Changes In Vegetation Index Research Articles

IMPACT ASSESSMENT OF A MINE SUBSIDENCE ON NATIVE VEGETATION OF SOUTH EASTERN COALFIELDS, INDIA

Abstract. This study aims to evaluate the effect of underground coal mining subsidence on the growth of native vegetation. For this study, an underground coal mine of South Eastern Coalfields Limited (SECL), India was selected. Changes in vegetation indices were analyzed using three remote sensing data of the previous five years. Three period’s Landsat 8 OLI resolution image data were used to calculate Normalized Difference Vegetation Index (NDVI) of the years 2014, 2016 and 2018 in QGIS environment. The study showed that the local grassland and forest were affected by the mining exploitation and subsidence but those effects were not significant to have an adverse impact on the same. The short-term mining was having an impact on the vegetation growth but the effects gradually disappeared with the gradual stabilization of the subsided land and in absence of human interference, vegetation recovered well. In long-term, subsidence was not having a major impact on the vegetation growth. Thus, coal resources exploitation and subsidence of the said mine of SECL did not bring out an adverse impact on a wide range of forest and grassland ecosystems, and these ecosystems could carry the partial destruction and ultimately stabilized ecosystems by self-repair.

Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia

Abstract. Wadi Yalamlam is known as one of the significant wadis in the west of Saudi Arabia. It is a very important water source for the western region of the country. Thus, it supplies the holy places in Mecca and the surrounding areas with drinking water. The floristic composition of Wadi Yalamlam has not been comprehensively studied. For that reason, this work aimed to assess the wadi vegetation cover, life-form presence, chorotype, diversity, and community structure using temporal remote sensing data. Temporal datasets spanning 4 years were acquired from the Landsat 8 sensor in 2013 as an early acquisition and in 2017 as a late acquisition to estimate normalized difference vegetation index (NDVI) changes. The wadi was divided into seven stands. Stands 7, 1, and 3 were the richest with the highest Shannon index values of 2.98, 2.69, and 2.64, respectively. On the other hand, stand 6 has the least plant biodiversity with a Shannon index of 1.8. The study also revealed the presence of 48 different plant species belonging to 24 families. Fabaceae (17 %) and Poaceae (13 %) were the main families that form most of the vegetation in the study area, while many families were represented by only 2 % of the vegetation of the wadi. NDVI analysis showed that the wadi suffers from various types of degradation of the vegetation cover along with the wadi main stream.

Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data

Forest ecosystems in the American west have long been influenced by timber harvests and fire suppression, and recently through treatments that reduce fuel for fire management. Precisely quantifying the structural changes to forests caused by fuel treatments is an essential step to evaluate their impacts. Satellite imagery-derived vegetation indices, such as the normalized difference vegetation index (NDVI), have been widely used to map forest dynamics. However, uncertainties in using these vegetation indices to quantify forest structural changes have not been thoroughly studied, mainly due to the lack of wall-to-wall validation data. In this study we generated forest structural changes in aboveground biomass (AGB) and canopy cover as a result of fuel treatments using bi-temporal airborne light detection and ranging (LiDAR) data and field measurements in a mixed coniferous forest of northern Sierra Nevada, California, USA. These LiDAR-derived forest structural measures were used to evaluate the uncertainties of using Landsat-derived vegetation indices to quantify treatments. Our results confirmed that vegetation indices can accurately map the extents of forest disturbance and canopy cover changes caused by fuel treatments, but the accuracy in quantifying AGB changes varied by the pre-treatment forest densities and treatment intensity. Changes in vegetation indices had relatively weaker correlations (coefficient of determination < 0.45) to biomass changes in forests with sparse (AGB < 100 Mg/ha) or dense biomass (AGB > 700 Mg/ha), than in forests with moderate-density (AGB between 100 Mg/ha and 700 Mg/ha) before the disturbances. Moreover, understory treatments (canopy height < 10 m) were poorly indicated by changes in satellite-derived vegetation indices. Our results suggest that when relating vegetation indices to AGB changes, researchers and managers should be cautious about their uncertainties in extremely dense or sparse forests, particularly when treatments mainly removed small trees or understory fuels.

Land use and land cover changes and the link to land degradation in Arsi Negele district, Central Rift Valley, Ethiopia

Accurate information on land use and land cover change (LULCC) is critical for understanding the causes of change and for developing effective policies and strategies to slow and reverse land degradation. In Ethiopia, the speed and scale of LULCC has been accelerated in the last 3–4 decades of the 21st century. The objectives of this study were to assess: (i) the extent of LULCC and normalized difference vegetation index (NDVI) and the link to land degradation; (ii) the causes of LULCC and implication for climate change adaptation. Satellite images analysis was used to detect the change in area and vegetation index, and farmers’ perception to see the magnitude of LULCC dynamics and causes of deforestation. Correlations were made between vegetation index with dry season rainfall and temperature. The analysis of confusion matrix of LULC classification showed 87% accuracy with Kappa coefficient of 0.84. In the period 1986–2016, agriculture and settlement areas have increased by 250% and 618%, respectively. On the other hand, forests and woodlands have decreased by 72% and 84%, respectively. These were also validated with the farmers’ quantification results with similar trends. Different causes have played roles in the dynamics of LULCC. The results showed that vegetation dynamics vary both spatially and temporally against precipitation and temperature. This study informs the need to focus on halting deforestation and development of alternative energy sources. It further helps to design future land management directions, landscape based adaptation and rehabilitation strategies to be considered by policy makers.

Runoff and sediment load change and its influencing factors in the Xiaonanchuan River Basin, China

Based on the annual monitoring data of runoff and sediment and the Normalized Difference Vegetation Index (NDVI), the water and sediment change and the vegetation coverage dynamics in the Xiaonanchuan (XNC) River basin were analyzed. The main impacts factors of the water and sediment change were further identified by using the Multiple Linear Regression method. It is found that the runoff was increasing and the sediment was reducing in the XNC River basin during 2000 to 2013. Simultaneously, a significant upward trend was shown in NDVI. It indicates that the vegetation coverage was increasing and the natural ecological environment was improved in the basin. The quantitative analysis results show that the vegetation coverage is the dominant influencing factor of the water and sediment change in the XNC River basin. Therefore, under the condition of recent climate change, ecological restoration is one of the most effective way to prevent water and soil loss in arid area.

Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia

This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.

Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery

ABSTRACTRubber expansion has been widely explored at various scales due to its increasing environmental and socio-economic impact and the global demand for natural rubber products. However, accurate and up-to-date maps and large-scale monitoring of rubber plantation spatial distribution are not yet available. In this article, we developed a simple algorithm for rapidly and accurately mapping rubber plantations on Hainan Island, China, by combining survey samples and Landsat 8 Operational Land Imager data from 2014 in order to understand their spatial distribution. The results showed that rubber plantations are distinguishable from other land-cover types by band value changes, vegetation index changes, and phenological phase changes (defoliation and foliation) and can be accurately extracted from multi-temporal Landsat images using a decision tree method and Google Earth Engine. This method results in a high overall classification accuracy of 92.17% with a corresponding κ of 84.33%. Rubber plantations are concentrated in the northwest of Hainan Island and gradually decrease to the south and east. Most plantations are found in the 50–500-m elevation range, with few found outside this range. We believe that the proposed approach will have significant implications for mapping and monitoring rubber spatial distribution at regional scales.

近17年陕西榆林植被指数的时空变化及影响因素

近17年陕西榆林植被指数的时空变化及影响因素

High-resolution IKONOS satellite imagery for normalized difference vegetative index-related assessment applied to land clearance studies

High-resolution satellite imagery permits verification of human rights land clearance violations across international borders as a result of unstable regimes or socio-economic upheaval. Without direct access to these areas to validate allegations of human rights abuse, the use of remote sensing tools, techniques, and data is extremely important. Humanitarian assessment can benefit from software-based solutions, involving radiometrically calibrated normalized difference vegetation index and temporal change imagery. We discuss the introduction of a matrix filter approach for change detection studies to help assist rapid building detection over large search areas against a bright background to evaluate internally displaced people in the 2005 Porta Farm Zimbabwe clearances. Future wide-scale near real-time space-based monitoring with a range of digital filters would be of great benefit to international human rights observers and human rights networks.

Coastal Geomorphological and Land Use and Land Cover Study on Some Sites of Gulf of Kachchh, Gujarat, West Coast of India using Multi-Temporal Remote Sensing Data

The coastal rapid changes are monitored by Remote sensing (RS) and Geographical information (GIS) tools for the conservation and management. Aims of this paper are to monitor morphological, total vegetation, and water index changes in last two and half decades (1989, 2002, 2016) using Remote Sensing and GIS. The study area is rapidly changing at an alarming rate due to anthropogenic and natural activities. Morphological changes on study area are presenting major anthropogenic actions. Between 1989 and 2016 period, a tropical cyclone in 1998 caused a major change in mudflat area which reduced from 1143.97 to 459.07 km 2 . More than fifty percent changes were observed in the built-up area and salt pan which increased from 9.18 to 18.17 km 2 and 51.55 to 90.32 km 2 respectively. The mangrove class increased from 180.63 to 296.5 km 2 showing better restoration and conservation practices of Gujarat government. The NDVI supports these results by increasing maximum positive index value from 1989 to 2016. This study indicates that Gulf of Kachchh coastal is undergone excessive pressure due to the rapid development of the surroundings artificial landscape, where socio-economic factors lead to changes in the near environment, as well as proliferative activities of salt production affecting the quality in the coastal ecosystem. For coastal conservation, the government introduced socio-economic activities, designing strict management policies and awareness programs for local communities about the importance of coastal for protection and management.

Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China

Observed data showed the climatic transition from warm-dry to warm-wet in Xinjiang during the past 30 years and will probably affect vegetation dynamics. Here, we analyze the interannual change of vegetation index based on the satellite-derived normalized difference vegetation index (NDVI) with temperature and precipitation extreme over the Xinjiang, using the 8-km NDVI third-generation (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1982 to 2010. Few previous studies analyzed the link between climate extremes and vegetation response. From the satellite-based results, annual NDVI significantly increased in the first two decades (1981–1998) and then decreased after 1998. We show that the NDVI decrease over the past decade may conjointly be triggered by the increases of temperature and precipitation extremes. The correlation analyses demonstrated that the trends of NDVI was close to the trend of extreme precipitation; that is, consecutive dry days (CDD) and torrential rainfall days (R24) positively correlated with NDVI during 1998–2010. For the temperature extreme, while the decreases of NDVI correlate positively with warmer mean minimum temperature (Tnav), it correlates negatively with the number of warmest night days (Rwn). The results suggest that the climatic extremes have possible negative effects on the ecosystem.

Assessing the effects of the climate change on land cover changes in different time periods

The present research evaluated the relation between the normalized difference vegetation index (NDVI) changes and the climate change during 2000–2014 in Qazvin Plain, Iran. Daily precipitation and mean temperature values during 2015–2040 and 2040–2065 were predicted using the statistical downscaling model (SDSM), and these values were compared with the values of the base period (2000–2014). The MODIS images (MOD13A2) were used for NDVI monitoring. In order to investigate the effects of climate changes on vegetation, the relationship between the NDVI and climatic parameters was assessed in monthly, seasonal, and annual time periods. According to the obtained results under the B2 scenario, the mean annual precipitation at Qazvin Station during 2015–2040 and 2040–2065 was 6.7 mm (9.3%) and 8.2 mm (11.36%) lower than the values in the base period, respectively. Moreover, the mean annual temperature in the mentioned periods was 0.7 and 0.92 °C higher than that in the base period, respectively. Analysis of the correlations between the NDVI and climatic parameters in different periods showed that there is a significant correlation between the seasonal temperature and NDVI (P < 0.01). Moreover, the NDVI will increase 0.009 and 0.011 during 2015–2040 and 2040–2065, respectively.

NDVI Change Analysis and Damage Mapping of the Vilonia, Arkansas Tornado, 27 April 2014

ABSTRACTNormalized difference vegetation index (NDVI) change analysis is performed on Landsat-7 ETM+ imagery of an area in north-central Arkansas that was hit by an EF-4 tornado on 27 April 2014. The NDVI values of seventy-seven pixels containing National Weather Service damage reports from the tornado are analyzed and organized by the severity of the report on the Enhanced Fujita Scale. Decreases in NDVI are found to be common in pixels containing ≥ EF-2 damage. Furthermore, a strong correlation () is observed between pretornado NDVI and NDVI change (and therefore posttornado NDVI) for a given pixel when only considering pixels containing EF-3 or EF-4 damage reports. Using the linear equation that describes this correlation, a simple model is derived with the purpose of capturing pixels that fit this pattern and therefore might have sustained ≥ EF-3 damage. The model correctly classifies seventy of seventy-five (93 percent; two pixels with negative pretornado NDVI were excluded) reports as either ≤ EF-2 or ≥ EF-3 intensity. If this correlation exists for other tornadoes, a similar model can be used to map the most intense damage for analysis, research, or other purposes.

Detecting historical changes to vegetation in a Cambodian protected area using the Landsat TM and ETM+ sensors

The Phnom Kulen National Park (PKNP), Cambodia is faced with increasing environmental pressures and disturbances to vegetation. Changes in satellite vegetation indices (VIs) over time can be used to understand historical changes to vegetation in the PKNP provided images are calibrated to surface reflectance. Relative radiometric normalization (RRN) is one method to achieve this while the Landsat Climate Data Record (CDR) surface reflectance product provides an alternative. The objectives of this study are to: (1) determine the magnitude of differences between VIs produced using the CDR product and RRN images; (2) determine whether any differences in the VIs produced using the RRN and CDR images will impact on the areal extent of change in vegetation detected; and (3) determine the spatial and temporal extent of historical changes to vegetation in the PKNP. The choice of RRN or CDR had subtle but important impacts on the areal extent of detected changes to vegetation. The RRN images reduced inter-annual variations in vegetation response. The EVI was more resilient to residual atmospheric effects in the RRN and CDR images. Two relatively large regions of the PKNP were found to be relatively undisturbed. Recent changes to vegetation were found in areas that were previously undisturbed, and the proportion of the PKNP where changes were detected was increasing. Future work will include images from other sensors to reduce spatial and temporal gaps, and improve the temporal range of the methodology.

Dynamic analysis of ecological environment combined with land cover and NDVI changes and implications for sustainable urban–rural development: The case of Mu Us Sandy Land, China

Climate change and human activities have affected ecological environment in recent decades. This paper investigates the ecological environment change of the Mu Us sandy land based on a combined analysis of land cover and normalized difference vegetation index (NDVI) change trends. The results show that land cover changed moderately during 1990–2010, and areas experienced conversion accounted for 10% of the total land surface. The NDVI values show an evident increase at about 0.0076 year−1 during 2000–2014. This rate is lower than that of Shaanxi-Gansu-Ningxia region (0.0117 year−1), but greatly exceeds those of the Three-North Shelter Forest and the upper reaches of the Yellow River. Four categories of grids have been identified for accurate ecological restoration through analyzing the ecological performance index mainly based on land cover and NDVI indicators. The ecological restoration projects laid important foundation for ecological reconstruction in the Mu Us, while the wetting trend has been major climatic factors in vegetation improvement since 2000 (R = 0.687, P ≤ 0.01). However, both field investigation and statistical data analysis indicate that the ecological restoration in the Mu Us are experiencing increasing challenges, due to the growing human activities and still fragile eco-environment. Therefore, this paper suggests that urban–rural development and coal exploitation in this region should give full consideration to the carrying capacity of water and land resources. Overall, the evolving ‘science’ of ‘BIG DATA’ on coupled human-environment system can help to reveal the dynamics of ecological environment, and contribute valuable information for decision making concerning natural resources management and urban–rural development.

Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

Abstract. The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

Investigating Land Surface Temperature Changes Using Landsat Data in Konya, Turkey

The main purpose of this paper is to investigate multi-temporal land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) changes of Konya in Turkey using remotely sensed data. Konya is located in the semi-arid central Anatolian region of Turkey and hosts many important wetland sites including Salt Lake. Six images taken by Landsat-5 TM and Landsat 8- OLI satellites were used as the basic data source. These raw images were taken in 1984, 2011 and 2014 intended as long-term and short-term. Firstly, those raw images was corrected radiometric and geometrically within the scope of project. Three mosaic images were obtained by using the full-frame images of Landsat-5 TM / 8- OLI which had been already transformed comparison each other. Then, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) maps have been produced to determine the dimension of the drought. The obtained results showed that surface temperature rates in the basin increased about 5°C between 1984 and 2014 as long periods, increased about 2-3°C between 2011and 2014 as short periods. Meteorological data supports the increase in temperature.

Landuse and NDVI change analysis of Sperchios river basin (Greece) with different spatial resolution sensor data by Landsat/MSS/TM and OLI

The assessment of land use–land cover (LULC) and normalized difference vegetation index (NDVI) changes on hydrology is essential for the development of sustainable water resource strategies. This study focuses on Sperchios river catchment, a complex and very heterogeneous area made up of numerous land covers that are difficult to map due to spectral similarities. Difficulties such as multi-seasonal spectral variables corresponding to different stages of land cover phenological development were addressed by choosing an unsupervised classification algorithm called k-means in combination with the Corine land cover database and past scientific studies. Available remote sensing data included three Landsat images (MSS, TM, OLI) all of which were resampled to a common 30-m resolution. Final LULC classifications in the period 1972–2013 separated forest, agricultural land and heath land and detected unified bare with urban land and pastures with areas of low vegetation. The recorded results include a significant reduction (–80%) of vegetation (NDVI values), 28 and 26% reduction rates in forests and pastures-low vegetation, respectively, a strong increase (114%) in heath land and an 47% increase, as concerns the agricultural land. Using of random points estimated the classification accuracy to be 75.4 and 85.2% for LULC of 1972–2013, respectively. This particular study constitutes a preliminary stage of an attempted integrated basin management of Sperchios river intending to contribute to overall ecological quality assessment of Sperchios river.

Spectral Reflectance and Vegetation Index Changes in Deciduous Forest Foliage Following Tree Removal: Potential for Deforestation Monitoring

It is important to detect and quantify deforestation to guide strategic decisions regarding environment, socioeconomic development, and climate change. In the present study, we conducted a field experiment to examine spectral reflectance and vegetation index changes in poplar and locust tree foliage with different leaf area indices over the course of three sunny days, following tree removal from the canopy. The spectral reflectance of foliage from harvested trees was measured using an ASD FieldSpec Prospectroradiometer; synchronous meteorological data were also obtained. We found that reflectance in short-wave infrared and red-edge reflectance was more time sensitive after tree removal than reflectance in other spectral regions, and that the normalized difference water index (NDWI) and the red-edge chlorophyll index (CIRE) were the preferred indicators of these changes from several indices evaluated. Synthesized meteorological environments were found to influence water and chlorophyll contents after tree removal, and this subsequently changed the spectral canopy reflectance. Our results indicate the potential for such tree removal to be detected with NDWI or CIRE from the second day of a deforestation event.

Monitoring deforestation in Iran, Jangal-Abr Forest using multi-temporal satellite images and spectral mixture analysis method

Satellite remote sensing assessment provides a systematic and object means to quantify the rate and form of forest changes. The main objective of this study was to quantify the forest change and forest degradation in one of the oldest forests of Iran named Jangal-Abr Forest at the north of Shahrood city. Based on Landsat-TM and OLI images acquired from 1987 to 2013, four vegetation and soil fraction images were produced and compared to map the land-cover changes occurred over 26 years. Spectral mixture analysis (SMA) method was applied to estimate fraction images of the selected components over the study area. This technique examines the direction of physical changes between multi-temporal images. Then, change vector analysis (CVA) approach was used for detecting forest changes. Results revealed that during the last 26 years, 147.5 km2 of forests were degraded mostly with medium severity. Part of the deforestation related to the northern and eastern regions of the study area in the vicinity of residential areas in contrast, forest regrowth area during the study period was estimated to be 62 km2 typically classified with low and medium severity in the eastern parts of the study area. CVA demonstrates that deforestation in the Jangal-Abr area has significantly accelerated from 4.6 km2/year in period 1987–2000 to about 9.6 km2/year in period 2010–2013. The rate of regrowth has been decreased gradually from 3.3 km2/year in period 1 (1987–2000) to about 0.76 km2/year in period 3 (1987–2000). By comparison the results of SMA method with Normalized Difference Vegetation Index (NDVI) changes method, it was found that except for some negligible differences, the results of both methods was very closed in terms of both spatially and the area of degradation and regrowth regions.