scholarly journals Farmland Changes and Their Ecological Impact in the Huangshui River Basin

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1082
Author(s):  
Hui Wei ◽  
Changhe Lu ◽  
Yaqun Liu
Keyword(s):  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Negative Impact ◽  
Ecological Impact ◽  
Google Earth ◽  
Ecological Effect ◽  
Grain Production ◽  
Land Occupation ◽  
Qinghai Province

The Huangshui River Basin (HRB) is the main grain production and key implementation region of the “Grain for Green Program” (GGP) of Qinghai Province, and has experienced a quick urbanization during the last 20 years. Therefore, identifying the farmland change and its ecological effects is significant for farmland and ecological protection in the HRB. To this end, this study analyzed the farmland change between 2000 and 2018, based on 1 m spatial resolution farmland data visually interpreted from Google Earth high-resolution images, and then estimated its ecological impact based on the Normalized Difference Vegetation Index (NDVI) data of MODIS, using an ecological impact index of farmland change. The study found that: (1) The farmland area in the HRB decreased from 320.15 k ha in 2000 to 245.01 k ha in 2018, reduced by 23.47% or 1.48% per year, as mainly caused by ecological restoration and built-up land occupation; (2) from 2000 to 2018, the natural environment showed a greening trend in the HRB, with the mean NDVI increasing by 0.74% per year; (3) the farmland changes had a positive ecological effect, contributing 6.67% to the regional increase in the NDVI, but had a negative impact on grain production; (4) it is suggested to strengthen farmland protection by strictly controlling the urban land occupation and over-conversion of farmland in the HRB.

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 Spatial analysis of damaged vegetation in the Mianyuan River basin after the Wenchuan Earthquake

2015 ◽  
Vol 3 (5) ◽  
pp. 3225-3250
Author(s):  
H. Z. Zhang ◽  
J. R. Fan ◽  
X. M. Wang ◽  
T. H. Chi ◽  
L. Peng
Keyword(s):  
Slope Stability ◽  
Wenchuan Earthquake ◽  
River Basin ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Probabilistic Approach ◽  
Model Development ◽  
Slope Aspect ◽  
2008 Wenchuan Earthquake ◽  
Elevation Model

Abstract. The 2008 Wenchuan earthquake destroyed large areas of vegetation. Presently, these areas of damaged vegetation are at various stages of recovery. In this study, we present a probabilistic approach for slope stability analysis that quantitatively relates data on earthquake-damaged vegetation with slope stability in a given river basin. The Mianyuan River basin was selected for model development, and earthquake-damaged vegetation and post-earthquake recovery conditions were identified via the normalized difference vegetation index (NDVI), from multi-temporal (2001–2014) remote sensing images. DSAL (digital elevation model, slope, aspect, and lithology) spatial zonation was applied to characterize the survival environments of vegetation, which were used to discern the relationships between successful vegetation regrowth and environmental conditions. Finally, the slope stability susceptibility model was trained through multivariate analysis of earthquake-damaged vegetation and its controlling factors (i.e. topographic environments and material properties). Application to the Subao River basin validated the proposed model, showing that most of the damaged vegetation areas have high susceptibility levels (88.1% > susceptibility level 3, and 61.5% > level 4). Our modelling approach may also be valuable for use in other regions prone to landslide hazards.

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.

scholarly journals Spatio-Temporal Analysis of Vegetation Dynamics as a Response to Climate Variability and Drought Patterns in the Semiarid Region, Eritrea

2019 ◽  
Vol 11 (6) ◽  
pp. 724 ◽  
Author(s):  
Simon Measho ◽  
Baozhang Chen ◽  
Yongyut Trisurat ◽  
Petri Pellikka ◽  
Lifeng Guo ◽  
...  
Keyword(s):  
Climate Variability ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Negative Impact ◽  
Pearson Correlation ◽  
Temporal Analysis ◽  
Semiarid Region ◽  
Ecological Zones ◽  
Spatio Temporal

There is a growing concern over change in vegetation dynamics and drought patterns with the increasing climate variability and warming trends in Africa, particularly in the semiarid regions of East Africa. Here, several geospatial techniques and datasets were used to analyze the spatio-temporal vegetation dynamics in response to climate (precipitation and temperature) and drought in Eritrea from 2000 to 2017. A pixel-based trend analysis was performed, and a Pearson correlation coefficient was computed between vegetation indices and climate variables. In addition, vegetation condition index (VCI) and standard precipitation index (SPI) classifications were used to assess drought patterns in the country. The results demonstrated that there was a decreasing NDVI (Normalized Difference Vegetation Index) slope at both annual and seasonal time scales. In the study area, 57.1% of the pixels showed a decreasing annual NDVI trend, while the significance was higher in South-Western Eritrea. In most of the agro-ecological zones, the shrublands and croplands showed decreasing NDVI trends. About 87.16% of the study area had a positive correlation between growing season NDVI and precipitation (39.34%, p < 0.05). The Gash Barka region of the country showed the strongest and most significant correlations between NDVI and precipitation values. The specific drought assessments based on VCI and SPI summarized that Eritrea had been exposed to recurrent droughts of moderate to extreme conditions during the last 18 years. Based on the correlation analysis and drought patterns, this study confirms that low precipitation was mainly attributed to the slowly declining vegetation trends and increased drought conditions in the semi-arid region. Therefore, immediate action is needed to minimize the negative impact of climate variability and increasing aridity in vegetation and ecosystem services.

scholarly journals Detecting Patterns of Vegetation Gradual Changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework

2019 ◽  
Vol 11 (21) ◽  
pp. 2475 ◽  
Author(s):  
Ju Wang ◽  
Yaowen Xie ◽  
Xiaoyun Wang ◽  
Jingru Dong ◽  
Qiang Bie
Keyword(s):  
River Basin ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Logistic Function ◽  
Linear Pattern ◽  
Shiyang River Basin ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Consistency ◽  
Almost All

A lot of timeseries satellite products have been well documented in exploring changes in ecosystems. However, algorithms allowing for measuring the directions, magnitudes, and timing of vegetation change, evaluating the major driving factors, and eventually predicting the future trends are still insufficient. A novel framework focusing on addressing this problem was proposed in this study according to the temporal trajectory of Normalized Difference Vegetation Index (NDVI) timeseries of Moderate Resolution Imaging Spectroradiometer (MODIS). It divided the inter-annual changes in vegetation into four patterns: linear, exponential, logarithmic, and logistic. All the three non-linear patterns were differentiated automatically by fitting a logistic function with prolonged NDVI timeseries. Finally, features of vegetation changes including where, when and how, were evaluated by the parameters in the logistic function. Our results showed that 87.39% of vegetation covered areas (maximum mean growing season NDVI in the 17 years not less than 0.2) in the Shiyng River basin experienced significant changes during 2001–2017. The linear pattern, exponential pattern, logarithmic pattern, and logistic pattern accounted for 36.53%, 20.16%, 15.42%, and 15.27%, respectively. Increasing trends were dominant in all the patterns. The spatial distribution in both the patterns and the transition years at which vegetation gains/losses began or ended is of high consistency. The main years of transition for the exponential increasing pattern, the logarithmic increasing pattern, and the logarithmic increasing pattern were 2008–2011, 2003–2004, and 2009–2010, respectively. The period of 2006–2008 was the foremost period that NDVIs started to decline in Liangzhou Oasis and Minqin Oasis where almost all the decreasing patterns were concentrated. Potential disturbances of vegetation gradual changes in the basin are refer to as urbanization, expansion or reduction of agricultural oases, as well as measures in ecological projects, such as greenhouses building, afforestation, grazing prohibition, etc.

scholarly journals Space-time variability of the Roncador river basin in the change of land use and cover and its correlation with climatic variables

2019 ◽  
Vol 35 (4) ◽  
Author(s):  
Raquel de Oliveira Santos ◽  
Rafael Coll Delgado ◽  
Marcos Gervasio Pereira ◽  
Leonardo Paula de Souza ◽  
Paulo Eduardo Teodoro ◽  
...  
Keyword(s):  
River Basin ◽  
Air Temperature ◽  
Vegetation Index ◽  
Space Time ◽  
Google Earth ◽  
Climatic Variables ◽  
Computer Application ◽  
Coefficient Of Determination ◽  
Native Forest ◽  
Time Dynamics

The objective of this study was to evaluate the space-time dynamics of the soil use and occupation of the Rio Roncador river basin between 1985 and 2010. The scenes were classified by two methods (partially unsupervised - K-Means and supervised - Maximum likelihood), the Thematic Mapper sensor products on the LANDSAT 5 orbital platform were used for both images of a 25-year time series (1985 to 2000). In order to measure the accuracy of the field the computer application Google Earth was used, in which nine classes (urban area, agricultural area, pasture, exposed soil, native forest, secondary vegetation, mangrove, altitude field and water) were collected. A multiple linear regression was performed, correlating the Normalized Difference Vegetation Index - mean NDVI (dependent variable) with the independent climatic variables (global solar radiation - MJm-2day-1, average air temperature - °C, relative humidity -%, evapotranspiration - mm d-1, and rain - mm). According to the general classification by Kappa parameter of the images for 2005 and 2010, they were identified as very good (68% and 74%). These results confirm that the Roncador River Basin is undergoing transformation in its landscape, with an average reduction of -49% in native vegetation areas due to the increase in urban areas (25%) and agriculture (31%). The statistical analysis showed that rainfall and air temperature were the only variables that presented significant sigma (0.04) and (0.02). The obtained coefficient of determination indicated that 47% of the variations of the "vegetation index" are explained by the environmental variables.  

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.

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