scholarly journals A simple approach for monitoring vegetation change using time series remote sensing analysis: A case study from the Thathe Vondo Area in Limpopo Province, South Africa

2021 ◽  
Vol 117 (7/8) ◽  
Author(s):  
Nndanduleni Muavhi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Simple Approach ◽  
Limpopo Province ◽  
Difference Image ◽  
Normalised Difference Vegetation Index ◽  
Gain And Loss ◽  
The Difference

This study presents a simple approach of spatiotemporal change detection of vegetation cover based on analysis of time series remotely sensed images. The study was carried out at Thathe Vondo Area, which is characterised by episodic variation of vegetation gain and loss. This variation is attributable to timber and tea plantations and their production cycles, which periodically result in either vegetation gain or loss. The approach presented here was implemented on two ASTER images acquired in 2007 and 2017. It involved the combined use of band combination, unsupervised image classification and Normalised Difference Vegetation Index (NDVI) techniques. True colour composite (TCC) images for 2007 and 2017 were created from combination of bands 1, 2 and 3 in red, blue and green, respectively. The difference image of the TCC images was then generated to show the inconsistencies of vegetation cover between 2007 and 2017. For analytical simplicity and interpretability, the difference image was subjected to ISODATA unsupervised classification, which clustered pixels in the difference image into eight classes. Two ISODATA derived classes were interpreted as vegetation gain and one as vegetation loss. These classes were confirmed as regions of vegetation gain and loss by NDVI values of 2007 and 2017. In addition, the polygons of vegetation gain and loss regions were created and superimposed over the TCC images to further demonstrate the spatiotemporal vegetation change in the area. The vegetation change statistics show vegetation gain and loss of 10.62% and 2.03%, respectively, implying a vegetation gain of 8.59% over the selected decade.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals RELATIONSHIP OF SPECTRAL REFLECTANCE AND NDVI TO SOME SOIL PROPERTIES OF BRICKS FACTORIES SOILS IN NAHRAWAN AREA,BAGHDAD IRAQ

2019 ◽  
Vol 50 (3) ◽  
Author(s):  
R. K. Abdullatiff
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Spectral Reflectivity ◽  
Tigris River ◽  
The Difference ◽  
Remote Sensing Techniques ◽  
The One ◽  
Relationship Of

A study was conducted to investigate the effect of the brick industry on the environmental system of these project soils of the brick factories in Alnahrawan district. Remote sensing techniques was used to study the relationship between the spectral reflectivity and the vegetative index on the one hand and some surface soil characters of the project and to determine the variation in vegetation cover for the same area and for two different periods.Ten sites were selected to study spectral reflectivity under similar geomorphological conditions near the brickworks project in the Anahrawan district with an area of 10,000 hectares. Soil samples were taken from the surface and at a depth of 0-30 cm. Some chemical and physical characters of research soil were analyzed in the soil department laboratories, college of Agriculture, Baghdad University.Several satellite images taken from the satellite Land sat (ETM) 2013 and another from same satellite in 1990 T.M to determining the change between the two periods. After obtaining remote sensing data (reflectivity and vegetation index).the correlation analysis was carried out between these data. It was observed that the soil salinity values were decreased due to the drainage that the area was confined between the Tigris River and the Diyala tributary which leads to good natural drainage.The attached tables indicate that thedigital numbers of the soil sampling sites in 2013 are highly significant correlated, While some of the characters did not show the use of this region industrially. After calculating the difference between the two images to determine the change. A 100% change was observed and the vegetation cover was sharply reduced between the two images. as well as the extension of the land of empty land, although these lands are still suitable for agriculture.

scholarly journals Temporal Influences of Vegetation Cover (C) Dynamism on MUSLE Sediment Yield Estimates: NDVI Evaluation

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2707
Author(s):  
David Gwapedza ◽  
Denis Arthur Hughes ◽  
Andrew Robert Slaughter ◽  
Sukhmani Kaur Mantel
Keyword(s):  
Sediment Yield ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Scale Validation ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Early Summer ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Normalised Difference Vegetation Index

Vegetation cover is an important factor controlling erosion and sediment yield. Therefore, its effect is accounted for in both experimental and modelling studies of erosion and sediment yield. Numerous studies have been conducted to account for the effects of vegetation cover on erosion across spatial scales; however, little has been conducted across temporal scales. This study investigates changes in vegetation cover across multiple temporal scales in Eastern Cape, South Africa and how this affects erosion and sediment yield modelling in the Tsitsa River catchment. Earth observation analysis and sediment yield modelling are integrated within this study. Landsat 8 imagery was processed, and Normalised Difference Vegetation Index (NDVI) values were extracted and applied to parameterise the Modified Universal Soil Loss Equation (MUSLE) vegetation (C) factor. Imagery data from 2013–2018 were analysed for an inter-annual trend based on reference summer (March) images, while monthly imagery for the years 2016–2017 was analysed for intra-annual trends. The results indicate that the C exhibits more variation across the monthly timescale than the yearly timescale. Therefore, using a single month to represent the annual C factor increases uncertainty. The modelling shows that accounting for temporal variations in vegetation cover reduces cumulative simulated sediment by up to 85% across the inter-annual and 30% for the intra-annual scale. Validation with observed data confirmed that accounting for temporal variations brought cumulative sediment outputs closer to observations. Over-simulations are high in late autumn and early summer, when estimated C values are high. Accordingly, uncertainties are high in winter when low NDVI leads to high C, whereas dry organic matter provides some protection from erosion. The results of this study highlight the need to account for temporal variations in vegetation cover in sediment yield estimation but indicate the uncertainties associated with using NDVI to estimate C factor.

Yield and Physiological Response of Common Bean in Three Semi-Commercial Farmers’ Fields in Limpopo Province

2019 ◽  
Author(s):  
K K Ayisi ◽  
L Munjonji ◽  
K V Masekela
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Physiological Response ◽  
Vegetation Index ◽  
Irrigation Management ◽  
Limpopo Province ◽  
Area Index ◽  
Spad Value ◽  
Normalised Difference Vegetation Index ◽  
Consumption Rates

Despite the importance of common bean (Phaseolus vulgaris) in human nutrition, its production in developing countries is far below consumption rates. A study was established to understand the yield performance and physiological response of common bean under three farmers’ field in the Limpopo Province. The results revealed significant variation in grain yield and biomass between the locations. Physiological parameters such as the Normalised Difference Vegetation Index (NDVI), Leaf Area Index (LAI), SPAD value and leaf gas exchanges were strongly influenced by the location, the crop growth stage and crop management. Dry bean crop performance was significantly lower at Masemola. Grain yield at Masemola was 30% less than that achieved at Dalmada and Mokopane. NDVI and LAI were also consistently lower at Masemola when compared to the other locations. This study revealed that poor yields by emerging farmers in dry areas are due to poor agronomic and irrigation management.

scholarly journals Interaction of Seasonal Sun-Angle and Savanna Phenology Observed and Modelled using MODIS

2019 ◽  
Vol 11 (12) ◽  
pp. 1398 ◽  
Author(s):  
Xuanlong Ma ◽  
Alfredo Huete ◽  
Ngoc Nguyen Tran
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Growing Season ◽  
The Sun ◽  
Space And Time ◽  
Ndvi Time Series ◽  
Angle Effect ◽  
Phenological Metrics

Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

scholarly journals Identifying Drought Status in Duhok Governorate (Iraqi Kurdistan Region) from 1998 through 2012 using Landsat Time Series Dataset

2020 ◽  
Vol 1 (1) ◽  
pp. 17-23
Author(s):  
Heman Gaznayee ◽  
Ayad Al-Quraishi
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Drought Severity ◽  
Kurdistan Region ◽  
Water Index ◽  
Iraqi Kurdistan ◽  
Study Results ◽  
Normalized Difference Water Index

Drought is a natural hazard that has a significant impact on the various aspects (i.e., economic, agricultural, environmental, and social). This study was carried out to evaluate drought severity and frequency during the growing season (April month) in Duhok Governorate (DUG), the Iraqi Kurdistan Region (IKR), for the period from 1998 through 2012 based on Landsat-based spectral indices. In this study, 15 mosaics assembled for 15 years consist of two scenes of Landsat time series, in a total of 30 TM and ETM+ images (WRS2: 170/34 & 170/35) acquired in 1998 to 2012. Annual precipitation data were collected from 18 meteorological stations distributed in the (DUG) for the study period. Drought status was investigated using the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI2), and Normalized Difference Water Index (NDWI). The study results showed an increase in drought severity and frequency in the (DUG) during the fifty years, particularly in 2000 and 2008. Whereas, the NDVI-based vegetation cover area has been reduced by 21.5% and 50.2% in 2000 and 2008, respectively. Additionally, the lowest values of the MSAVI2 (0.012 and 0.266) occurred in 2000 and 2008. As a result, the percentage of the vegetation cover reduction was 14.0% and 23.9%, respectively. Moreover, drop-in precipitation averages have occurred in those two drought years 2000 and 2008, as well as a significant reduction in the vegetation cover. On the other side, the most significant shrinkage in Duhok Dam (DUD) was by 1.13, 1.44, and 1.36 km2 in 2007, 2008, and 2009. It can be concluded that there are increasing drought episodes in the last two decades, declining in the water body surface area, and decreasing the precipitation averages in DUG from 1998 through 2012.

Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau

2015 ◽  
Vol 37 (1) ◽  
pp. 77 ◽  
Author(s):  
Haidi Zhao ◽  
Shiliang Liu ◽  
Shikui Dong ◽  
Xukun Su ◽  
Xuexia Wang ◽  
...  
Keyword(s):  
Human Disturbance ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Tibet Plateau ◽  
Desert Rangelands ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Qinghai Tibet Plateau

This paper evaluated changes in vegetation from 2000 to 2012, based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI), and related them to changes in estimates of human disturbance on the rangelands of the Qinghai-Tibet Plateau. The main rangeland types studied were desert, steppe and meadow with the latter mainly found in the southern and eastern parts of the study area. The results indicated that human disturbance was distributed mainly in the southern and eastern parts of the study area and corresponded with high NDVI values. The NDVI values showed an upward trend over the study period, with 28.5% of the study area exhibiting a significant increase. The proportion of rangelands that experienced a downward trend in NDVI increased as the level of human disturbance increased. Of the different rangeland types, meadow had the highest NDVI values, the greatest human disturbance, and the highest proportion of rangelands that exhibited a significant decrease in NDVI. Compared with areas with no human disturbance, meadow and steppe rangelands that experienced an increase in human disturbance had lower rates of increase in their NDVI values but, conversely, desert rangelands showed the opposite trend. In addition, it was found that precipitation had the dominant influence on NDVI values and that higher precipitation and slighter lower temperatures over the period of the study were related to an increase in NDVI values.

Trend analysis of medium- and coarse-resolution time series image data for burned area mapping in a Mediterranean ecosystem

2014 ◽  
Vol 23 (5) ◽  
pp. 668 ◽  
Author(s):  
Thomas Katagis ◽  
Ioannis Z. Gitas ◽  
Pericles Toukiloglou ◽  
Sander Veraverbeke ◽  
Rudi Goossens
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Trend Analysis ◽  
Vegetation Index ◽  
Probability Of Detection ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Mediterranean Ecosystem ◽  
Normalised Difference Vegetation Index ◽  
Induced Changes

In this study, the Breaks for Additive Seasonal and Trend (BFAST), a recently introduced trend analysis technique, was employed for the detection of fire-induced changes in a Mediterranean ecosystem. BFAST enables the decomposition of time series into trend, seasonal and noise components, resulting in the detection of gradual and rapid land cover changes. Normalised Difference Vegetation Index (NDVI) time series derived from the MODIS and VEGETATION (VGT) standard products were analysed. The time series decomposition resulted in the mapping of the burned area and the demonstration of the post-fire vegetation recovery trend. The observed gradual changes revealed an increase of NDVI values over time, indicating post-fire vegetation recovery. Spatial validation of the generated burned area maps with a higher resolution reference map was performed and probability statistics were derived. Both maps achieved a high probability of detection – 0.90 for MODIS and 0.87 for VGT – and a low probability of false alarms, 0.01 for MODIS and 0.02 for VGT. In addition, the Pareto boundary theory was implemented to account for the low-resolution bias of the maps. BFAST facilitated detection of fire-induced changes using image time series, without having to set thresholds, select specific seasons or adjust to certain land cover types. Further evaluation of the approach should focus on a more comprehensive assessment across regions and time.

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