Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Assessing the Utility of Sentinel-1 C Band Synthetic Aperture Radar Imagery for Land Use Land Cover Classification in a Tropical Coastal Systems When Compared with Landsat 8

Cloud cover constitutes a major obstacle to land cover classification in the humid tropical regions when using optical remote sensing such as Landsat imagery. The advent of freely available Sentinel-1 C band synthetic aperture radar (SAR) imagery offers new opportunities for land cover classification in frequently cloud covered environments. In this study, we investigated the utility of Sentinel-1 for extracting land use land cover (LULC) information in the coastal low lying strip of Douala, Cameroon when compared with Landsat enhanced thematic mapper (TM). We also assessed the potential of integrating Sentinel-1 and Landsat. The major LULC classes in the region included water, settlement, bare ground, dark mangroves, green mangroves, swampy vegetation, rubber, coastal forest and other vegetation and palms. Textural variables including mean, correlation, contrast and entropy were derived from the Sentinel-1 C band. Various conventional image processing techniques and the support vector machine (SVM) algorithm were applied. Only four land cover classes (settlement, water, mangroves and other vegetation and rubber) could be calibrated and validated using SAR imagery due to speckles. The Sentinel-1 only classification yielded a lower overall classification accuracy (67.65% when compared to all Landsat bands (88.7%)). The integrated Sentinel-1 and Landsat data showed no significant differences in overall accuracy assessment (88.71% and 88.59%, respectively). The three best spectral bands (5, 6, 7) of Landsat imagery yielded the highest overall accuracy assessment (91.96%). in the study. These results demonstrate a lower potential of Sentinel-1 for land cover classification in the Douala estuary when compared with cloud free Landsat images. However, comparable results were obtained when only broad classes were considered.

Water quality monitoring in a slightly-polluted inland water body through remote sensing-Case study of the Guanting Reservoir in Beijing,China

This study focused on the water quality of the Guanting Reservoir,a possible auxiliary drinking water source for Beijing.Through a remote sensing(RS)approach and using Landsat 5 Thematic Mapper(TM)data,water quality retrieval models were established and analyzed for eight common water quality variables,including algae content,turbidity,and concentrations of chemical oxygen demand,total nitrogen,ammonia nitrogen,nitrate nitrogen,total phosphorus,and dissolved phosphorus.The results show that there exists a statistically significant correlation between each water quality variable and remote sensing data in a slightly-polluted inland water body with fairly weak spectral radiation.With an appropriate method of sampling pixel digital numbers and multiple regression algorithms,retrieval of the algae content,turbidity,and nitrate nitrogen concentration was achieved within 10% mean relative error,concentrations of total nitrogen and dissolved phosphorus within 20%,and concentrations of ammonia nitrogen and total phosphorus within 30%.On the other hand,no effective retrieval method for chemical oxygen demand was found.These accuracies were acceptable for the practical application of routine monitoring and early warning on water quality safety with the support of precise traditional monitoring.The results show that performing the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.

Information extraction and dynamic evaluation of soil salinization with a remote sensing method in a typical county on the Huang-Huai-Hai Plain of China

Monitoring the dynamics of soil salinization is of great importance for agricultural production.This study selected Yucheng County,a typical county on the Huang-Huai-Hai Plain(HHHP)of China,as the study area and evaluated the spatial and temporal variation of soil salinization.Three methods,consisting of principal component analysis(PCA)transformation,tasseled cap(TC)transformation,and optimal band combination(OBC),were used to extract information from an early Landsat multispectral scanner(MSS)image from 1984,and their advantages were compared.In addition,OBC was used on a thematic mapper(TM)image from 2009.An iteratively self-organizing data analysis algorithm was used together with prior knowledge of likely classifications to interpret the MSS and TM images for data classification.Finally,a transfer matrix method was used to assess the spatial and temporal variability of soil salinization and analyze the driving factors of soil salinization.Compared to PCA transformation and OBC,TC transformation was a more effective method for extracting soil salinization information from the MSS sensor.The results indicate that a soil area of approximately 298 km^2was affected by salinity in 1984 in Yucheng County,of which 5.40%,11.96%,and 12.75%were classified as being subject to slight,moderate,and severe salinization,respectively.In 2009,the saline area was reduced to only 146 km^2,of which 10.70%and 3.75%were characterized by slight to moderate salinization and no severe salinization,respectively.The saline land decreased at an average rate of 6 km^2per year.This decrease was probably a result of lower groundwater depth,increased organic fertilizer or crop straw in soil,changed land use type,and increased vegetation coverage.

Reclamation areas and their development studied by vegetation indices

Large areas in the Czech Republic were used for open casts of brown coal mining.Many of them have been already closed.Reclamation of them and of their dumps is the next step intheir development.It is possible to divide used reclamations into the forest,hydrologic,agricultural and other ones
roads,etc.Their age varies from 45 years to as yet unfinished.Reclaimed areas are documented in reclamation projects.Information about age and land use determined groups of these areas to be evaluated by vegetation indices.100 areas with forest type were evaluated.Eight vegetation indices(NDVI,DVI,RVI,PVI,SAVI,MSAVI,TSAVI and EVI)were calculated and their average value in each area in 1988,1992 and 1998 Thematic Mapper data were compared.Changes over years showed close relation to precipitations of previous periods.This relation was confirmed by evaluation of forest areas situated near reclamation areas.Positive/negative changes of vegetation indices were different for different groups and different vegetation indices.An overview of results of vegetation indices is presented for individual areas whose land use comprised at least partly forest stand.Results in a 4-year period(1988
1992)were in many areas by many indices negative,changes in 10 years were in most areas by most vegetation indices positive.Changes,minimum values and maximum values in groups were compared.Evaluation of vegetation indices brought again various results.One vegetation index is not sufficient to prove improvement/deterioration of vegetation changes.Precipitation state before measurement should be controlled.Temporary shortage of precipitation can cause vegetation cover deterioration,which is also only temporary.The best development derived from vegetation indices evaluation was found at forest reclamation with mixed tree stand that was 10
20 years old.The method was derived as a tool for post-finishing control of vegetation development of reclamations performed in several year periods.

A remotely sensed tracking of forest cover and associatedtemperature change in Margalla hills

Worldwide, forest degradation is a serious environmental issue, and inPakistan, forest wealth is depleting at the highest rate in South Asia. Toensure sustainable development goals of environmental stewardship,social development and economic growth, a sound monitoring andregulatory mechanism is essential for tracking forest cover changes. Thisstudy aims to quantify the decline of forest reserves and associatedtemperature variations in a relatively unexplored biodiversity hotspot ofIslamabad, Margalla Hills National Park (MHNP). Imagery acquired byLandsat TM (Thematic Mapper) for the year 1992, 2000 and 2011 areused to assess the spatial and temporal changes occurred over the lasttwo decades (from 1992 to 2011). A robust hybrid-classification routineis implemented to monitor the changes in forest cover and ANOVAalong with Tukey’s HSD (Honestly Significant Difference) test is used totest the significance of temperature variation associated with a shift inland cover classes. The results showed a significant growth insettlements, agricultural area and barren soil whereas water body, lowervegetation, scrub and pine forest are diminishing. In both decades, thetemperature alteration associated with a change in land cover classesare statistically significant (confirmed by ANOVA and Tukey’s HSD tests)for most of the land use/land cover classes. Based on these findings, thisstudy concludes that forests are dwindling at MHNP and the degradingcondition of the forest is below par and necessitates the promotion ofconservation practices to minimize ecological disturbances.

Relationship between spatial heterogeneity and wavelength in multisensor airborne images

Variogram has been utilized to exploring the spatial heterogeneity of remote sensing images,especially its association with spatial resolution.However,very few attentions have been drawn on evaluating the spatial heterogeneity of multisensor airborne imagery and its relationship with spectral wavelength.Therefore,an investigation was carried out on multisensor airborne images to determine the relation between spatial heterogeneity and spectral wavelength for woodland,grass,and urban landscapes by applying variogram modeling.The airborne thematic mapper(ATM),compact airborne spectrographic imager(CASI),and Specim AISA Eagle airborne images at Harwood Forest,Monks wood,Cambridge,and River Frome areas,UK,were utilized.Results revealed that(1)the red band contained greater spatial variability than near-infrared wavelengths and other visible wavebands;(2)there was a steep gradient at the red edge in reference to its spatial variability of multisensor airborne images;(3)only for natural landscape such as woodland and grass,near-infrared waveband contains greater within-scene variations than the blue and green bands;(4)compared with the discrepancy of spatial resolution introduced by multisensor images(ATM,CASI,and Eagle),the specific landscape and spectral bands were more important in determining heterogeneity by means of original visible,near-infrared bands,and normalized difference vegetation index(NDVI).These findings remained us to be caution when combining and interpreting spatial variability and spatial structures derived from airborne images with different spatial resolution and spectral wavelength.Additionally,the outcomes of this study also have considerable implications in terms of designing and choosing suitable images for different applications.