scholarly journals Differing physiological and behavioral responses to anthropogenic factors between resident and non-resident African elephants at Mpala Ranch, Laikipia County, Kenya

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10010
Author(s):  
Sandy Oduor ◽  
Janine Brown ◽  
Geoffrey M. Macharia ◽  
Nicole Boisseau ◽  
Suzan Murray ◽  
...  
Keyword(s):  
Body Condition ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Herd Size ◽  
Positive Influence ◽  
Google Earth ◽  
Anthropogenic Factors ◽  
African Elephants ◽  
Management Intervention ◽  
Adrenal Glucocorticoid

Background Heterogeneous landscapes like those of Laikipia County, Kenya consist of a mosaic of land-use types, which may exert differential physiological effects on elephants that occupy and traverse them. Understanding behavioral and physiological states of wild African elephants in response to the challenges of living in human-dominated landscapes is therefore important for conservation managers to evaluate risks imposed by elephants to humans and vice versa. Several conservation physiology tools have been developed to assess how animals respond to both natural and anthropogenic changes, and determine biological impacts. This study investigated how migratory and avoidance behavioral to vehicle presence, and vegetation quality affected fecal glucocorticoid (GC) metabolite (FGM) concentrations in African elephants at Mpala Ranch, Laikipia County, Kenya. Methods The study compared adrenal glucocorticoid activity of resident elephants that live within Mpala (n = 57) and non-resident elephants whose space use patterns overlap several ranches (n = 99) in Laikipia County, Kenya. Fecal samples were collected for a 4-month period between April and August for analysis of FGM concentrations. Behavioral reactions to research vehicles and body condition also were assessed. Satellite images from Terra Moderate Resolution Imaging (MODIS MOD13Q1) were downloaded and processed using Google Earth Engine to calculate a Normalized Difference Vegetation Index (NDVI) as a measure of vegetation quality. Results As expected, there was a positive correlation between avoidance behavior to vehicle presence and FGM concentrations in both resident and non-resident elephants, whereas there was an inverse relationship between FGM concentrations and NDVI values. Our study also found a positive influence of age on the FGM concentrations, but there were no relationships between FGM and sex, social group type, herd size, and body condition. However, contrary to our expectations, resident elephants had higher FGM concentrations than non-residents. Discussion Findings reveal elephants with stronger avoidance responses to research vehicles and resident elephants with relatively smaller home ranges exhibited higher FGM concentrations within the Mpala Ranch, Kenya and surrounding areas. Higher vegetative quality within the ranges occupied by non-resident elephants in Laikipia may be one reason for lower FGM, and an indication that the non-residents are tracking better forage quality to improve energy balance and reduce overall GC output. Additionally, our study found a positive influence of age, but no other demographic variables on FGM concentrations. Finally, adrenal glucocorticoid activity was inversely related to vegetative quality. Our findings can help conservation managers better understand how behavior and environment influences the physiological states of African elephants, and how management intervention might mitigate negative human–elephant interactions.

scholarly journals A Study of Desertification Using Remote Sensing Techniques in Basra Governorate, South Iraq

2021 ◽  
pp. 912-926
Author(s):  
Fadel Abbas Zwain ◽  
Thair Thamer Al-Samarrai ◽  
Younus I. Al-Saady
Keyword(s):  
Remote Sensing ◽  
Land Degradation ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arid Zone ◽  
Sand Dunes ◽  
Risk Index ◽  
Anthropogenic Factors ◽  
Landsat Images

Iraq territory as a whole and south of Iraq in particular encountered rapid desertification and signs of severe land degradation in the last decades. Both natural and anthropogenic factors are responsible for the extent of desertification. Remote sensing data and image analysis tools were employed to identify, detect, and monitor desertification in Basra governorate. Different remote sensing indicators and image indices were applied in order to better identify the desertification development in the study area, including the Normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), Salinity index (SI), Top Soil Grain Size Index (GSI) , Land Surface Temperature (LST) , Land Surface Soil Moisture (LSM), and Land Degradation Risk Index (LDI) which was used for the assessment of degradation severity .Three Landsat images, acquired in 1973, 1993, and 2013, were used to evaluate the potential of using remote sensing analysis in desertification monitoring. The approach applied in this study for evaluating this phenomenon was proven to be an effective tool for the recognition of areas at risk of desertification. The results indicated that the arid zone of Basra governorate encounters substantial changes in the environment, such as decreasing surface water, degradation of agricultural lands (as palm orchards and crops), and deterioration of marshlands. Additional changes include increased salinization with the creeping of sand dunes to agricultural areas, as well as the impacts of oil fields and other facilities.

scholarly journals Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3170
Author(s):  
Zemeng Fan ◽  
Saibo Li ◽  
Haiyan Fang
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Regression Tree ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Driving Mechanisms

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

scholarly journals An Analysis of Vegetation Change Trends and Their Causes in Inner Mongolia, China from 1982 to 2006

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Baolin Li ◽  
Wanli Yu ◽  
Juan Wang
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Anthropogenic Factors ◽  
Simple Linear Regression ◽  
Ordos Plateau ◽  
Hetao Plain ◽  
Avhrr Data ◽  
The Relationship ◽  
Very High

This paper presents the vegetation change trends and their causes in the Inner Mongolian Autonomous Region, China from 1982 to 2006. We used National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to determine the vegetation change trends based on regression model by fitting simple linear regression through the time series of the integrated Normalized Difference Vegetation Index (NDVI) in the growing season for each pixel and calculating the slopes. We also explored the relationship between vegetation change trends and climatic and anthropogenic factors. This paper indicated that a large portion of the study area (17%) had experienced a significant vegetation increase at the 0.05 level from 1982 to 2006. The significant vegetation increase showed no positive link with precipitation and was mainly caused by human activities. In or to the south of Horqin Sandy Land, in the Hetao Plain, and at the northern foothills of the YinshanMountain, the significant NDVI increase trends were mainly caused by the increase of the millet yield per unit of cropland. In the east of Ordos Plateau, the significant NDVI increase trends were mainly determined by the fencing and planting of grasses and trees on grassland.

scholarly journals Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

2021 ◽  
Vol 13 (20) ◽  
pp. 4063
Author(s):  
Jie Xue ◽  
Yanyu Wang ◽  
Hongfen Teng ◽  
Nan Wang ◽  
Danlu Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Dynamic Change ◽  
Google Earth ◽  
Arid Areas ◽  
Climate Data ◽  
Northern Margin ◽  
The Past

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

scholarly journals A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012038
Author(s):  
Benedict ◽  
Lalu Muhamad Jaelani
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Image Data ◽  
Temporal Analysis ◽  
Google Earth ◽  
Vegetation Density ◽  
Angle Sensor ◽  
Study Results ◽  
Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

scholarly journals HazMapper: a global open-source natural hazard mapping application in Google Earth Engine

2021 ◽  
Vol 21 (5) ◽  
pp. 1495-1511
Author(s):  
Corey M. Scheip ◽  
Karl W. Wegmann
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Volcanic Eruptions ◽  
Relative Difference ◽  
Google Earth ◽  
Satellite Networks ◽  
Pyroclastic Flows ◽  
Hazard Mapping ◽  
Polar Regions ◽  
Google Earth Engine

Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

scholarly journals Mapping Forest Cover Changes using Sentinel-2A Imagery in the Municipality of Zubin Potok, Republic of Kosovo

2021 ◽  
Vol 17 (2) ◽  
pp. 105-119
Author(s):  
Ferat Krasniqi ◽  
Géza Király
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Normalized Difference Vegetation Index ◽  
Google Earth ◽  
Covered Area ◽  
Forest Coverage ◽  
Forest Cover Changes ◽  
Sentinel 2A ◽  
Forest Plans ◽  
Band Combinations

This research aimed to investigate the changes in forest cover, utilizing Sentinel-2A imagery data. Annual results of deforestation, non-forest, and forest area in the Municipality of Zubin Potok (Kosovo) between 2016 and 2017 were presented and analyzed by applying the image difference change detection method on a Normalized Difference Vegetation Index (NDVI) product derived for both years. The study reveals that forest coverage in this municipality has changed due to human activity (harvested and burnt forests). The footprint of changes was evidenced by using Sentinel 2A band combinations and very high resolution (VHR) images available in Google Earth (GE). From the overall forest-covered area of 24,873.61 hectares, the detected changes during the annual period are as follows: 24,423.57 ha or 98.19 % is mapped as forest, 113.75 hectares or 0.46 % as non-forest, and 336.77 or 1.35 % of the area forest is mapped as deforestation. These results can be used to identify human-made deforestation and to develop monitoring forest plans for the coming years.

scholarly journals Estimating Proportion of Vegetation Cover at the Vicinity of Archaeological Sites Using Sentinel-1 and -2 Data, Supplemented by Crowdsourced OpenStreetMap Geodata

2020 ◽  
Vol 10 (14) ◽  
pp. 4764 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Hazard Analysis ◽  
Archaeological Site ◽  
Google Earth ◽  
Archaeological Sites ◽  
Radar Images ◽  
Optical Images ◽  
Sentinel 2

Monitoring vegetation cover is an essential parameter for assessing various natural and anthropogenic hazards that occur at the vicinity of archaeological sites and landscapes. In this study, we used free and open access to Copernicus Earth Observation datasets. In particular, the proportion of vegetation cover is estimated from the analysis of Sentinel-1 radar and Sentinel-2 optical images, upon their radiometric and geometric corrections. Here, the proportion of vegetation based on the Radar Vegetation Index and the Normalized Difference Vegetation Index is estimated. Due to the medium resolution of these datasets (10 m resolution), the crowdsourced OpenStreetMap service was used to identify fully and non-vegetated pixels. The case study is focused on the western part of Cyprus, whereas various open-air archaeological sites exist, such as the archaeological site of “Nea Paphos” and the “Tombs of the Kings”. A cross-comparison of the results between the optical and the radar images is presented, as well as a comparison with ready products derived from the Sentinel Hub service such as the Sentinel-1 Synthetic Aperture Radar Urban and Sentinel-2 Scene classification data. Moreover, the proportion of vegetation cover was evaluated with Google Earth red-green-blue free high-resolution optical images, indicating that a good correlation between the RVI and NDVI can be generated only over vegetated areas. The overall findings indicate that Sentinel-1 and -2 indices can provide a similar pattern only over vegetated areas, which can be further elaborated to estimate temporal changes using integrated optical and radar Sentinel data. This study can support future investigations related to hazard analysis based on the combined use of optical and radar sensors, especially in areas with high cloud-coverage.

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