NDVI Layer Research Articles

Spatial-temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean.

In Syria, soil erosion (SoEr) by water is one of the major challenges for sustainability. Thus, the main goals of this research were to evaluate the spatial changes of SoEr between 2000 and 2018 in the whole coastal basin (CB) of Syria and to provide a soil water erosion risk map for the study area. For this purpose, monthly rainfall data, the SoilGrids dataset, satellite image derived NDVI layers, and Digital Elevation Model (DEM) were collected. Through the integration of these layers into the Revised Universal Soil Loss Equation (RUSLE), under a Geographic Information System (GIS), soil loss was assessed. Also, the contribution of land cover changes and R factor on SoEr were evaluated. The outcomes of this assessment illustrated that the R factor ranged from 800 to 2600MJmmha-1h-1yr-1, while the soil erodibility factor (K factor) ranged from 0.048 to 0.035 ton ha MJ-1mm-1. The C factor (vegetation coverage) values ranged between 0.07 and 1 with a spatial average value of 0.44 for the 2000-2009 period and 0.39 for the 2010-2018 interval. The output of RUSLE revealed that average annual SoEr was of 21.35 ton ha-1 y-1 (± 38) for 2000-2009 and 22.47 ton ha-1 y-1(± 41.8) for 2010-2018. Interestingly, the increased SoEr caused by the R factor was dominant (34.65%), followed by changes in both C factor and R factor (13.34%). However, decrease of SoEr rates is due to the increase of the C factor accounting for 36.82% of the CB. The outcome of this research can provide constructive spatial insights for rehabilitation plans for the post-war phase of Syria.

EVALUATING THE IMPACT OF CLIMATE CHANGE ON THE URBAN ENVIRONMENT USING GEOSPATIAL TECHNOLOGIES IN BHUBANESWAR, INDIA

Abstract. Fast population growth is a significant contributor to worldwide land scarcity and has a long-term impact on the environment and climate. Rapid urbanisation along with industrialisation contributed to land transformation across the globe. Understanding, monitoring, and mitigating the environmental impacts of urbanisation are critical for sustainable development. Also, Rapid urbanisation has contributed significantly to regional and global temperature. Land surface temperature (LST) has long been regarded as a critical physical metric for analysing regional and global climate change and its effects on many ecosystems. Remote sensing is an effective method for monitoring and quantifying urbanisation and assessing its impact on regional heating. This paper examines the combined impact of land use and land cover (LULC) change and climatic variability in Bhubaneswar city using remote sensing, geographic information system and google earth engine. Images from the Landsat were utilised to prepare the LULC, LST and NDVI layer for 2001, 2010 and 2020. The random forest method of supervised classification was employed to estimate the LULC. According to this study, the impact of urbanisation shows that the built-up areas in Bhubaneswar City have increased by over 70% at the cost of agricultural land and vegetation cover. The finding also indicates the urban area has been expanded around 12 percent where LST has increased by 4°C in the last two decades. As a result, continuous monitoring of LULC dynamics is required to guide sustainable land-use decisions that promote environmental protection and economic development in this region. A multi-temporal environmental analysis of the region is critical for future environmental planning, such as the restoration of damaged areas, to be more effective and efficient. This study, thus, would be helpful for the administrators, urban planners, and policymakers for proper planning and sustainable development of Bhubaneswar City.

Detection The Spatiotemporal Association between Climatic Factors and Vegetation Cover (NDVI) Incorporate MODIS and TRMM Product (Case study: CharmahalvaBakhtiary Province of Iran) Behzad Amraei

Background and Objective: the climate factors are main determinant of vegetation spatiotemporal dynamics. A visual comparison of climate and vegetation on a global scale immediately reveals a strong association between climate and vegetation. The main object of this study is to reveal the spatiotemporal association between climatic factors and Modis derived NDVI in Charmahal&Bakhtiary province of Iran.Material and Methods: In this study, we use MOD13A3 of MODIS product as NDVI layer for study area. MOD11A2 as land surface temperature and mean monthly accumulative rainfall of synoptic station for study area during 2008 to 2018. We used the correlation analysis in 0.95 confident level (P_value =0.5) to reveal the spatiotemporal association between the NDVI and climatic factors.Results: The results indicated that during winter (December to March) the spatial distribution of NDVI is highly correlated with LST spatial distribution. In these months, the pixels which have the high value of NDVI are spatially associated with the pixels which have highest value of LST (6 to 12C°). In winter the spatial correlation between NDVI and LST is so high which is statistical significant in 0.95 confident level. In transient months such as May, October and November, the spatial correlation between NDVI and LST is falling to 0.30 to 0.35, which is not statistical, significant in 0.95 confident level. Finally, in summer season or warm months including Jun to September, we found the minimum spatial association between the NDVI and LST.Conclusion: we found that the maximum correlation between NDVI and LST simultaneously appears and no lag time has been observed. The spatial correlation of NDVI and monthly accumulative rainfall was statistical significant in spring season (April to Jun) by 1 month lag time but in other months we do not find any significant correlation between NDVI and rainfall.

Estimation of Semiarid Forest Canopy Cover Using Optimal Field Sampling and Satellite Data with Machine Learning Algorithms

Forest canopy cover represents different characteristics of forest stands. However, especially in semiarid forest, the estimation of canopy cover by field measurements is too expensive. Therefore, it is necessary to develop appropriate techniques to estimate forest canopy cover for forest management in semiarid areas. In this research, a robust procedure to estimate canopy cover using stratification field sampling and AdaBoostM1 machine learning algorithm with Landsat 8 OLI imagery is suggested. Approximately 29,000 ha of semiarid forest (Manesht- and Ghelarang-protected area) in west of Iran was selected as the study area. The unsupervised classification was used on NDVI layer extracted from OLI data, and Neyman method was applied for allocation, in stratified areas. The crown cover was measured in percentage in each plot. In inaccessible plots, the optical satellite imagery of Worldview-2 from Google Earth database was used (0.46 m spatial resolution). For the classification of canopy cover, the AdaBoostM1 algorithm with random forest classifier was trained by 75% split original data, while 25% remaining data were used for accuracy assessment using ROC curve, true positive (TP), false positive (FP), overall accuracy (OA) and kappa coefficient (K). The results showed the overall accuracy and kappa coefficient of 91% and 0.88, respectively. Based on the results, the methodology developed in this study is suitable to estimate canopy cover in semiarid forests.

Fractional Woody Cover Mapping of Texas Savanna at Landsat Scale

Texas savanna experienced substantial woody plant encroachment during the past several decades, resulting in habitat fragmentation and species loss. A detailed map of woody plant abundance and distribution in this area is critically needed for management purpose. This study endeavors to map the fractional woody cover of Texas savanna at Landsat scale (30 m) in an affordable way. The top of atmosphere reflectance, thermal bands, and NDVI layer of Web-Enabled Landsat Data (WELD) of 2012 were used as predictors, together with mean annual precipitation. Classification and Regression Trees (CART) were calibrated against training data of a whole range of fractional woody cover, which were derived from 1-m resolution digital orthophotos of 2012. Validation indicates a reasonable pixel level accuracy of the result fractional woody cover map, with a R-squared value of 0.45. Moreover, the result map clearly depicts the distribution of woody plants across the study area, as reflected by the orthophotos. Furthermore, this new map proves an improvement over the existing Landsat Vegetation Continuous Fields (VCF) tree cover product. The method developed here, combining remote sensing and statistical techniques, can contribute to savanna management through revealing the abundance and distribution of woody plants.

Land cover analysis based on descriptive statistics of Sentinel-2 time series data

In our paper we examined the opportunities of a classification based on descriptive statistics of NDVIthroughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 imagesin 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, inaccordance with Corine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overallaccuracy. We described the reasons for the disagreement in case of the most remarkable errors. .

Rice Equivalent Crop Yield Assessment Using MODIS Sensors’ Based MOD13A1-NDVI Data

This paper analyzes the site-specific infield fertilizer treatments, its application rate discrepancies and crop yield assessment using rice equivalent productivity in terms of their economic potential using MOD13A1-normalized difference vegetation index (NDVI) (a moderate-resolution imaging spectroradiomete derived 16 day composite normalized difference vegetation index product, with spatial resolution of 500 m). Soil quality and final crop yield response to nitrogen (N), phosphorus (P), and potassium (K) fertilizers were taken from selected experimental Agri-plots in the part of Kuru region in North India, to calculate site-specific rice equivalent yield (REY) in the crop year of 2005-2006. A 3 × 3 spatial window average pixel reflectance of the NDVI layer at the regional level was used to assess its relationship with contemporaneous cropping systems, such as rice-wheat, rice-sugarcane, and rice-onion in the study area. A robust linear regressive relationship of R 2 = 0.69, has been found between site-specific vegetation index values and calculated REY. Inverse distance weighted spatial interpolation method was used to analyze the spatial variability of three major fertilizer nutrients (NPK) response in the study area. The potassium nutrient availability showed high levels of spatial autocorrelation, suggesting that proper fertilizer application ratio with genuine irrigation practices may be used for underpinning of the high crop yield variety acreages. In order to strengthen the crop productivity, we have suggested the diversified triple-based cropping systems with satellite mounted sensor derived NDVI products as a holistic and feasible monitoring approach.

THE EUROSDR PROJECT "RADIOMETRIC ASPECTS OF DIGITAL PHOTOGRAMMETRIC IMAGES" – RESULTS OF THE EMPIRICAL PHASE

Abstract. This article presents the empirical research carried out in the context of the multi-site EuroSDR project "Radiometric aspects of digital photogrammetric images" and provides highlights of the results. The investigations have considered the vicarious radiometric and spatial resolution validation and calibration of the sensor system, radiometric processing of the image blocks either by performing relative radiometric block equalization or into absolutely reflectance calibrated products, and finally aspects of practical applications on NDVI layer generation and tree species classification. The data sets were provided by Leica Geosystems ADS40 and Intergraph DMC and the participants represented stakeholders in National Mapping Authorities, software development and research. The investigations proved the stability and quality of evaluated imaging systems with respect to radiometry and optical system. The first new-generation methods for reflectance calibration and equalization of photogrammetric image block data provided promising accuracy and were also functional from the productivity and usability points of view. The reflectance calibration methods provided up to 5% accuracy without any ground reference. Application oriented results indicated that automatic interpretation methods will benefit from the optimal use of radiometrically accurate multi-view photogrammetric imagery.

Identification of cropping activity in central and southern Queensland, Australia, with the aid of MODIS MOD13Q1 imagery

Cropping activity has an importance that extends beyond farming communities, to governments, private industries, and to scientific research. We have developed a remote sensing-based method to detect arable cropping activity in central and southern Queensland, Australia, based on time series analysis of the NDVI layer of MODIS-Terra MOD13Q1 (250-m pixel) imagery. Local auto-regression was used to characterise phenological cycles in the NDVI time series. A random forest was then used to model three broad classes of agricultural vegetation (Grazing, Summer Cropping and Winter Cropping), as a function of phenological metrics and the local variance of the NDVI time series. The latter was found to be the most important distinguishing factor between the three classes. Pixel-by-pixel predictions of the random forest were obtained bi-annually for the study area over a 10-year period. Moderate agreement was seen between the predictions of the random forest and (independent) visual interpretation of Landsat imagery (Cohen's index of agreement, κc, of 0.59). We then demonstrated how the random forest's predictions can be used to define the consistency of cropping activity at the spatial scale of an individual farm property; when compared with (independent) visual interpretation of Landsat imagery the agreement was also moderate (κc=0.68). In comparison with other crop-mapping approaches in the literature, our results have been achieved: (i) without restricting the method to annual NDVI time series; (ii) without assuming that the time series is regularly spaced and periodic; (iii) by considering only the 'greening-up’ phase of the phenological cycles.