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.     

The surface temperature-vegetation index space for land cover and land-cover change analysis

Most previous applications of coarse scale remote sensing data for land-cover mapping and land-cover change analysis were based on multi-temporal Normalized Difference Vegetation Index (NDVI) data. Recent empirical studies have documented that the combination of measurements of thermal infrared radiation (e.g., land brightness temperature, Ts) and vegetation indices (VI) improves the mapping and monitoring of land cover at broad scales. We investigate the biophysical justification for such a combination, using 10 years of Advanced Very High Resolution Radiometer (AVHRR) global area coverage ( GAC) data over the African continent. First, we review recent findings on the biophysical interpretation of the TS-VI space. Second, we analyse the seasonal time trajectories of different biomes in the TS-NDVI space. Third, we measure the relative role of multi-temporal NDVI and Ts data in the discrimination of land cover classes for land-cover mapping. Fourth, we analyse trajectories of land-cover change in the TS-NDVI space for study sites in three different environments. We illustrate the usefulness of the ratio between T_s and VI as an index to perform measurements in the Tj-NDVI space.     

Historical and future land-cover changes in the Upper Ganges basin of India

The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multi-temporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (–9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.     

Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion

Time series of vegetation index (VI) information derived from remote sensing is important for land-cover change detection. Although traditional change vector analysis (TCVA) is an effective method for extracting land-cover change information from a time series of VI data, it has the disadvantage of being too sensitive to temporal fluctuations in VI values. The method tends to overestimate the changes and confuse the actual land-cover conversion with the land covers that have not been converted but experience significant VI changes. Cross-correlogram spectral matching (CCSM) can tell the degree of shape similarity between VI profiles and be used to detect land-cover conversion. However, this method may omit some land conversion in which the before and after land-cover types are rather similar in VI profile shape but differ significantly in absolute VI values. This article proposes a new approach that improves TCVA with an adapted use of CCSM. First, TCVA is employed for preliminary detection of land-cover changes. Second, the changes caused by temporal fluctuations of VI values are identified through the CCSM analysis and excluded to only keep the most likely land-cover conversions. Finally, classification is performed to map the different types of land-cover conversions. The improved change vector analysis (ICVA) was applied to detect land-cover conversions from 2000 to 2008, using a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced VI images for the Beijing–Tianjin–Tangshan urban agglomeration district, China. The results show that ICVA is able to detect land-cover conversion with a significantly higher accuracy (78.00%, κ?=?0.56) than TCVA (64.00%, κ?=?0.35) or CCSM (66.60%, κ?=?0.27). The proposed approach is of particular value in distinguishing actual land-cover conversion from land-cover modifications resulting from phenological changes.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.     

Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring

Land-cover is an important parameter in analyzing the state and dynamics of natural and anthropogenic terrestrial ecosystems. Land-cover classes related to semi-arid savannas currently exhibit among the greatest uncertainties in available global land cover datasets. This study focuses on the Kalahari in northeastern Namibia and compares the effects of different composite lengths and observation periods with class-wise mapping accuracies derived from multi-temporal MODIS time series classifications to better understand and overcome quality gaps in mapping semi-arid land-cover types. We further assess the effects of precipitation patterns on mapping accuracy using Tropical Rainfall Measuring Mission (TRMM) observation data. Botanical field samples, translated into the UN Land Cover Classification System (LCCS), were used for training and validation. Different sets of composites (16-day to three-monthly) were generated from MODIS (MOD13Q1) data covering the sample period from 2004 to 2007. Land-cover classifications were performed cumulatively based on annual and inter-annual feature sets with the use of random forests. Woody vegetation proved to be more stable in terms of omission and commission errors compared to herbaceous vegetation types. Generally, mapping accuracy increases with increasing length of the observation period. Analyses of variance (ANOVA) verified that inter-annual classifications significantly improved class-wise mapping accuracies, and confirmed that monthly composites achieved the best accuracy scores for both annual and inter-annual classifications. Correlation analyses using piecewise linear models affirmed positive correlations between cumulative mapping accuracy and rainfall and indicated an influence of seasonality and environmental cues on the mapping accuracies. The consideration of the inter-seasonal variability of vegetation activity and phenology cycling in the classification process further increases the overall classification performance of savanna classes in large-area land-cover datasets. Implications for global monitoring frameworks are discussed based on a conceptual model of the relationship between observation period and accuracy.     

Estimating accuracy of land-cover composition from two-stage cluster sampling

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias.     

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.     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

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.     

Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia's rangelands

The primary objective of this study was to assess the condition of a portion of Saudi Arabia's rangelands and evaluate the effects of grazing by the animal herds of indigenous nomads over the last decade. Because of the desertic condition of these rangelands, changes in vegetation cover are more subtle than would be the case for other, less arid areas. Consequently, a new analytic methodology for the detection of desertification of arid and hyper-arid rangelands was developed specifically for this project. The conceptual framework for the analysis is the use of the coefficient of variation (COV) of the monthly Normalized Difference Vegetation Index (NDVI, maximum-value composite) as a measure of vegetative biomass change. A higher NDVI COV for a given pixel (excluding cases of changes in land use) represents a greater change in vegetation biomass in the ground area represented by that pixel. Linear regression was used to determine the trend in COV values for each pixel over the 12-year period for which data was available; pixels with a negative slope are considered to represent ground areas with decreasing amounts of vegetation. Results were validated by tests of statistical significance and by comparison of the theoretical results to vegetation change and land-cover data from the remote sensing systems and from reconnaissance flights over select areas. These desertification trend results were then combined with land-cover information to provide an assessment of desertification status.     

Polarized visible light as an aid to vegetation classification

Radiation, when reflected from the surface of the earth, can be described in terms of both its radiance and its polarization and yet remote sensing has concerned itself with the measurement of radiance and has paid little attention to the measurement of polarization. However, the use of polarization measurements in remote sensing may increase as NASA have included polarizing filters on the satellite-borne Multispectral Resource Sampler (MRS), which may be launched in the mid-1980s. Photographic measurements of percent reflected visible light (percent RVL) and percent polarised visible light (percent PVL) were taken from a light aircraft on two summer days and two winter days. The study area was a heathland with seven land cover classes. In the summer, percent RVL, percent PVL, and percent RVL plus percent PVL could discriminate four land-cover classes. In the winter percent RVL plus percent PVL could discriminate five land-cover classes, percent PVL could discriminate four land-cover classes and percent RVL could discriminate only three land-cover classes. It was concluded that measurements of percent PVL when combined with measurements of percent RVL improved vegetation discrimination in winter months.     

Artificial neural network ensemble-based land-cover classifiers using MODIS data

Terra and Aqua, two satellites launched by the NASA-centered International Earth Observing System project, house MODIS (moderate resolution imaging spectroradiometer) sensors. Moderate-resolution remote sensing allows the quantifying of land-surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this article, we propose land-surface classification by applying an ensemble technique based on fault masking among individual classifiers in N-version programming. An N-version programming ensemble of artificial neural networks is created, in which the majority vote result is used to predict land-surface cover from MODIS data. It is shown by experiment that an N-version programming ensemble of neural networks greatly improves the classification error rate of land-cover type.     

Mapping land-cover over large areas using multispectral data derived from the NOAA-AVHRR: A case study of Nigeria

Land-cover information for Nigeria was obtained from a countrywide, low-level aerial survey conducted in 1990. A range of spectral vegetation indices (SVIs) and ground surface temperature estimates were calculated for Nigeria using daily data throughout 1990 from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. A supervised classification of the land-cover classes was then performed using a modified discriminant analysis in which predictor variables were selected from the mean, maximum, minimum and standard deviation of the raw waveband AVHRR data, AVHRR derived products and a digital elevation model (DEM). With a 60 per cent threshold coverage by any one of eight major vegetation types the analysis correctly predicted land-cover type with producer accuracies (excluding 'bare ground' with only a few points) of between 48 per cent (cultivation) and 100 per cent (mangrove) (average 74.5 per cent).     

The use of temporal metrics for land cover change detection at coarse spatial scales

Successful land cover change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Coarse spatial resolution satellite sensors offer the advantage of frequent coverage of large areas and this facilitates the monitoring of surface processes. Fine spatial resolution satellite sensors provide reliable land cover information on a local basis. This work examines the ability of several temporal change metrics to detect land cover change in sub-Saharan Africa using remote sensing data collected at a coarse spatial resolution over 16 test sites for which fine spatial resolution data are available. We model change in the fine-resolution data as a function of the coarse spatial resolution metrics without regard to the type of change. Results indicate that coarse spatial resolution temporal metrics (i) relate in a statistically significant way to aggregate changes in land cover, (ii) relate more strongly to fine spatial resolution change metrics when including a measure of surface temperature instead of a vegetation index alone, and (iii) are most effective as land cover change indicators when various metrics are combined in multivariate models.     

Mapping land-cover modifications over large areas: A comparison of machine learning algorithms

Large area land-cover monitoring scenarios, involving large volumes of data, are becoming more prevalent in remote sensing applications. Thus, there is a pressing need for increased automation in the change mapping process. The objective of this research is to compare the performance of three machine learning algorithms (MLAs); two classification tree software routines (S-plus and C4.5) and an artificial neural network (ARTMAP), in the context of mapping land-cover modifications in northern and southern California study sites between 1990/91 and 1996. Comparisons were based on several criteria: overall accuracy, sensitivity to data set size and variation, and noise. ARTMAP produced the most accurate maps overall ( 84%), for two study areas — in southern and northern California, and was most resistant to training data deficiencies. The change map generated using ARTMAP has similar accuracies to a human-interpreted map produced by the U.S. Forest Service in the southern study area. ARTMAP appears to be robust and accurate for automated, large area change monitoring as it performed equally well across the diverse study areas with minimal human intervention in the classification process.     

Confusion errors among urban land-cover types on SAR imagery

Urban land-cover maps were produced by interpreting dual-polarized (HH and HV) X-band synthetic aperture radar imagery of Los Angeles, California. These maps were then registered to and compared with an existing 85-category land-use map of the area to determine: (i) specific points of interpretation error (errors of commission) among the types of land cover; and (ii) differences in detectability and misidentification between polarizations. The HH data were much more difficult to interpret than the HV imagery and consequently produced a greater number of errors and types of land-cover confusion. However, there were some land-cover categories which were consistently confused with one another. Those within and between category misidentifications are discussed as they relate to SAR imagery.     

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.     

Spatiotemporal analysis of land-use and land-cover change in the Brazilian Amazon

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.     

被动微波遥感指数及其应用

分别概述了微波极化指数、散射指数以及土壤湿度指数等被动微波遥感指数的发展及其应用。37GHz的微波极化差指数△T37（△T37=TB37V—TB37H）和极化比指数（MPDI=C＊（TB37V—TB37H）／（TB37V＋TB37H））被认为是监测植被状况的微波植被指数，利用GAME—Tibet1998IOP数据计算和分析了青藏高原中部5个试验站点6～9月的平均△T37值和MPDI值的变化情况。结果表明：ANDUO和MS3608的平均值在15K左右，表现出裸土的微波辐射特征；总体上5个站点的MPDI随时间的变化不大，也即在1998年6～9月间，各个站点的植被状况变化不大；而站间的差别比较大，也即各个站点的植被状况有较大的差别；ANDUO的MPDI表现出规律性的变化，即在6至9月的变化中，8月份的MPDI最小，对应植被最好的月份；对研究区的MPDI和相应时间的MSAVI（可见／近红外数据得到的修改型土壤调整植被指数）的空间分布图进行了比较，二者基本吻合。