThe authors explore a new approach to monitoring of desertification that is based on use of results on the relation between albedo and surface temperature for the Sonoran Desert in northwestern Mexico. The criteria of predominance of radiation by using the threshold value of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were determined. The radiation mechanism for regulating the temperature of the surface and the definition of threshold values for AVHRR and MODIS NDVI have an objective justification for the energy budget, which is based on the dominance of radiation surface temperature regulation in relation to evapotranspiration. Changes in the extent of arid regions with AVHRR NDVI of <0.08 and MODIS NDVI of <0.10 can be considered to be a characteristic in the evolution of desertification in the Sonoran Desert region. This is true because, in a certain year, the time span of the period when radiation factor predominates is important for the desertification process.

scholarly journals Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Long Zhao ◽  
Pan Zhang ◽  
Xiaoyi Ma ◽  
Zhuokun Pan
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Information Extraction ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Land Resource ◽  
Spectral Angle Mapper ◽  
Ndvi Time Series

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences.

scholarly journals SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

2020 ◽  
Vol 11 (2) ◽  
pp. 94-110 ◽  
Author(s):  
Syed Riad Morshed Riad Morshed ◽  
Md. Abdul Fattah ◽  
Asma Amin Rimi ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Moisture Index ◽  
Type Transformation ◽  
Spatial Environment ◽  
Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

Prevalence and seasonal variation of Fasciola hepatica in slaughtered cattle: the role of climate and environmental factors in Mexico

2021 ◽  
Vol 95 ◽  
Author(s):  
K. Hernández-Guzmán ◽  
P. Molina-Mendoza ◽  
J. Olivares-Pérez ◽  
Y. Alcalá-Canto ◽  
A. Olmedo-Juárez ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fasciola Hepatica ◽  
Temperate Climate ◽  
Wet Season ◽  
Dry Land

Abstract The objective of this study is to determine the prevalence of Fasciola hepatica infection in cattle slaughterhouses, as well as its association with climatic/environmental factors (derived from satellite data), seasonality and climate regions in two states in Mexico. Condemned livers from slaughtered animals were obtained from three abattoirs in the states of Puebla and Veracruz. The overall prevalence of the parasite in cattle between January and December of 2017 was 20.6% (1407 out of 6834); the highest rate of condemnation was observed in Veracruz (26.3%; tropical climate), and the lowest rate was found in Puebla (15.5%; temperate climate). The seasonal prevalence of fluke infection was 18.6%, 14.8% and 28.4% during the wet season, and 17.1%, 12.4% and 22.8% during the dry season in the three abattoir sites, located in the districts of Zacatlán, Teziutlán and Ciudad Alemán, respectively. Liver condemnations due to bovine fasciolosis were prevalent in the Zacatlán, Teziutlán and Ciudad Alemán districts during summer, autumn and summer, respectively. Using generalized estimating equations analysis, we determined six variables – rainfall (wet/dry), land surface temperature day, land surface temperature night, normalized difference vegetation index, seasonality and climate regions (temperate/tropical) – to be significantly associated with the prevalence of condemned livers. Climate region was the variable most strongly associated with F. hepatica infection (odds ratio (OR) 266.59; 95% confidence interval (CI): 241.90–353.34), followed by wet and dry seasons (OR 25.56; 95% CI: 20.56–55.67).

scholarly journals A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 389-394 ◽  
Author(s):  
R. Bala ◽  
R. Prasad ◽  
V. P. Yadav ◽  
J. Sharma
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sensor Data ◽  
Landsat 8 ◽  
Temperature Relation

<p><strong>Abstract.</strong> The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.</p>

scholarly journals THE GEO-SPATIAL APPROACH TO DETECT THE CHANGE IN VEGETATION AND LAND SURFACE TEMPERATURE (LST) AFTER FORMATION OF ROHINGYA SETTLEMENTS IN BANGLADESH

2021 ◽  
Vol 12 (2) ◽  
pp. 288-241
Author(s):  
Mahdi Mansur Mahi ◽  
Md. Shahriar Sharif ◽  
Rhyme Rubayet Rudra ◽  
Md. Nazmul Haque
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Vegetation Density ◽  
Tree Plantation

The goal of this study is to examine the effects of Rohingya Influx specially on vegetation land cover and LST in Teknaf Peninsula, Cox’s Bazar, Bangladesh over time. For doing so, the research followed three steps. Firstly, the primary and secondary data were collected from prescribed sources like LANDSAT 8 images from Earth Explorer (USGS) and the Shapefiles were collected from secondary sources. Then, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) functions are explored in geospatial environment to assess the effect of deforestation on the region. Finally, A correlation is shown between LST and NDVI for making a decision from the environmental perspective. The findings state that, the region around the Rohingya Camps progressively lost its vegetation density as a result of increasing deforestation. According to this analysis, there was 87.87 % vegetation cover in 2013, which gradually decreased before the Rohingya Invasion in 2017. After the incident in 2018, vegetation cover drops to 75.67 %. Similarly, area with no vegetation increased more rapidly than others. The outcome showed that the transition in land cover was quicker and more noticeable in recent time. As a result, the LST has been increasing over the years. According to the study, there were around 8.71 % of areas with high temperatures in 2013, which increased to 36.86 % in 2020. It indicates that a large quantity of vegetation has been lost as a result of deforestation, and the LST of this region has changed dramatically. Furthermore, data was examined by Union to assess the individual effect from 5 Rohingya camps, and it was discovered that the situation in Teknaf Union is terrible, while the situation in Baharchhara Union is comparably better. Finally, the results of the research encourage an extensive regional environmental policy to eradicate this problem. To recompense the loss of nature govt. and responsible department should take necessary steps like hill conservation or tree plantation.

scholarly journals SPATIOTEMPORAL ANALYSIS OF URBAN LAND COVER CHANGES IN KIGALI, RWANDA USING MULTITEMPORAL LANDSAT DATA AND LANDSCAPE METRICS

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 137-144
Author(s):  
T. Mugiraneza ◽  
J. Haas ◽  
Y. Ban
Keyword(s):  
Land Cover ◽  
Landscape Metrics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Early Warning Systems ◽  
Landscape Configuration ◽  
Support Vector ◽  
Landsat Data ◽  
Environment Impact ◽  
Landsat Images

Mapping urbanization and ensuing environmental impacts using satellite data combined with landscape metrics has become a hot research topic. The objectives of the study are to analyze the spatio-temporal evolution of urbanization patterns of Kigali, Rwanda over the last three decades (from 1984 to 2015) using multitemporal Landsat data and to assess the associated environmental impact using landscape metrics. Landsat images, Normalized Difference Vegetation Index (NDVI), Grey Level Co-occurrence Matrix (GLCM) variance texture and digital elevation model (DEM) data were classified using a support vector machine (SVM). Eight landscape indices were derived from classified images for urbanization environment impact assessment. Seven land cover classes were derived with an overall accuracy exceeding 88&amp;thinsp;% with Kappa Coefficients around 0.8. As most prominent changes, cropland was reduced considerably in favour of built-up areas that increased from 2,349&amp;thinsp;ha to 11,579&amp;thinsp;ha between 1984 and 2015. During those 31 years, the increased number of patches in most land cover classes illustrated landscape fragmentation, especially for forest. The landscape configuration indices demonstrate that in general the land cover pattern remained stable for cropland but it was highly changed in built-up areas. Satellite-based analysis and quantification of urbanization and its effects using landscape metrics are found to be interesting for grassroots and provide a cost-effective method for urban information production. This information can be used for e.g. potential design and implementation of early warning systems that cater for urbanization effects.

scholarly journals An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

2020 ◽  
Vol 12 (23) ◽  
pp. 3880
Author(s):  
Chiman Kwan ◽  
David Gribben ◽  
Bulent Ayhan ◽  
Jiang Li ◽  
Sergio Bernabe ◽  
...  
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Land Cover Classification ◽  
Support Vector ◽  
Growth Monitoring ◽  
Land Cover Types ◽  
Imaging Detection ◽  
Better Than

Accurate vegetation detection is important for many applications, such as crop yield estimation, land cover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA).

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