MODIS NDVI Images Research Articles

Decadal Variations in Area under Different Soil Erosion Classes using RUSLE and GIS: Case Studies of River Basins from Western and Eastern Arunachal Pradesh

Abstract This study provides a comparative evaluation of spatio-temporal distribution of soil erosion in Western (Mago Basin) and Eastern (Dibang Basin) basins of Arunachal Pradesh, India as these two basins are vulnerably exposed to soil erosion due to its topographical characteristics of mountainous steep slope and experiences heavy rainfall. The study was carried out for a ten-years period (2003 to 2014) using RUSLE model which encompasses five important factors contributing to soil erosion. Rainfall erosivity (R factor) map was calculated using Climate Prediction Center gridded precipitation. Soil map and soil samples were used to analyze soil erodibility (K factor) map. Slope length and slope steepness (LS factor) maps were computed from SRTM DEM (30 m resolution). MODIS NDVI images were used to obtain cover management (C factor) map. Landuse Landcover map was used to obtained support practice (P factor) map. Higher value in rainfall erosivity and cover management factor was observed in Mago basin which contributed to higher average annual soil loss of 17.423 t ha−1 y−1 in Mago basin and 5.461 t ha−1 y−1 in Dibang basin, whereas the other three factor values were almost the same. The spatial maps showed 56.65% of Mago basin area and 76.27% of Dibang basin area was under the class of slight erosion, with the remaining areas of moderate to severe erosion risk for both the basins. Temporal average soil erosion in Mago basin varied within moderate to very high erosion classes whereas Dibang basin erosion classes varied from slight to moderate. The temporal trend line showed that the overall soil erosion was increasing at an alarming rate for Mago basin whereas a slight increase in Dibang basin was observed.

Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing

ABSTRACT In this study, the relationship between vegetation dynamics and atmospheric patterns over Iceland from 2001-2019 has been assessed using remote sensing. This study is based on MODIS NDVI images, NCEP/NCAR reanalysis dataset and values of the North Atlantic Oscillation (NAO). The results show that the vegetation coverage in Iceland reaches a maximum in the period from the middle of July to late August, with an average of about 65% of the total area (66858 km2). There is not a strong relationship between NAO phases and the occurrence of the dry (less vegetation) or green months, which means that a dry year can be accompanied by a negative NAO phase (i.e. July 2009 with NDVI anomaly=-3.35 and NAO =-2.15) or with a positive phase (September 2005 with NDVI anomaly=-2.23 and NAO=0.63). The most important factor influencing the occurrence of months with denser/less dense vegetation is shifting west/eastward of Greenland Low height (GL), which is accompanied by a green/dry month in Iceland, respectively. The knowledge of this can help us to understand the variations in Iceland's vegetation and also enables us to have a closer look at the impact of changes in global atmospheric patterns on the vegetation productivity in Iceland.

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.

Spatial and temporal changes in inter-annual and seasonal NDVI in the Qinling mountains of China

As an important ecological barrier in central China, the Qinling Mountains have received more attention in response to global or regional climate change. Spatial and temporal changes of inter-annual and seasonal NDVI were analysed after extraction of the vegetation growth season, based on MODIS NDVI images and linear trend analysis. The results showed that: (1) the NDVI value and vegetation cover level of the Qinling Mountains were high. The NDVI showed significant annual and seasonal increases, in which the summer value contributed the most to the inter-annual value, followed by spring and autumn. In terms of the change trend, the NDVI showed a linear increase in annual and seasonal data, in which the spring growth rate contributed the most to the inter-annual value, followed by autumn and summer. (2) The spatial distribution of the linear change trend of the NDVI in the inter-annual and seasonal data was mainly increased, but the area of reduction was also large. The area of reduced NDVI was mainly distributed in the middle and western area with middle and high elevations, as well as urban neighbourhoods with intensive human activities, such as the Hanzhong basin, the Ankang basin, and the Shangluo basin. The rate of change in NDVI in human activity areas was higher than in the middle and higher elevation areas. The change may be driven by human activities in the former and by climate change in the latter. Monitoring of the vegetation ecological system in the Qinling Mountains should be strengthened to understand the change process, as well as to provide a scientific basis when making policies for regional ecological environmental protection.

Vegetation productivity trends in response to urban dynamics

Urbanization is a global phenomenon with still unknown consequences for vegetation dynamics of urban ecosystems, especially in subtropical areas of developing countries. In this paper we analyze the vegetation productivity trend associated to urban densification and urban expansion during the last decade, in twelve cities of northern Argentina. We used time series analysis of MODIS-NDVI images to reconstruct the phenological patterns to retrieve a productivity trend under three spatial classes of urban dynamics: (1) urban, (2) expansion and (3) periphery. Our results show that trends in vegetation productivity are more associated to the environmental characteristics (basal productivity and climate) than to the land cover class. The average trend in productivity in urban areas ranged between −2.54% year−1 (Metán) and −0.22% year−1 (Concepción). In contrast, the range was much tighter between classes; it was −1.37% year−1 in urban areas and −1.21% in the periphery. In this sense we found significant differences between cities, but no significant differences were observed between classes. Urban growth and urban expansion patterns found in our study suggest the system dynamics is dominated by sprawl patterns rather than by a homogeneous densification. Related to this phenomenon, our results dismissed the idea of urban expansion as the main factor affecting vegetation phenology and supported the hypothesis of regional warming as an explanation for the decrease in vegetation productivity, probably due to the decrease of water balance in arid regions.

Time–space characterization of vegetation in a semiarid mining area using empirical orthogonal function decomposition of MODIS NDVI time series

There are always different patterns of temporal variation determined by the heterogeneity of the surface coverage and influence factors at regional scale. Identification of the temporal patterns and positioning their spatial distribution are critical for rational use of nature resources and ecological protection. In this study, the empirical orthogonal function decomposition (EOFD) was proposed for the time–space characterization of vegetation in a semiarid mining area using 206 MODIS-NDVI images with a spatial resolution of 250 m and temporal resolution of 16 days. Four kinds of temporal patterns of the vegetation were decomposed, which are annual cycle, desertification, increase and degradation. Moreover, the spatial distributions of the decomposed temporal patterns were mapped. As a result, a much stronger relationship was found between the NDVI and rainfall, which is the key factor controlling the vegetation growth in the study area. The spatial distribution of the impacts on the vegetation from the underground coal mining, open-pit mining, and vegetation reconstruction was identified with the EOFD method and verified by site investigations. These results demonstrated that the EOFD method can decompose the whole temporal variation of vegetation into several different patterns of variations and help to distinguish between the influences from natural factors or anthropogenic activities. The EOFD method is determined by the temporal variations of the study area without human intervention; therefore, it is a promising methodology for time–space characterization with the aid of satellite images time series at a large scale.

A method to improve the spatial features of NDVI data series

A multi-step methodology is presented to improve the spatial features of low resolution NDVI data series by the use of single-date multispectral higher resolution scenes. The first step consists in the application of the Sequential Maximum Angle Convex Cone (SMACC) algorithm to spectrally decompose the high spatial resolution scene. The abundance images obtained are then used to identify spatially variable NDVI endmembers, which are recombined with the abundance images to produce synthetic NDVI imagery with improved spatial features. A test performed using three Landsat TM and MODIS NDVI images taken over Tuscany (Central Italy) in 2000 supports the potential of the methodology and indicates some limits which are critically discussed. Keywords: NDVI, SMACC, endmembers, synthetic imagery.

Critical Area Identification of Potential Soil Loss in a Typical Watershed of the Three Gorges Reservoir Region

Identifying critical soil loss-prone areas is necessary to better control soil loss in the Xiangxi watershed, the river basin nearest the Three Gorges Dam. A crucial element of this scheme is the development of a risk assessment model that can identify critical potential soil loss areas for land use prioritization and soil conservation. An assessment model for the risk of potential soil loss based on the revised universal soil loss equation and sediment delivery ratio was developed in this work. The proposed model consists of five multiplied factors: the rain and runoff erosivity, soil erodibility, slope steepness and length, vegetation cover, and sediment delivery. The risk of potential soil loss in the Xiangxi watershed was assessed using the developed model integrated with the ArcGIS platform along with precipitation data, soil data, DEM, and MODIS NDVI images. The risk values ranged from 0 to 478.18, and were categorized into four classes. The classification showed that critical and sub-critical areas accounted for 4.48% and 6.05% respectively, of the entire Xiangxi watershed area. The results of the identification of critical and sub-critical areas were verified by analyzing the relationship between the variations of the agricultural land area and those of sediment discharge. Statistical relationship analysis between the distribution of critical/sub-critical areas and two parameters (the cell distance to the nearest river channel and the slope) showed that the critical and sub-critical areas for potential soil loss in the Xiangxi watershed assemble in the zone with a cell distance below 2,000 m, or in the zone with slopes above 25°.