Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.

Remote sensing applications in water resources

With the introduction of the earth observing satellites, remote sensing has become an important tool in analyzing the Earth's surface characteristics, and hence in supplying valuable information necessary for the hydrologic analysis. Due to their capability to capture the spatial variations in the hydro-meteorological variables and frequent temporal resolution sufficient to represent the dynamics of the hydrologic processes, remote sensing techniques have significantly changed the water resources assessment and management methodologies. Remote sensing techniques have been widely used to delineate the surface water bodies, estimate meteorological variables like temperature and precipitation, estimate hydrological state variables like soil moisture and land surface characteristics, and to estimate fluxes such as evapotranspiration. Today, near-real time monitoring of flood, drought events, and irrigation management are possible with the help of high resolution satellite data. This paper gives a brief overview of the potential applications of remote sensing in water resources.

Improved modeling of groundwater recharge in agricultural watersheds using a combination of crop model and remote sensing

For improved water management and efficiency of use in agriculture, studies dealing with coupled crop-surface water-groundwater models are needed. Such integrated models of crop and hydrology can provide accurate quantification of spatio-temporal variations of water balance parameters such as soil moisture store, evapotranspiration and recharge in a catchment. Performance of a coupled crop-hydrology model would depend on the availability of a calibrated crop model for various irrigated/rainfed crops and also on an accurate knowledge of soil hydraulic parameters in the catchment at relevant scale. Moreover, such a coupled model should be designed so as to enable the use/assimilation of recent satellite remote sensing products (optical and microwave) in order to model the processes at catchment scales. In this study we present a framework to couple a crop model with a groundwater model for applications to irrigated groundwater agricultural systems. We discuss the calibration of the STICS crop model and present a methodology to estimate the soil hydraulic parameters by inversion of crop model using both ground and satellite based data. Using this methodology we demonstrate the feasibility of estimation of potential recharge due to spatially varying soil/crop matrix.

Remote sensing study of snowline altitude at the end of melting season, Chandra-Bhaga basin, Himachal Pradesh, 1980-2007

Glaciers have a direct relation with climate change. The equilibrium line altitude (ELA) is the most useful parameter to study the effect of climate change on glaciers. The ELA is a theoretical snowline at which the glacier mass balance is zero. Snowline altitude (SLA) at the end of melting season is generally regarded as the ELA. Glaciers of Chandra-Bhaga basin in Lahaul-Spiti district of Himachal Pradesh were chosen to study the ELA, using satellite images from 1980 to 2007. A total of 19 glaciers from the Chandra-Bhaga basin were identified and selected to carry out the study of ELA variation over 27 years. This study reveals that the mean SLA of the sub-basin has increased from 5009 +/- 61m to 5401 +/- 21m from 1980 to 2007. The study is in agreement with the reported increase in the temperature and decrease in winter snowfall of North-West Himalaya in the last century.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data

Surface energy processes has an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. The research was undertaken to analyze the potential of Landsat and MODIS data in retrieving biophysical parameters in estimating land surface temperature & heat fluxes diurnally in summer and winter seasons of years 2000 and 2010 and understanding its effect on anthropogenic heat disturbance over Delhi and surrounding region. Results show that during years 2000-2010, settlement and industrial area increased from 5.66 to 11.74% and 4.92 to 11.87% respectively which in turn has direct effect on land surface temperature (LST) and heat fluxes including anthropogenic heat flux. Based on the energy balance model for land surface, a method to estimate the increase in anthropogenic heat flux (Has) has been proposed. The settlement and industrial areas has higher amounts of energy consumed and has high values of Has in all seasons. The comparison of satellite derived LST with that of field measured values show that Landsat estimated values are in close agreement within error of 2 degrees C than MODIS with an error of 3 degrees C. It was observed that, during 2000 and 2010, the average change in surface temperature using Landsat over settlement & industrial areas of both seasons is 1.4 degrees C & for MODIS data is 3.7 degrees C. The seasonal average change in anthropogenic heat flux (Has) estimated using Landsat & MODIS is up by around 38 W/m(2) and 62 W/m(2) respectively while higher change is observed over settlement and concrete structures. The study reveals that the dynamic range of Has values has increased in the 10 year period due to the strong anthropogenic influence over the area. The study showed that anthropogenic heat flux is an indicator of the strength of urban heat island effect, and can be used to quantify the magnitude of the urban heat island effect. (C) 2013 Elsevier Ltd. All rights reserved.

Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.

Prediction of Land Surface Temperature under Cloudy Conditions using Microwave Remote Sensing and ANN

Land surface temperature (LST) is an important variable in climate, hydrologic, ecological, biophysical and biochemical studies (Mildrexler et al., 2011). The most effective way to obtain LST measurements is through satellites. Presently, LST from moderate resolution imaging spectroradiometer (MODIS) sensor is applied in various fields due to its high spatial and temporal availability over the globe, but quite difficult to provide observations in cloudy conditions. This study evolves of prediction of LST under clear and cloudy conditions using microwave vegetation indices (MVIs), elevation, latitude, longitude and Julian day as inputs employing an artificial neural network (ANN) model. MVIs can be obtained even under cloudy condition, since microwave radiation has an ability to penetrate through clouds. In this study LST and MVIs data of the year 2010 for the Cauvery basin on a daily basis were obtained from MODIS and advanced microwave scanning radiometer (AMSR-E) sensors of aqua satellite respectively. Separate ANN models were trained and tested for the grid cells for which both LST and MVI were available. The performance of the models was evaluated based on standard evaluation measures. The best performing model was used to predict LST where MVIs were available. Results revealed that predictions of LST using ANN are in good agreement with the observed values. The ANN approach presented in this study promises to be useful for predicting LST using satellite observations even in cloudy conditions. (C) 2015 The Authors. Published by Elsevier B.V.

Estimation of Aboveground Phytomass of Plantations Using Digital Photogrammetry and High Resolution Remote Sensing Data

The study follows an approach to estimate phytomass using recent techniques of remote sensing and digital photogrammetry. It involved tree inventory of forest plantations in Bhakra forest range of Nainital district. Panchromatic stereo dataset of Cartosat-1 was evaluated for mean stand height retrieval. Texture analysis and tree-tops detection analyses were done on Quick-Bird PAN data. The composite texture image of mean, variance and contrast with a 5x5 pixel window was found best to separate tree crowns for assessment of crown areas. Tree tops count obtained by local maxima filtering was found to be 83.4 % efficient with an RMSE+/-13 for 35 sample plots. The predicted phytomass ranged from 27.01 to 35.08 t/ha in the case of Eucalyptus sp. while in the case of Tectona grandis from 26.52 to 156 t/ha. The correlation between observed and predicted phytomass in Eucalyptus sp. was 0.468 with an RMSE of 5.12. However, the phytomass predicted in Tectona grandis was fairly strong with R-2=0.65 and RMSE of 9.89 as there was no undergrowth and the crowns were clearly visible. Results of the study show the potential of Cartosat-1 derived DSM and Quick-Bird texture image for the estimation of stand height, stem diameter, tree count and phytomass of important timber species.

Remote Sensing of Giant Reed with QuickBird Satellite Imagery

QuickBird high resolution (2.8 m) satellite imagery was evaluated for distinguishing giant reed ( Arundo donax L.) infestations along the Rio Grande in southwest Texas. (PDF has 5 pages.)

Light Reflectance Characteristics and Remote Sensing of Waterlettuce

Waterlettuce ( Pistia stratiotes L.) is a free-floating exotic aquatic weed that often invades and clogs waterways in the southeastern United States. A study was conducted to evaluate the potential of using remote sensing technology to distinguish infestations of waterlettuce in Texas waterways. Field reflectance measurements showed that waterlettuce had higher visible green reflectance than associated plant species. Waterlettuce could be detected in both aerial color- infrared (CIR) photography and videography where it had light pink to pinkish-white image tonal responses. Computer analysis of CIR photographic and videographic images had overall accuracy assessments of 86% and 84%, respectively. (PDF contains 6 pages.)

Potential use of remote sensing to assess effects of wave action on plant re-establishment

Evaluation of the potential for remote sensing to detect a relationship between wave action factors and plant re-establishment after a habitat enhancement at Lake Kissimmee, Florida. Using Geographic Information Systems (GIS) and remote sensing, wave action factors were found to be inversely related to the probability of plant re-establishment. However, correlation of wave action factors with areal coverage of aquatic plants based on field measurements, were unable to detect a significant relationship. Other factors aside from wave action, including littoral slope and the presence of offshore vegetation, may have influenced plant re-establishment in these sites. Remote sensing techniques may be useful to detect large changes in plants communities, however small changes in plant coverages may not be detectable using this technique.

Remote sensing of giant salvinia in Texas waterways

Giant salvinia (Salvinia molesta Mitchell) is an invasive aquatic fern that has been discovered at several locations in southeast Texas. Field reflectance measurements were made on two classes of giant salvinia [green giant salvinia (green foliage) and senesced giant salvinia (mixture of green and brown foliage)] and several associated species. Reflectance measurements showed that green giant salvinia could be best distinguished at the visible green wavelength, whereas senesced giant salvinia could generally be best separated at the near-infrared (NIR) wavelength. Green giant salvinia and senesced giant salvinia could be detected on color-infrared (CIR) aerial photographs where them had pink and grayish-pink or olive-green image responses, respectively. Both classes of giant salvinia could be distinguished in reflectance measurements made on multiple dates and at several locations in southeast Texas. Likewise, they could he detected in CIR photographs obtained on several dates and at widely separated locations. Computer analysis of a CIR photographic transparency showed that green giant salvinia and senesced giant salvinia populations could he quantified. An accuracy assessment performed on the classified image showed an overall accuracy of 87.0%.

Using Remote Sensing and Spatial Information Technologies to Detect and Map Two Aquatic Macrophytes

This paper describes the light reflectance characteristics ofwaterhyacinth [Eichhornia crassipes (Mort.) Solms] and hydrilla [Hydrilla verticillata (L.F.) Royle] and the application of airborned videography with global positioning system (GPS) and geographic information system (GIS) technologies for distinguishing and mapping the distribution of these two aquatic weeds in waterways of southern Texas. Field reflectance measurements made at several locations showed that waterhyacinth generally had higher near-infrared (NIR) reflectance than associated plant species and water. Hydrilla had lower NIR reflectance than associated plant species and higher NIR reflectance than water. Reflectance measurements made on hydrilla plants submerged below the water surface had similar spectral characteristics to water. Waterhyacinth and hydrilla could be distinguished in color-infrared (CIR) video imagery where they had bright orange-red and reddish-brown image responses, respectively. Computer analysis of the imagery showed that waterhyacinth and hydrilla infestaions could be quantified. An accuracy assessment performed on the classified image showed an overall accuracy of 87.7%. Integration of the GPS with the video imagery permitted latitude/longitude coordinates of waterhyacinth and hydrilla infestation to be recorded on each image. A portion of the Rio Grande River in extreme southern Texas was flown with the video system to detect waterhyacinth and hydrilla infestaions. The GPS coordinates on the CIR video scenes depicting waterhyacinth and hydrilla infestations were entered into a GIS to map the distribution of these two noxious weeds in the Rio Grande River.