Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (T _s) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–T _s space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Determination of chlorophyll-a amount in Golden Horn, Istanbul, Turkey using IKONOS and in situ data

The objective of this research was to explore an accurate and fast way of estimating chlorophyll-a amount, a water quality parameter (WQP), using IKONOS satellite sensor image and in situ measurements. Since the in situ data of WQPs are limited with the number of sampling locations, deriving a correlation between these measurements and remotely sensed image allows synoptic estimates of the related parameter over large areas even if the areas are in remote and inaccessible locations. In this study, simultaneously collected satellite image data and in situ measurements of chlorophyll-a were correlated using multivariate regression model. Different experiments were designed by changing the numbers and distributions of in situ measurements. Regression coefficients of each design and differences between model-derived data and in situ measurements were calculated to find out the optimum design to produce chlorophyll-a map of study region. Results illustrated that both the number and distribution of in situ measurements have impact on regression analysis, therefore should be selected attentively. Also, it is found that IKONOS imagery is an efficient and effective source to derive chlorophyll-a map of the large areas using limited number of ground measurements.     

Linking satellite and field survey data,through the use of gis,as implemented in Great Britain in the Countryside Survey 1990 project

The Institute of Terrestrial Ecology (ITE) has been studying land use and the effects of land use on ecology for two decades. A series of national field surveys have been undertaken by the Land Use Section of ITE since 1978, the most recent being Countryside Survey 1990 (CS1990). The three-year project brought together field survey and remote sensing data which were analyzed using Geographical Information Systems (GIS). National and regional land-cover patterns were described and changes estimated.The data collected by the field survey part of CS1990 recorded stratified samples based on a land classification. Thematic maps for surveyed 1-km squares covered physiography, agriculture and semi-natural vegetation, forestry, structures and boundaries. The same sites were surveyed in 1984 and 1990 with 14 000 digital maps produced describing both years. GIS was used to generate stock figures for each year, and overlay allowed change between survey dates to be estimated.GIS was used to compare data collected from both field survey and satellite imagery so that both sets of information could be qualified when expressed as national figures.This paper describes the historical development of the ITE Land Classification, examines the way in which data were collected for surveys, with particular reference to Countryside Survey 1990, and shows how satellite and field survey data can be linked through GIS.     

Mapping broom snakeweed through image analysis of color-infrared photography and digital imagery

A study was conducted on a south Texas rangeland area to evaluate aerial color-infrared (CIR) photography and CIR digital imagery combined with unsupervised image analysis techniques to map broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby]. Accuracy assessments performed on computer-classified maps of photographic images from two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 88.3%, respectively; whereas, accuracy assessments performed on classified maps from digital images of the same two sites had mean producer's and user's accuracies for broom snakeweed of 98.3 and 92.8%, respectively. These results indicate that CIR photography and CIR digital imagery combined with image analysis techniques can be used successfully to map broom snakeweed infestations on south Texas rangelands.     

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R ² = 0.94, 0.91) than their performance on Chl-a concentration (R ² = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R ² = 0.91, 0.82) and TSM (R ² = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R ² = 0.75 and 0.90 for Chl-a; R ² = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.     

Top-down and bottom-up inventory approach for above ground forest biomass and carbon monitoring in REDD framework using multi-resolution satellite data

This study deals with the future scope of REDD (Reduced Emissions from Deforestation and forest Degradation) and REDD+ regimes for measuring and monitoring the current state and dynamics of carbon stocks over time with integrated geospatial and field-based biomass inventory approach. Multi-temporal and multi-resolution geospatial synergic approach incorporating satellite sensors from moderate to high resolution with stratified random sampling design is used. The inventory process involves a continuous forest inventory to facilitate the quantification of possible CO₂ reductions over time using statistical up-scaling procedures on various levels. The combined approach was applied on a regional scale taking Himachal Pradesh (India), as a case study, with a hierarchy of forest strata representing the forest structure found in India. Biophysical modeling implemented revealed power regression model as the best fit (R ²?=?0.82) to model the relationship between Normalized Difference Vegetation Index and biomass which was further implemented to calculate multi-temporal above ground biomass and carbon sequestration. The calculated value of net carbon sequestered by the forests totaled to 11.52 million tons (Mt) over the period of 20 years at the rate of 0.58 Mt per year since 1990 while CO₂ equivalent reduced from the environment by the forests under study during 20 years comes to 42.26 Mt in the study area.     

Use of earth observation satellite data for land degradation mapping and monitoring in Mediterranean ecosystems: Towards a satellite-observatory

The degradation of the permanent seminatural vegetation and the resulting acceleration of soil degradation and erosion processes constitute major elements of land degradation in the Mediterranean basin. Given the European Commission's interest in mapping and controlling desertification phenomena in the Mediterranean member states, the Environmental Mapping and Modelling Unit (EMAP) of the Joint Research Centre is investigating possibilities for using operational earth observation satellites for mapping and repeated monitoring of vegetation and soil characteristics. In the context of previous experiments, approaches have been developed that can already be routinely applied to large regions. This paper discusses the problems of standardized retrieval of remotely sensed primary parameters (such as reflectance), concepts relating to the thematic interpretation of reflectance data, and the definition of satellite-derived degradation indices. In addition, this paper presents requirements for designing an operational satellite observatory for monitoring Mediterranean land degradation.     

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.     

Evaluation of the differences between the SRTM and satellite radar altimetry height measurements and the approach taken for the ACE2 GDEM in areas of large disagreement

The new ACE2 (Altimeter Corrected Elevations 2) Global Digital Elevation Model (GDEM) which has recently been released aims to provide the most accurate GDEM to date. ACE2 was created by synergistically merging the SRTM and altimetry datasets. The comprehensive comparison carried out between the two datasets yielded a myriad of information, with the areas of disagreement providing as much valuable information as the areas of agreement. Analysis of the comparison dataset revealed that certain topographic features displayed consistent differences between the two datasets. The largest differences globally are present over the rainforests, particularly the two largest, around the Amazon and the Congo. The differences range between 10 m and 40 m; these differences can be attributed to the height of the rainforest canopy, as the SRTM returned height values from somewhere within the uppermost reaches of the vegetation whereas the altimeter was able to penetrate through and return true ground heights. The second major class of terrain feature to demonstrate coherent differences are desert regions; here, different deserts present different characteristics. The final area of interest is that of Wetlands; these are areas of special significance because even a slight misrepresentation of the heights can have wide ranging effects in modelling wetland areas. These examples illustrate the valuable additional information content gleaned from the synergistic global combination of the two datasets.     

Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey

Coastline mapping and coastline change detection are critical issues for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Changes in the shape of coastline may fundamentally affect the environment of the coastal zone. This may be caused by natural processes and/or human activities. Over the past 30 years, the coastal sites in Turkey have been under an intensive restraint associated with a population press due to the internal and external touristic demand. In addition, urbanization on the filled up areas, settlements, and the highways constructed to overcome the traffic problems and the other applications in the coastal region clearly confirm an intensive restraint. Aerial photos with medium spatial resolution and high resolution satellite imagery are ideal data sources for mapping coastal land use and monitoring their changes for a large area. This study introduces an efficient method to monitor coastline and coastal land use changes using time series aerial photos (1973 and 2002) and satellite imagery (2005) covering the same geographical area. Results show the effectiveness of the use of digital photogrammetry and remote sensing data on monitoring large area of coastal land use status. This study also showed that over 161 ha areas were filled up in the research area and along the coastal land 12.2 ha of coastal erosion is determined for the period of 1973 to 2005. Consequently, monitoring of coastal land use is thus necessary for coastal area planning in order to protecting the coastal areas from climate changes and other coastal processes.     

The application of satellite data for the quantification of mangrove loss and coastal management in the Godavari estuary, East Coast of India

The mangrove formations of Godavari estuary are due to silting over many centuries. The estuary covers an area of 62,000 ha of which dense Coringa mangrove forest spread in 6,600 ha. Satellite sensor data was used to detect change in the mangrove cover for a period of 12 years (1992-2004). It was found that an area of about 1,250 ha of mangroves was destroyed by anthropogenic interference like aquaculture, and tree felling etc. It was found that mangrove's spectral response/digital number (DN) value is much lower than non-mangrove vegetation such as plantation and paddy fields in SWIR band. By taking this as an advantage, spectral data was utilized for clear demarcation of mangroves from nearby paddy fields and other vegetation. Simpson's diversity index, which is a measure of biodiversity, was found to be 0.09, showing mangroves dominance. Ecological parameters like mud-flats/swamps, mangrove cover alterations, and biodiversity status are studied in detail for a period of 12 years. The increase in mangrove front towards coast was delineated using remote sensing data. The major advantages of remote sensing data is monitoring of change periodically. The combination of moderate and high-resolution data provided detailed coastal land use maps for implementing coastal regulation measures. The classification accuracy has been achieved is 90%. Overall, simple and viable measures are suggested based on multi-spectral data to sustain this sensitive coastal ecology.