The effect of land-cover change on vegetation greenness-based satellite agricultural drought indicators: a case study in the southwest climate division of Indiana,USA

During the last decade, the use of the normalized difference vegetation index (NDVI) for drought monitoring applications has drawn many criticisms, mainly because a number of drivers such as land-cover/land-use change, pest infestation, and flooding may depress the NDVI, further causing false drought identification. In this study, the impacts of land-cover change on the NDVI-derived satellite drought indicator, the vegetation condition index (VCI), are presented. It was found that the VCI is sensitive to changes in land cover, especially deforestation, the land cover changes from evergreen and deciduous forests to other land-cover classes. However, because the scale of land-cover changes was very small across the study area, only trivial drought alerts were observed in the VCI-based drought maps during non-drought years. Because drought is a large-scale climate event, it is reasonable to neglect these alerts. Besides, when the VCI was averaged to climate division scale, the results obtained through the VCI method were in good agreement with those acquired by the meteorological data-based drought indices such as the Palmer drought severity index and standardized precipitation index.     

Predicting the spectral information of future land cover using machine learning

Application of machine learning models to study land-cover change is typically restricted to the change detection of categorical, i.e. classified, land-cover data. In this study, our aim is to extend the utility of such models to predict the spectral band information of satellite images. A Random Forests (RF)-based machine learning model is trained using topographic and historical climatic variables as inputs to predict the spectral band values of high-resolution satellite imagery across two large sites in the western United States, New Mexico (10,570 km²), and Washington (9400 km²). The model output is used to obtain a true colour photorealistic image and an image showing the normalized difference vegetation index values. We then use the trained model to explore what the land cover might look like for a climate change scenario during the 2061–2080 period. The RF model achieves high validation accuracy for both sites during the training phase, with the coefficient of determination (R²) = 0.79 for New Mexico site and R² = 0.73 for Washington site. For the climate change scenario, prominent land-cover changes are characterized by an increase in the vegetation cover at the New Mexico site and a decrease in the perennial snow cover at the Washington site. Our results suggest that direct prediction of spectral band information is highly beneficial due to the ability it provides for deriving ecologically relevant products, which can be used to analyse land-cover change scenarios from multiple perspectives.     

An automatic approach for urban land-cover classification from Landsat-8 OLI data

Due to the lack of clear shape, texture characteristics, and abundant spectral or spatial information of urban objects, traditional per-/sub-pixel analysis and interpretation for moderate-resolution-remote sensing data are always confused by such shortcomings as dependence on special skills, requirements to a priori knowledge and training samples, complex process, time-consuming and subjective operations, etc.. In order to alleviate such disadvantages, an automatic approach is proposed to classify vegetation, water, impervious surface areas (dark and bright), and bare land from the Operational Land Imager (OLI) sensor data of Landsat-8 in urban areas, which can be employed by common users to automatically obtain land-cover maps for urban applications. In detail, a preliminary classification result is achieved based on a new vegetation and water masking index (VWMI), the normalized difference vegetation index (NDVI), and a new normalized difference bare land index (NDBLI), which are acquired automatically from the remote-sensing images based on available knowledge of spectral properties. VWMI is designed to extract vegetation and water information together with a simpler threshold, while NDBLI is developed to identify dark impervious surfaces and bare land in this work. A modification strategy is further proposed to improve preliminary classification results by a linear model. For this purpose, a stable sample selection method based on the histogram is developed to select training samples from the preliminary classification result and to build a non-linear support vector machine (SVM) model to reclassify the classes. For validation and comparison purposes, the proposed approach is evaluated via experiments with real OLI data of two study areas, Nanjing and Ordos. The results demonstrate that the approach is effective in automatically obtaining urban land-cover classification maps from OLI data for thematic analysis.     

Effect of climate change on vegetation phenology of different land-cover types on the Tibetan Plateau

Research in vegetation phenology change has been one heated topic of current ecological and climate change study. The Tibetan Plateau, as the highest plateau of the earth, is more vulnerable and sensitive to climate change than many other regions. In this region, shifts in vegetation phenology have been intensively studied during recent decades, primarily based on satellite-retrieved data. In this study, we explored the spatiotemporal changes of vegetation phenology for different land-cover types in the Tibetan Plateau and characterized their relationship with temperature and precipitation by using long-term time-series datasets of normalized difference vegetation index (NDVI) from 1982 to 2014. Diverse phenological changes were observed for different land-cover types, with an advancing start of growing season (SOS), delaying end of growing season (EOS) and increasing length of growing season (LOS) in the eastern Tibetan Plateau where meadow was the dominant vegetation type, but with the opposite changes in the steppe and sparse herbaceous or sparse shrub regions which are mostly located in the northwestern and western edges of the Tibetan Plateau. Correlation analysis indicated that sufficient preseason precipitation may delay the SOS of evergreen forests in the southeastern Plateau and advance the SOS of steppe and sparse herbaceous or sparse shrub in relatively arid areas, while the advance of SOS in meadow areas could be related to higher preseason temperature. For EOS, because it is less sensitive to climate change than SOS, the response of EOS for different land-cover types to precipitation and temperature were more complicated across the Tibetan Plateau.     

An automated two-step NDVI-based method for the production of low-cost historical burned area map records over large areas

The recognition and understanding of long-term fire-related processes and patterns, such as the possible connection between the increased frequency of wildfires and global warming, requires the study of historical data records. In this study, a methodology was proposed for the automated production of long historical burned area map records over large-scale regions. The methodology was based on remotely sensed, high temporal resolution, normalized difference vegetation index (NDVI) data that could be easily acquired at medium or low spatial resolution. The proposed methodology was used to map the burned areas of the wildfires that occurred over the Peloponnese peninsula, Greece, during the summer of 2007. The method was built upon the NDVI data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Système Pour l’Observation de la Terre (SPOT)-VEGETATION. The higher spatial resolution data of MODIS resulted in higher burned area user accuracy (91.10%) and kappa (0.85) values than the respective ones for VEGETATION (79.29% and 0.77). The majority of classification errors were located along the perimeter of the burned areas and were mainly attributed to spatial resolution limitations of the remotely sensed data. The commission errors located away from the fire perimeter were primarily attributed to topographically shaded areas and land-cover types spectrally similar to burned areas. The omission errors resulted primarily from the small size and elongated shape of remote burned areas. Owing to their geometry, they have a high proportion of mixed pixels, whose spectral properties failed to meet the strict set of criteria for core fire pixels. The benefits of the proposed methodology are maximized when applied to data of the highest available spatial resolution, such as those collected by MODIS and the Project for On-Board Autonomy – Vegetation (PROBA-V) and when land-cover types spectrally similar to burned areas are masked prior to its application.     

Multispectral image data fusion under a Bayesian approach

This paper reviews the application of NOAA/NASA Pathfinder AVHRR Land (PAL) dataset (8 km) to detect land-cover change in China between 1982 and 1992. Changes in productivity, as indicated by a normalized difference vegetation index (NDVI), not changes in land-cover classifications are analysed. The research uses the change detection techniques of simple differencing, or univariate differencing, and standardised principal component analysis. Both techniques produce similar results which indicate that forest regions are decreasing in productivity while agricultural regions are increasing. The majority of pixels indicating changes are ones showing an increase in productivity and are clustered primarily in agricultural regions, especially the North China Plain, along with grasslands. The paper demonstrates the potential of using global-scale PAL data to monitor land-cover change in areas where official governmental data are either suspect or hard to acquire. China is an important area in which to analyse landcover change because little is known about recent changes and the region has a strong potential impact on global environmental change.     

Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta,Andhra Pradesh,India using multi-temporal satellite data

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990–2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of land-cover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005–2010.     

On the monitoring of land surface temperatures with the NOAA/AVHRR: Removing the effect of satellite orbit drift

To study the inter-annual variability of land surface temperature with NOAA Advanced Very High Resolution Radiometer (AVHRR) data, one must account for changes in the observed radiances due to changes in the observation time caused by satellite orbit drift (SOD). This study proposes a simple method to remove the SOD component from the AVHRR thermal IR. Spurious trends in these data should be corrected for to prevent their misidentification as real trends in the Earth's climate system and to infer more reliable conclusions from the inter-annual land surface variability studies, such as monitoring droughts. The proposed correction requires information on the observation solar zenith angle and normalized difference vegetation index for the region of interest.     

Binary images in seasonal land-cover change identification: a comparative study in parts of New South Wales,Australia

Numerous land-cover change detection techniques have been developed with varying opinions about their appropriateness and success. Decisions on the selection of the most suitable change detection method is often influenced by the study region landscape complexity and the type of data used for analysis. For different climatic areas, the method that suits best the seasonal land-cover change identification remains uncertain. In this study, 11 different binary change detection methods were tested and compared with respect to their capability in detecting land-cover change/no-change information in different seasons. The methods include image differencing (I_Diff), Improved image differencing (Imp_Diff), principal component image differencing (PC_Diff), vegetation index differencing (VI_Diff), change vector analysis (CVA), image ratioing (IR), improved image ratioing (Imp_IR), vegetation index image ratioing (VI_R), multi-date principal component analysis (PCA) using all bands (M_PCA), two-date bands PCA (B_PCA), and two-date vegetation index images PCA (VI_PCA). Multi-Date Thematic Mapper (TM) data were used for a wide set of change image generation. A relatively new approach was applied for optimal threshold value determination for the separation of change/no-change areas. Research results indicated that any methods involving TM Band 4 performed better than those using TM Band 3 or 5 on each of the change periods. However, irrespective of the method used, the accuracy assessment and change/no-change validation results from normalized difference vegetation index (NDVI)-based techniques outperformed all other tested techniques in the change detection process (overall accuracy >90% and kappa value >0.85 for all six change periods). The image differencing technique was found to be marginally better than PCA and IR in most cases and any of these techniques can be used for change detection. However, because of the simplistic nature and relative ease in identifying both negative and positive changes from difference images, the NDVI differencing technique is recommended for seasonal land-cover change identification in the study region.     

Analysis of multi-temporal SPOT NDVI images for small-scale land-use mapping

Land-use information is required for a number of purposes such as to address food security issues, to ensure the sustainable use of natural resources and to support decisions regarding food trade and crop insurance. Suitable land-use maps often either do not exist or are not readily available. This article presents a novel method to compile spatial and temporal land-use data sets using multi-temporal remote sensing in combination with existing data sources. Satellite Pour l'Observation de la Terre (SPOT)-Vegetation 10-day composite normalized difference vegetation index (NDVI) images (1998–2002) at 1km² resolution for a part of the Nizamabad district, Andhra Pradesh, India, were linked with available crop calendars and information about cropping patterns. The NDVI images were used to stratify the study area into map units represented by 11 distinct NDVI classes. These were then related to an existing land-cover map compiled from high resolution Indian Remote Sensing (IRS)-images (Liss-III on IRS-1C), reported crop areas by sub-district and practised crop calendar information. This resulted in an improved map containing baseline information on both land cover and land use. It is concluded that each defined NDVI class represents a varying but distinct mix of land-cover classes and that the existing land-cover map consists of too many detailed ‘year-specific’ features. Four groups of the NDVI classes present in agricultural areas match well with four categories of practised crop calendars. Differences within a group of NDVI classes reveal area specific variations in cropping intensities. The remaining groups of NDVI classes represent other land-cover complexes. The method illustrated in this article has the potential to be incorporated into remote sensing and Geographical Information System (GIS)-based drought monitoring systems.     

MODIS‐derived visible atmospherically resistant index for monitoring chaparral moisture content

Time series of normalized difference indices (NDIs) derived from MODIS surface reflectance data provide potentially useful information for monitoring fuel moisture content (FMC) for fire risk assessment. The visible atmospherically resistant index (VARI) and normalized difference water index (NDWI) were compared for monitoring live FMC of chaparral shrublands. Regression coefficients are encouraging given disparate spatial resolutions of ground‐based FMC measurements and MODIS‐derived NDIs. VARI exhibited greater temporal co‐variability (0.79>r ²<0.94) and spatial robustness with FMC than NDWI, even though the former is based solely on visible waveband reflectance data.     

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.     

Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images

Recent studies using low-resolution satellite time series show that the Sahelian belt of West Africa is witnessing an increase in vegetation cover/biomass, called re-greening. However, detailed information on local processing and changes is rare or lacking. A multi-temporal set of Landsat images was used to produce land-cover maps for the years 2000 and 2007 in a semi-arid region of Niger, where an anomalous vegetation trend was previously detected. Several supervised classification approaches were tested: spectral classification of single Landsat data, temporal classification of normalized difference vegetation index time series from Landsat images, and two-step classification integrating both these approaches. The accuracy of the land-cover maps obtained ranges between 80% and 90% overall for the two-step classification approach. Comparison of the maps between the two years indicates a stable semi-arid region, where some change in hot spots exists despite a generally constant level of rainfall in the area during this period. In particular, the Dallol Bosso fossil valley highlights an increase in cultivated land, while a decrease in herbaceous vegetation was observed outside the valley where rangeland is the predominant natural landscape.     

Major trends in the land surface phenology (LSP) of Africa,controlling for land-cover change

ABSTRACT

Monitoring land surface phenology (LSP) trends is important in understanding how both climatic and non-climatic factors influence vegetation growth and dynamics. Controlling for land-cover changes in these analyses has been undertaken only rarely, especially in poorly studied regions like Africa. Using regression models and controlling for land-cover changes, this study estimated LSP trends for Africa from the enhanced vegetation index (EVI) derived from 500 m surface reflectance Moderate-Resolution Imaging Spectroradiometer (MOD09A1), for the period from 2001 to 2015. Overall end of season showed slightly more pixels with significant trends (12.9% of pixels) than start of season (11.56% of pixels) and length of season (LOS) (5.72% of pixels), leading generally to more ‘longer season’ LOS trends. Importantly, LSP trends that were not affected by land-cover changes were distinguished from those that were influenced by land-cover changes such as to map LSP changes that have occurred within stable land-cover classes and which might, therefore, be reasonably associated with climate changes through time. As expected, greater slope magnitudes were observed more frequently for pixels with land-cover changes compared to those without, indicating the importance of controlling for land cover. Consequently, we suggest that future analyses of LSP trends should control for land-cover changes such as to isolate LSP trends that are solely climate-driven and/or those influenced by other anthropogenic activities or a combination of both.     

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km². AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.     

A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features

Relative radiometric normalization (RRN) with multi-sensor images is required for land-cover change detection. However, there are only a few RRN studies using multiple sensors. This article presents a new method for normalizing multiple images with pseudo-invariant features (PIFs) (MIPIF), which includes automatic selection and step-by-step optimization of PIFs. The normalized difference water index (NDWI) was used to select the original PIFs, and statistical rules with iterative control were used to fix the final PIFs. The method was tested on multiple images from a single sensor and multiple sensors in four groups of experiments with different land-cover areas. The results show that the normalization coefficients exceeded 0.90 at a significance level of 0.01. For the reference and normalized subject images, the root mean squared error (RMSE) values of the PIFs were much smaller than those of the reference and original subject images. The difference histogram curves of the reference and normalized subject images in the PIF pixels had roughly narrow normal Gaussian distributions with one pick around the zero position. The results demonstrated that the MIPIF method considers the physical definition of the PIFs and is effective, stable, and applicable for multiple images from a single sensor and from multiple sensors.     

NDVI-based land-cover change detection using harmonic analysis

This study presents a normalized difference vegetation index (NDVI)-based land-cover change detection method based on harmonic analysis. Multi-temporal NDVI data show seasonal variation characteristics in the time domain. A harmonic model represents the characterization of the temporal variability in a data set over a local region corresponding to a pixel through its harmonic components. In this research, annual land-cover change detection is performed by tracking the temporal dynamics through analysing harmonic components. A simple but effective noise reduction process is also proposed to provide the necessary high-quality data stream for the multi-temporal NDVI analysis based on the statistics of the observed oscillations. The proposed algorithm was tested and evaluated with the multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the MYD13Q1, 16 day L3 global 250 m SIN grid (v005) VI data set. The results indicate that the proposed algorithm provides a computationally inexpensive automatic method to monitor vegetation conditions and long-term land-cover change over large regions. The method described here is particularly useful for monitoring changes in well-established deciduous forests with developed canopies.     

Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests

Detection of land-cover changes through time can be complicated because of sensor-specific differences in spatial and spectral resolutions; classified land-cover changes can be due to either real changes on the ground or a switch in sensors used to collect data. This study focused on two objectives: (1) selecting the best predictor variables for the classification of semi-arid Zagros forests given the characteristics of the study area and available data sets and (2) evaluating the application of the random forest (RF) algorithm as a unified technique for the classification of data sets acquired from different sensors. Three images of the same study area were acquired from the Landsat-5 Thematic Mapper (TM) sensor in 2009, the Landsat-7 Enhanced Thematic Mapper (ETM+) sensor with Scan Line Corrector (SLC) in 1999 and the Landsat-2 Multispectral Scanner (MSS) sensor in 1975. Following image preprocessing, the RF algorithm was applied for variable selection and classification. A test of equivalence was used to compare the overall accuracy of the classified maps from the three sensors. Slope, normalized difference vegetation index (NDVI) and elevation were determined to be the most important predictor variables for all three images. High overall classification accuracies were achieved for all three images (97.90% for MSS, 95.43% for TM and 95.29% for ETM). The ETM- and TM-derived maps had equivalent overall accuracy and even significantly higher overall accuracy was obtained for the MSS-derived map. The post-classification comparison showed an increase in agriculture and a decrease in forest cover. The selected predictor variables were consistent with ecological reality and showed more details on the changes of the land-cover classes across biophysical variables of the study area through time.     

Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data

Estimating the water status of vegetation is one of the most important elements in assessing forest fire danger. In this paper, laboratory measurement confirmed a relationship between leaf water status and the normalized difference water index (NDWI), derived from near-infrared and shortwave-infrared spectral data. Two results were confirmed: (a) NDWI is related to equivalent water thickness, and, (b) in addition to NDWI, the quantity of leaf material must be known in order to estimate vegetation dryness. Based on these findings, the authors developed a vegetation dryness index (VDI) to estimate global vegetation water content. VDI values, calculated by using SPOT/VEGETATION data, were applied to data from a 1998 forest fire in the Russian Far East. This led to two results: (a) VDI was useful for detecting areas with a high potential for ignition, and (b) VDI may have been able to detect the fire-spread direction.