Landsat NDVI Research Articles

Vegetation spatial and temporal dynamic characteristics based on NDVI time series trajectories in grassland opencast coal mining

The spatiotemporal dynamic patterns of vegetation in mining area are still unclear. This study utilized time series trajectory segmentation algorithm to fit Landsat NDVI time series which generated from fusion images at the most prosperous period of growth based on ESTARFM algorithm. Combining with the shape features of the fitted trajectory, this paper extracted five vegetation dynamic patterns including pre-disturbance type, continuous disturbance type, stabilization after disturbance type, stabilization between disturbance and recovery type, and recovery after disturbance type. The result indicated that recovery after disturbance type was the dominant vegetation change pattern among the five types of vegetation dynamic pattern, which accounted for 55.2% of the total number of pixels. The follows were stabilization after disturbance type and continuous disturbance type, accounting for 25.6% and 11.0%, respectively. The pre-disturbance type and stabilization between disturbance and recovery type accounted for 3.5% and 4.7%, respectively. Vegetation disturbance mainly occurred from 2004 to 2009 in Shengli mining area. The onset time of stable state was 2008 and the spatial locations mainlydistributed in open-pit stope and waste dump. The reco-very state mainly started since the year of 2008 and 2010, while the areas were small and mainly distributed at the periphery of open-pit stope and waste dump. Duration of disturbance was mainly 1 year. The duration of stable period usually sustained 7 years. The duration of recovery state of the type of stabilization between disturbances continued 2 to 5 years, while the type of recovery after disturbance often sustained 8 years.

Phenological Observations on Classical Prehistoric Sites in the Middle and Lower Reaches of the Yellow River Based on Landsat NDVI Time Series

Buried archeological features show up as crop marks that are mostly visible using high-resolution image data. Such data are costly and restricted to small regions and time domains. However, a time series of freely available medium resolution imagery can be employed to detect crop growth changes to reveal subtle surface marks in large areas. This paper aims to study the classical Chinese settlements of Taosi and Erlitou over large areas using Landsat NDVI time series crop phenology to determine the optimum periods for detection and monitoring of crop anomalies. Burial areas (such as the palace area and the sacrificial area) were selected as the research area while the surrounding empty fields with a low density of ancient features were used as reference regions. Landsat NDVI covering two years’ growth periods of wheat and maize were computed and analyzed using Pearson’s correlation coefficient and Euclidean distance. Similarities or disparities between the burial areas and their empty areas were computed using the Hausdorff distance. Based on the phenology of crop growth, the time series NDVI images of winter wheat and summer maize were generated to analyze crop anomalies in the archeological sites. Results show that the Hausdorff distance was high during the critical stages of water for both crops and that the images of high Hausdorff distance can provide more obvious subsurface archeological information.

Evaluating the Woody Species Diversity by Means of Remotely Sensed Spectral and Texture Measures in the Urban Forests

There is an urgent need to obtain an accurate data on biological diversity and its temporal variation, in order to adopt an appropriate protection strategy to manage the urban forest remnants on sustainable manner for future generations. RapidEye Satellite images with spatial resolution of 5 × 5 m (orthorectified pixel size), ASTER VNIR images with spatial resolution of 15 × 15 m and Landsat-8 OLI images with spatial resolution of 30 × 30 m were used to evaluate the woody species diversity in urban forest remnants. Pearson Correlation Coefficient (PCC) was used to determine the relationships between the woody species diversity at Alpha (α) level and the spectral & texture properties derived from the satellite images. PCC test showed a positive significant relationship between the brightness of the RapidEye satellite image (i.e., Atmospherically Resistant Vegetation Index; ARVI) and the Simpson’s Diversity Index (S) (r = 0.80, p < 0.01). In order to also estimate the beta (β) diversity in the study, the relationship between species rarefaction curves and spectral rarefaction curves was calculated through multiple regression analysis i.e., Landsat NDVI, ASTER NDVI and RapidEye NDVI (respectively, R2 = 0.99; 0.99; 0.98, p < 0.01). The findings of the study revealed that satellite image with a resolution of 5 m would be more appropriate to estimate the woody species diversity of urban forest remnants. However, medium resolution satellite images (Landsat) may be used to examine the species rarefaction curve and β diversity in forest remnants.

Man-made Changes of the Relief Due to the Mining Activities within Husnicioara Open Pit (Mehedinţi County, Romania)

Depending on the relief, the technological process for lignite extraction from Husnicioara open pit uses 2 or 3 excavation steps of the mine waste deposits that lie over the coal strata (I+IV); first, the mining waste is deposited in the outer spoil tip and then in the inner tip, followed by coal excavation on another step. So far, the mining activities have affected 3.5 sqkm, including part of the Lungă and Lacului Valleys, and Faţa Lacului and Pădurea Dumbrava hills. The area of the mining exploitation is an artificial space that functions according to new rules. Consequently, the study attempts to identify the man-made changes of the relief due to the mining activities using field measurements, Landsat satellite images and NDVI index. The results reveal that the lignite exploitation implies the dislocation, relocation and storage of materials, which has caused a significant change of the local geomorphological context, leading to the construction of some anthropic structures such as cavities and prominent relief forms.

Thirty Year Analysis of Forest and Scrub Canopy Cover on the Big Sur Coast of California using Landsat Imagery

The potential for negative effects such as habitat loss and high severity wildfires on endemic and threatened biological resources on the United States Pacific coast from recent climate warming has received increasing attention. This study was designed to quantify, for the first time, variations in forest and coastal bluff scrub canopy density and related growth rates using 30 consecutive years of April-to-June Landsat satellite image data for the central California coast around Big Sur. A trend model was fit to multi-year Landsat NDVI time series using least squares non-linear regression on time. Analysis showed that annual growth rates for coast redwood stands in the Big Sur area, the southern-most range for Sequoia sempervirens on the Pacific coast, have been depressed by periods of drought that exceeded -1.2 standard deviations of long-term annual precipitation. There was no evidence from the Landsat record that recent large wildfires have affected region-wide coast redwood stand structure or growth rates. Nonetheless, extensive wildfires of 2008 did have a marked (albeit temporary) impact on canopy density of the region’s mixed hardwood and Coulter pine forest zones, equivalent to the extreme drought effects of 1990-1991 and 2013-2014 on these higher elevation forest zones.

A Bayesian Approach to Combine Landsat and ALOS PALSAR Time Series for Near Real-Time Deforestation Detection

To address the need for timely information on newly deforested areas at medium resolution scale, we introduce a Bayesian approach to combine SAR and optical time series for near real-time deforestation detection. Once a new image of either of the input time series is available, the conditional probability of deforestation is computed using Bayesian updating, and deforestation events are indicated. Future observations are used to update the conditional probability of deforestation and, thus, to confirm or reject an indicated deforestation event. A proof of concept was demonstrated using Landsat NDVI and ALOS PALSAR time series acquired at an evergreen forest plantation in Fiji. We emulated a near real-time scenario and assessed the deforestation detection accuracies using three-monthly reference data covering the entire study site. Spatial and temporal accuracies for the fused Landsat-PALSAR case (overall accuracy = 87.4%; mean time lag of detected deforestation = 1.3 months) were consistently higher than those of the Landsat- and PALSAR-only cases. The improvement maintained even for increasing missing data in the Landsat time series.

Trends in NDVI and Tundra Community Composition in the Arctic of NE Alaska Between 1984 and 2009

As Arctic ecosystems experience increases in surface air temperatures, plot-level analyses of tundra vegetation composition suggest that there are important changes occurring in tundra communities that are typified by increases in shrubs and declines in non-vascular species. At the same time analyses of NDVI indicate that the Arctic tundra is greening. Few studies have combined plot-level trends in species composition and cover with remote sensing measurements to understand the linkages between tundra vegetation dynamics and NDVI over time. This study reports on trends in species composition for field plots in the Arctic National Wildlife Refuge in NE Alaska from 1984 to 2009 and links these trends to the trends in NDVI at fine and coarse scales. Over this time frame there were few changes in plant community composition. None of the five tundra types that were measured had increases in total vegetative cover, and deciduous shrub cover did not show the large increases reported elsewhere. Surface-(plot) measured NDVI was positively correlated to deciduous and evergreen shrub composition suggesting that these functional groups had a strong influence on NDVI values. Modeled values of NDVI, derived from measures of deciduous and evergreen shrub composition over time, decreased slightly for tussock tundra but did not change for other tundra types. This result suggests that surface NDVI did not change over time on these tundra types. Fine-scale (30-m pixels) Landsat NDVI also did not show any changes for the pixels located at the permanent plots (1992–2009). However, coarse-scale (8-km pixels) AVHRR NDVI across the study area did increase (1988–2007). Furthermore, aggregate values of Landsat pixels matching the same area as AVHRR pixels also did not show significant changes over time. Although Landsat NDVI was consistent with surface-measured NDVI, AVHRR NDVI was not. AVHRR NDVI values showed increases that were in neither the field nor Landsat data. This result suggests that AVHRR may be demonstrating increasing trends in NDVI that are not occurring on the ground in some Arctic tundra ecosystems. These results highlight the need to combine remote sensing with on-the-ground measurements of plant community composition and NDVI in the analysis of the responses of Arctic tundra ecosystems to climate change.

A Kalman Filter-Based Method to Generate Continuous Time Series of Medium-Resolution NDVI Images

A data assimilation method to produce complete temporal sequences of synthetic medium-resolution images is presented. The method implements a Kalman filter recursive algorithm that integrates medium and moderate resolution imagery. To demonstrate the approach, time series of 30-m spatial resolution NDVI images at 16-day time steps were generated using Landsat NDVI images and MODIS NDVI products at four sites with different ecosystems and land cover-land use dynamics. The results show that the time series of synthetic NDVI images captured seasonal land surface dynamics and maintained the spatial structure of the landscape at higher spatial resolution. The time series of synthetic medium-resolution NDVI images were validated within a Monte Carlo simulation framework. Normalized residuals decreased as the number of available observations increased, ranging from 0.2 to below 0.1. Residuals were also significantly lower for time series of synthetic NDVI images generated at combined recursion (smoothing) than individually at forward and backward recursions (filtering). Conversely, the uncertainties of the synthetic images also decreased when the number of available observations increased and combined recursions were implemented.

Climate Change for Agriculture, Forest Cover and 3d Urban Models

Abstract. This research demonstrates the important role of the remote sensing in finding out the different parameters behind the agricultural crop change, forest cover and urban 3D models. Standalone software is developed to view and analysis the different factors effecting the change in crop productions. Open-source libraries from the Open Source Geospatial Foundation have been used for the development of the shape-file viewer. Software can be used to get the attribute information, scale, zoom in/out and pan the shapefiles. Environmental changes due to pollution and population that are increasing the urbanisation and decreasing the forest cover on the earth. Satellite imagery such as Landsat 5(1984) to Landsat TRIS/8 (2014), Landsat Data Continuity Mission (LDCM) and NDVI are used to analyse the different parameters that are effecting the agricultural crop production change and forest change. It is advisable for the development of good quality of NDVI and forest cover maps to use data collected from the same processing methods for the complete region. Management practices have been developed from the analysed data for the betterment of the crop and saving the forest cover

Fusing Landsat and SAR time series to detect deforestation in the tropics

Fusion of optical and SAR time series imagery has the potential to improve forest monitoring in tropical regions, where cloud cover limits optical satellite time series observations. We present a novel pixel-based Multi-sensor Time-series correlation and Fusion approach (MulTiFuse) that exploits the full observation density of optical and SAR time series. We model the relationship of two overlapping univariate time series using an optimized weighted correlation. The resulting optimized regression model is used to predict and fuse two time series. Using the MulTiFuse approach we fused Landsat NDVI and ALOS PALSAR L-band backscatter time series. We subsequently used the fused time series in a multi-sensor change detection framework to detect deforestation between 01/2008 - 09/2010 at a managed forest plantation in the tropics (Pinus caribea; 2859ha). 3-monthly reference data covering the entire study area was used to validate and assess spatial and temporal accuracy. We tested the impact of persistent cloud cover by increasing the per-pixel missing data percentage of the NDVI time series stepwise from ~53% (~6 observations/year) up to 95% (~0.5 observation/year) while fusing with a consistent PALSAR time series of ~2 observations/year. A significant linear correlation was found between the Landsat NDVI and ALOS PALSAR L-band SAR time series observables for logged forest. The multi-temporal filtered PALSAR HVHH backscatter ratio time series (HVHHmt) was most strongly correlated with the NDVI time series. While for Landsat-only the spatial and temporal accuracy of detected deforestation decreased significantly with increasing missing data, the accuracies for the fused NDVI-PALSAR case remained high and were observed to be above the NDVI- and PALSAR-only cases for all missing data percentages. For the fused NDVI-HVHHmt time series the overall accuracy was 95.5% with a 1.59month mean time lag of detected changes. The MulTiFuse approach is robust and automated, and it provides the opportunity to use the upcoming data streams of free-of charge, medium resolution optical and SAR satellite imagery in a beneficial way for improved tropical forest monitoring.

A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion

The focus of the current study is to compare data fusion methods applied to sensors with medium- and high-spatial resolutions. Two documented methods are applied, the spatial and temporal adaptive reflectance fusion model (STARFM) and an unmixing-based method which proposes a Bayesian formulation to incorporate prior spectral information. Furthermore, the strengths of both algorithms are combined in a novel data fusion method: the Spatial and Temporal Reflectance Unmixing Model (STRUM). The potential of each method is demonstrated using simulation imagery and Landsat and MODIS imagery. The theoretical basis of the algorithms causes STARFM and STRUM to produce Landsat-like reflectances while preserving the spatial patterns found in Landsat images, and the unmixing-based method to produce MODIS-like reflectances. The ability of fused images to capture phenological variations is also assessed using temporal NDVI profiles. Temporal profiles of STARFM NDVI closely resembled Landsat NDVI profiles. However, the unmixing-based method and STRUM produced a more accurate reconstruction of the NDVI trajectory in experiments simulating situations where few input high-resolution images are available. STRUM had the best performance as it produced surface reflectances which had the highest correlations to reference Landsat images. The results of this study indicate that STRUM is more suitable for data fusion applications requiring Landsat-like surface reflectances, such as gap-filling and cloud masking, especially in situations where few high-resolution images are available. Unmixing-based data fusion is recommended in situations which downscale the spectral characteristics of the medium-resolution input imagery and the STARFM method is recommended for constructing temporal profiles in applications containing many input high-resolution images.

Analysing Phenological Characteristics Extracted from Landsat NDVI Time Series to Identify Suitable Image Acquisition Dates for Cannabis Mapping in Afghanistan

Analysing Phenological Characteristics Extracted from Landsat NDVI Time Series to Identify Suitable Image Acquisition Dates for Cannabis Mapping in Afghanistan

Mapping agricultural wetlands in the Sacramento Valley, USA with satellite remote sensing

Information on rice paddy practices and rice habitat is required by decision makers and monitoring protocol to manage the agricultural wetlands of the Sacramento Valley, California. Satellite remote sensing imagery have been able to support these needs with accurate maps of rice extent and delineation of winter flooding. However, delineation of rice and habitat, paddy hydroperiod, and spatiotemporal dynamics have not been thoroughly carried out in the region. The objective of this research application was to evaluate cost-efficient synthetic aperture radar and optical imagery for mapping agricultural wetlands and hydroperiod management. Ground-truth field data from the Glenn-Colusa Irrigation District was used to test a suite of remote sensing indices to differentiate among agricultural wetland types and irrigation practices. A Classification And Regression Tree approach that utilized the random forest algorithm was built to map the agricultural wetlands of the Sacramento Valley. Results show optical indices sensitive to vegetative development and surface water (Landsat NDVI and LSWI) along with Synthetic Aperture Radar backscatter values (PALSAR HH sigma nought σ° dB) were the most useful for mapping hydroperiod across habitats. Overpass timing and irrigation management were key factors in sensor and index selection. Nearly 93 % (185,494 ha) of rice paddies in the Valley underwent wet seeding practices and half (90,168 ha) of actively cultivated rice area was flooded during the winter. Eight percent (16,420 ha) of rice habitat received at least one irrigation application during the crop season. The most challenging habitat types to separate were fallow rice paddies that underwent different frequencies of inundation management due to permitting policies and duck habitat promotion. Overall, the results emphasize the utility of satellite remote sensing for rice decision making and as cost-effective tools for supporting Monitoring, Reporting, and Verification protocol.

Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with Random Forest

Savanna ecosystems are geographically extensive and both ecologically and economically important, and require monitoring over large spatial extents. Remote-sensing-based characterization of vegetation properties in savannas is methodologically challenging, mainly due to high structural and functional heterogeneity. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address these challenges. Focusing on the semi-arid savanna ecosystem in the central Kalahari, this study examined the suitability of a hierarchical OBIA approach combined with in situ data and an ensemble classification technique for mapping vegetation morphology types at landscape scale. A stack of Landsat TM imagery, NDVI, and topographic variables was segmented with six different scale factors resulting in a hierarchical network of image objects. Sample objects for each vegetation morphology class were selected at each segmentation scale and classification was performed using optimal features consisting of spectral and textural features. Overall and class-specific classification accuracies were compared across the six scales to examine the influence of segmentation scale on each. Results suggest that the highest overall classification accuracy (i.e. 85.59%) was observed not at the finest segmentation scale, but at coarse segmentation. Additionally, individual vegetation morphology classes differed in the segmentation scale at which they achieved highest classification accuracy, reflecting their unique ecology and physiognomic composition. While classes with high vegetation density/height attained higher accuracy at fine segmentation scale, those with lower vegetation density/height reached higher classification accuracy at coarse segmentation scales. Contrarily, for pans and bare areas, accuracy was relatively unaffected by changing segmentation scale. Variable importance plots suggested that spectral features were the most important, followed by textural variables. These results show the utility of the OBIA approach and emphasize the requirement of multi-scale analysis for accurately characterizing savanna systems.

Long‐term hydrological response to reforestation in a large watershed in southeastern China

AbstractThe long‐term hydrological response to reforestation is critical to regional water management, especially in areas where large‐scale reforestation has been practiced. In this study, we investigated the long‐term hydrological response to reforestation in the 579 km2 basin in southeastern China through ground‐based monitoring of water yield, sedimentation, vegetation cover and climate in the basin. The vegetation dynamics were also examined by remote sensing data (MSS, Landsat and AVHRR NDVI). We found that forest cover increased 23% or 13 593 ha from 1975 to 2002. Meanwhile, annual water yield decreased 86–88 mm from 1971–1983 (i.e. pre‐reforestation) to 1984–2009 (i.e. post‐reforestation). These decreases were significant statistically according to both regression and double mass analyses. Time series analysis demonstrated that there was a clear decline in annual sediment yield and an increase in annual evapotranspiration (ET) over the period from 1971 to 2009. We concluded that reforestation could significantly reduce annual water yield and sediment yield in the basin because of the forest cover change and forest growth. This conclusion is consistent with findings from widely paired‐watershed studies and literatures published on the impact of reforestation in large watersheds. Our results also have important strategic implications and provide insight into more sustainable forest management practices for the future. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Seasonal spectral variation of Zostera noltii and its influence on pigment-based Vegetation Indices

The influence of phenological variations on the reflectance of the intertidal marine angiosperm Zostera noltii was studied using spectroradiometry and pigment analysis. Leaves were sampled each month from March to November and spectral reflectance was measured in the 400–900nm wavelength range in the laboratory for increasing biomass. High Performance Liquid Chromatography (HPLC) was used to assess the pigment composition and concentration of the leaves and their epiphytes. Several vegetation indices (VIs) were tested for their sensitivity to seasonal variations, from NDVI-like ratios to indices based on derivative analysis. Pigment concentration showed a seasonal effect, with the highest concentrations observed in summer, synchronous with the above-ground biomass peak. The seasonal variations were clearly visible in the leaf reflectance spectrum, showing for the same amount of vegetation, lower reflectance in summer for the visible wavelengths and a higher near-infrared (NIR) plateau compared to the other months. Indices based on the difference between the red and NIR reflectance values, typically all the NDVI-like ratios, were the most sensitive to seasonal variations in pigment concentration. In fact, seasonal effects were systematically reduced for VIs including a blue band correction, namely the modified Specific Ratio (mSR(705)), the modified Normalized Difference (mND(705)), the modified NDVIs for Landsat, FORMOSAT and narrow band NDVI (mNDVI(673)) as well as the Atmospherically Resistant Vegetation Index (ARVI). However, these indices showed a faster saturation for increasing seagrass biomass, except for the mSR(705), which was the least sensitive to biomass saturation. The interesting properties of this index suggest that it should now be tested for airborne or satellite remote sensing mapping of Z. noltii beds but it requires a high spectral resolution and cannot be applied to multispectral satellite images.

Long-term sustainability of the hydrology and vegetation of Cienega de Santa Clara, an anthropogenic wetland created by disposal of agricultural drain water in the delta of the Colorado River, Mexico

The Ciénega of Santa Clara is a valuable coastal wetland sustained almost entirely by discharge of brackish agricultural drain water from the U.S. and Mexico. In other locations, agricultural drain water has been problematic in supporting wetlands due to problems of salinity buildup, toxic substances and undesirable plant succession processes. We studied the development of the Cienega de Santa Clara from its creation in 1977 to the present to determine if it is on a sustainable trajectory in terms of vegetation, hydrology and habitat value. We used Landsat NDVI imagery from 1975 to 2011 to determine the area and intensity of vegetation and to estimate evapotranspiration (ET) to construct a water balance. Remote sensing data were combined with hydrological data, site surveys and other sources of information on the Cienega. The vegetated area increased from 1978 to 1995 and has been constant at about 4200ha since then. The dominant vegetation type is Typha domingensis (southern cattail), and peak summer NDVI since 1995 has been stable at 0.379 (SD=0.016), about half of NDVIMax. Flows into the marsh have been stable both month-to-month and year-to-year, with a mean annual value of 4.74m3s−1 (SD=1.03). Salinity has been stable with a mean value of 2.09gL−1 TDS (SD=0.13). About 37% of the inflow water is consumed in ET, with the remainder exiting the Cienega as outflow water, mainly during winter months when T. domingensis is dormant. The sustainability of the Cienega is attributed to: stable inflow rates; salinities within the tolerance limit of the dominant vegetation; and tidal flushing which maintains the wetland as an open system.

NDVI and E. de Martonne Indices in an Environmentally Stressed Area (Thriasio Plain – Greece)

In this paper we examine the possible differentiations of the E. de Martonne climatic aridity index in conjunction to Normalized Difference Vegetation Index – NDVI index. For this purpose meteorological data cover the period from 1958 to 2011 was used. Also for three Landsat satellite images NDVI index was performed. The results for the climatic index show a trend towards characterizing the climate as warmer and drier. The latter conclusion is consistent with intense land use change in the area of interest, and is due to a shift in the management of surface waters used for crops irrigation in the region.

Geospatial Investigation into Groundwater Pollution and Water Quality Supported by Satellite Data: A Case Study from the Evros River (Eastern Mediterranean)

Managing water resources, in terms of both quality and quantity, in transboundary rivers is a difficult and challenging task that requires efficient cross-border cooperation and transparency. Groundwater pollution risk assessment and mapping techniques over the full catchment area are important tools that could be used as part of these water resource management efforts, to estimate pollution pressures and optimize land planning processes. The Evros river catchment is the second largest river in Eastern Europe and sustains a population of 3.6 million people in three different countries (Bulgaria, Turkey and Greece). This study provides detailed information on the main pollution sources and pressures in the Evros catchment and, for the first time, applies, assesses and evaluates a groundwater pollution risk mapping technique using satellite observations (Landsat NDVI) and an extensive dataset of field measurements covering different seasons and multiple years. We found that approximately 40 % of the Greek part of the Evros catchment is characterized as of high and very high pollution risk, while 14 % of the study area is classified as of moderate risk. Both the modeled and measured water quality status of the river showed large spatiotemporal variations consistent with the strong anthropogenic pressures in this system, especially on the northern and central segments of the catchment. The pollutants identified illustrate inputs of agrochemicals and urban wastes in the river. High correlation coefficients (R between 0.79 and 0.85) were found between estimated pollution risks and measured concentrations of those chemical parameters that are mainly attributed to anthropogenic activities rather than in situ biogeochemical processes. The pollution risk method described here could be used elsewhere as a decision support tool for mitigating the impact of hazardous human activities and improving management of groundwater resources.

Diachronic Mapping of LUCC in the Northwest of China over the last half Century: Conversion of CORONA Panchromatic reflectance into LANDSAT NDVI Imagery

It is only by using the first collection of intelligence satellite imagery acquired by CORONA, that time seriesof NDVI imagery can go back for a time period of nearly 50 years,basedon diachronic mapping . This is an exceptionally long time period if not the longest, for which remote sensing imagery is available to estimate land use change for specificregions on the globe. The P.R. ofChina and the formerUSSR can be documented to nearly 50 years back in time, using CORONA satellite imagery. Typically-for many regions of the globeisthat a 50 year time span represents the principal time period in which many anthropogenicland usechanges have taken place. In thispaper,an account is given of the land use change which took place over the last 44 years in the