Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach

The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km² of the study area, whereas the poor groundwater potential zone covers 180.05 km². Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%).     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

Land subsidence prediction in Beijing based on PS-InSAR technique and improved Grey-Markov model

Land subsidence induced by excessive groundwater withdrawal has caused serious social, geological, and environmental problems in Beijing. Rapid increases in population and economic development have aggravated the situation. Monitoring and prediction of ground settlement is important to mitigate these hazards. In this study, we combined persistent-scatterer interferometric synthetic aperture radar with Grey system theory to monitor and predict land subsidence in the Beijing plain. Land subsidence during 2003–2014 was determined based on 39 ENVISAT advanced synthetic aperture radar (ASAR) images and 27 RadarSat-2 images. Results were consistent with global positioning system, leveling measurements at the point level and TerraSAR-X subsidence maps at the regional level. The average deformation rate in the line-of-sight was from ?124 to 7 mm/year. To predict future subsidence, the time-series deformation was used to build a prediction model based on an improved Grey-Markov model (IGMM), which adapted the conventional GM(1,1) model by utilizing rolling mechanism and integrating a k-means clustering method in Markov-chain state interval partitioning. Evaluation of the IGMM at both point level and regional scale showed good accuracy (root-mean-square error <3 mm; R² = 0.94 and 0.91). Finally, land subsidence in 2015–2016 was predicted, and the maximum cumulative deformation will reach 1717 mm by the end of 2016. The promising results indicate that this method can be used as an alternative to the conventional numerical and empirical models for short-term prediction when there is lack of detailed geological or hydraulic information.     

Remote Sensing and GIS Approach for Delineation of Groundwater Potential and Groundwater Quality Zones of Western Doon Valley, Uttarakhand, India

Groundwater exploration in the Western Doon valley has been carried out to delineate the groundwater potential and groundwater quality zones suitable for domestic purposes based on the integrated use of Remote Sensing and Geographical Information Systems (GIS). The Western Doon Valley, occupying broad synclinal troughs in the evolving fold-thrust system of sub-Himalaya, which is filled by post-Siwalik fluvial and debris flow deposits in the late Quaternary-Holocene. The Western Doon Valley area is bounded by the Mussoorie range in the north with 1800–2800 m elevation and in the south by young topographic relief of the frontal Siwalik range with ~800 m average elevation. Groundwater quality of Western Doon valley through pictorially representation in the GIS environment, it is inferred that calcium, magnesium, total hardness and nitrate at some locations above the desirable limit. The groundwater prospects map has been prepared by integrating the hydrogeomorphologic, land use/land cover from satellite data (IRS-ID, LISS-III data) slope, soil, drainage density, depth to water table of pre-monsoon and post-monsoon periods (unconfined aquifer), water table fluctuation, static water level (confined to semi-confined aquifers), specific capacity, discharge and drawdown maps using index overlay method in the GIS environment. The groundwater prospects are depicted in five categories Very high, high, moderate, low and very low (runoff zone) integrated with the groundwater quality zones which have been prepared from hydrochemical data. The results indicated that 16.82 % of the area is under Very high potential zone category with 16.11 % and 0.71 % of desirable and undesirable quality of groundwater and 18.65 %, 42.06 %, 6.96 % and 15.46 % classified as high, moderate, low and very low potential zones with desirable and undesirable quality of groundwater for domestic purposes. This study be useful for designing the groundwater prospects and management plan for the sustainable development of study area.     

Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite radar interferometry

ABSTRACT

Over-exploitation of groundwater has caused severe land subsidence in Beijing during the past two decades. Since the middle route of South-to-North Water Diversion Project (SNWDP), the biggest water diversion project in China, started to deliver water to Beijing in December 2014, the groundwater shortage has been greatly alleviated. This study aims to analyze the impact of SNWDP on the spatiotemporal evolution of land subsidence in Beijing. Change in surface displacement in Beijing after SNWDP was retrieved and the spatiotemporal patterns of the change were analyzed based on long time-series Envisat Advanced Synthetic Aperture Radar (ASAR) (2004–2010), Radarsat-2 (2011–2014), and Sentinel-1 (2015–2017) satellite datasets using Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) techniques. Land subsidence unevenness index (LSUI) was proposed to represent the spatial unevenness of surface displacement. PS-Time approach was then adapted to examine the time series evolution of LSUI. The results showed that the InSAR measurements agree well with leveling measurements with R² over 0.96. Although the maximum annual displacement rate reached ?159.7 mm/year by 2017, over 57% of the area within 25 mm/year contour line showed decreasing or unchanged displacement rate after the south-north water delivered to Beijing. The settlement rate in Chaoyang-Dongbalizhuang (CD) subsidence center has decreased for 26 mm/year from 2011–2014 to 2015–2017. Only around 15% of the area experienced continued accelerating settlement rate through the three time periods, which was mainly located in the area with the compressible layer thickness over 190 m, while the magnitude of velocity increment considerably decreased after SNWDP. Land subsidence unevenness, represented by LSUI, developed more slowly after SNWDP than that during 2011–2014. However, LSUI at the edge of settlement funnel has kept developing and reached 1.7‰ in 2017. Decreasing groundwater level decline after SNWDP and the positive relationship (R² > 0.74) between land subsidence and groundwater level clearly showed impacts of SNWDP on the alleviating land subsidence. Other reasons include geological background, increasing precipitation, and strict water management policies implemented during these years.     

Potential CO<Subscript>2</Subscript> Emission Due to Loss of Above Ground Biomass from the Indian Sundarban Mangroves During the Last Four Decades

Sundarban, the largest single patch of mangrove forest of the world is shared by Bangladesh (~ 60 %) and India (~ 40 %). Loss of mangrove biomass and subsequent potential emission of carbon dioxide is reported from different parts of the world. We estimated the loss of above ground mangrove biomass and subsequent potential emission of carbon dioxide in the Indian part of the Sundarban during the last four decades. The loss of mangrove area has been estimated with the help of remotely sensed data and potential emission of carbon dioxide has been evaluated with the help of published above ground biomass data of Indian Sundarban. Total loss of mangrove area was found to be 107 km² between the year 1975 and 2013. Amongst the total loss ~60 % was washed away in the water by erosion, ~ 23 % was converted into barren lands and the rest were anthropogenically transformed into other landforms. The potential carbon dioxide emission due to the degradation of above ground biomass was estimated to be 1567.98 ± 551.69 Gg during this period, which may account to 64.29 million $ in terms of the social cost of carbon. About three-forth of the total mangrove loss was found in the peripheral islands which are much more prone to erosion. Climate induced changes and anthropogenic land use change could be the major driving force behind this loss of ‘blue carbon’.     

Analysis of snow cover and climatic variability in Bhaga basin located in western Himalaya

Bhaga Basin has complex mountainous terrain; little study has been done on the spatial and temporal characteristics of snow cover in the region. The Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day snow cover products between 2001 and 2012 for winter period (November–April) have been used to study the variation in snow cover area (SCA). The statistical analysis based on non-parametric Mann Kendall and Sen’s slope methods have been used for detecting and estimating trends for climatic variables (temperature and snowfall) and SCA for winter period. Results of statistical analysis indicate rise in minimum temperature (0.02 °C year^?1) and fall in maximum temperature (0.17 °C year^?1). It also shows decrease in mean seasonal snowfall (0.07 cm year^?1). The seasonal SCA was found to decrease at the rate of 0.002% year^?1. This study indicates that the climate change is probably one of the major causes for depleting SCA.     

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm,India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d^?1 for ET_SEBAL, i.e. ET_SEBAL having good accuracy with respect to observed ET_Lysimeter. Results were also validated using R-RMSE test, which also proved that ET_SEBAL data are having good accuracy with respect to observed ET_Lysimeter as R-RMSE of crop-growing period is 0.19 mm d^?1. MAE (0.19), NRMSE (0.21) and r² (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET_24 h) estimation. The temporal study of the ET_24 h values analysed has revealed that the highest ET_24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

Identifying ephemeral and perennial stream reaches using apparent thermal inertia of an ungauged basin in Central New Mexico

The night and day temperature images from advanced spaceborne thermal emission and reflection radiometer (ASTER) remote sensing images are used to identify ephemeral and perennial stream reaches for use in the calibration of an integrated hydrologic model of an ungauged basin. The concept is based on apparent thermal inertia [ATI = (1-albedo)/(day temperature ? night temperature)]. These calculations help both the conceptual model and the calibration for the hydrologic model by indicating where there are thin alluvium and/or shallow groundwater. The study is on the Sevilleta National Wildlife Refuge, a long-term ecological research project that ASTER has included in its regular duty cycle. There are over 360 ASTER scenes in 8 years; however, only 10 night/day pairs suitable for ATI were found. The results correlate to the soil moisture recorded at two locations near the channel (R ² of 0.88). The relationship between soil moisture and surrounding materials allows for differentiation of the perennial and ephemeral stream reaches.     

Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks

This study tested the use of machine learning techniques for the estimation of above-ground biomass (AGB) of Sonneratia caseolaris in a coastal area of Hai Phong city, Vietnam. We employed a GIS database and multi-layer perceptron neural networks (MLPNN) to build and verify an AGB model, drawing upon data from a survey of 1508 mangrove trees in 18 sampling plots and ALOS-2 PALSAR imagery. We assessed the model’s performance using root-mean-square error, mean absolute error, coefficient of determination (R²), and leave-one-out cross-validation. We also compared the model’s usability with four machine learning techniques: support vector regression, radial basis function neural networks, Gaussian process, and random forest. The MLPNN model performed well and outperformed the machine learning techniques. The MLPNN model-estimated AGB ranged between 2.78 and 298.95 Mg ha^?1 (average = 55.8 Mg ha^?1); below-ground biomass ranged between 4.06 and 436.47 Mg ha^?1 (average = 81.47 Mg ha^?1), and total carbon stock ranged between 3.22 and 345.65 Mg C ha^?1 (average = 64.52 Mg C ha^?1). We conclude that ALOS-2 PALSAR data can be accurately used with MLPNN models for estimating mangrove forest biomass in tropical areas.     

A workflow for Sustainable Development Goals indicators assessment based on high-resolution satellite data

ABSTRACT

For evaluating the progresses towards achieving the Sustainable Development Goals (SDGs), a global indicator framework was developed by the UN Inter-Agency and Expert Group on Sustainable Development Goals Indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDGs indicators. The main improvements consist of using moderate and high spatial resolution satellite data and state-of-the-art deep learning methodology for land cover classification and for assessing land productivity. Within the European Network for Observing our Changing Planet (ERA-PLANET), three SDGs indicators are calculated. In this research, harmonized Landsat and Sentinel-2 data are analyzed and used for land productivity analysis and yield assessment, as well as Landsat 8, Sentinel-2 and Sentinel-1 time series are utilized for crop mapping. We calculate for the whole territory of Ukraine SDG indicators: 15.1.1 – ‘Forest area as proportion of total land area’; 15.3.1 – ‘Proportion of land that is degraded over total land area’; and 2.4.1 – ‘Proportion of agricultural area under productive and sustainable agriculture’. Workflows for calculating these indicators were implemented in a Virtual Laboratory Platform. We conclude that newly available high-resolution remote sensing products can significantly improve our capacity to assess several SDGs indicators through dedicated workflows.     

Quantification of LST and CO2 levels using Landsat-8 thermal bands on urban environment

Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO₂, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO₂ plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO₂ on LST, the relationship between LST, air temperature and CO₂ was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R² = 0.994), May (R² = 0.297) and June (R² = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO₂ levels were obtained from linear regression analysis. band 11 was correlating significantly with CO₂ values in each of the months April (R² = 0.217), May (R² = 0.914) and June, (R² = 0.934), because band 11 is closer to the 15-micron band of CO₂. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.     

An assessment of gully erosion along major armoured roads in south-eastern region of South Africa: a remote sensing and GIS approach

An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R²?=?0.69, α = 0.00) and road contributing surface area (R²?=?0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R² = 0.52, α = 0.00) and rainfall (R² = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.     

Identifying natural and anthropogenically-induced geohazards from satellite ground motion and geospatial data: Stoke-on-Trent,UK

Determining the location and nature of hazardous ground motion resulting from natural and anthropogenic processes such as landslides, tectonic movement and mining is essential for hazard mitigation and sustainable resource use. Ground motion estimates from satellite ERS-1/2 persistent scatterer interferometry (PSI) were combined with geospatial data to identify areas of observed geohazards in Stoke-on-Trent, UK. This investigation was performed within the framework of the EC FP7-SPACE PanGeo project which aimed to provide free and open access to geohazard information for 52 urban areas across Europe. Geohazards identified within the city of Stoke-on-Trent and neighbouring rural areas are presented here alongside an examination of the PanGeo methodology.A total of 14 areas experiencing ground instability caused by natural and anthropogenic processes have been defined, covering 122.35 km². These are attributed to a range of geohazards, including landslides, ground dissolution, made ground and mining activities. The dominant geohazard (by area) is ground movement caused by post-mining groundwater recharge and mining-related subsidence (93.19% of total geohazard area), followed by landsliding (5.81%). Observed ground motions along the satellite line-of-sight reach maxima of +35.23 mm/yr and −22.57 mm/yr. A combination of uplift, subsidence and downslope movement is displayed.‘Construction sites’ and ‘continuous urban fabric’ (European Urban Atlas land use types) form the land uses most affected (by area) by ground motion and ‘discontinuous very low density urban fabric’ the least. Areas of ‘continuous urban fabric’ also show the highest average velocity towards the satellite (5.08 mm/yr) and the highest PS densities (1262.92 points/km²) along with one of the lowest standard deviations. Rural land uses tend to result in lower PS densities and higher standard deviations, a consequence of fewer suitable reflectors in these regions. PSI is also limited in its ability to identify especially rapid ground motion. As a consequence the supporting geospatial data proved especially useful for the identification of landslides and some areas of ground dissolution. The mapped areas of instability are also compared with modelled potential geohazards (the BGS GeoSure dataset).     

Fuzzy logic and multi-criteria methods for groundwater potentiality mapping at Al Fo’ah area,the United Arab Emirates (UAE): an integrated approach

This study has modified an approach based on fuzzy logic in a GIS and multi-criteria decision making (MCDM) for groundwater potentiality mapping in Al Fo’ah area, the United Arab Emirates. The proposed approach uses nine groundwater conditioning factors recognized by local hydrology experts. Each factor and class of each factor was given a weight and score based on its level of contributions to groundwater accumulation using analytical hierarchy process and MCDM. Our results revealed that the areas of very high harbouring groundwater are located at the foot of the Oman mountains and occupies an area of about 8.46 km² (4%) of the study area. We validated our results using the Relative Operating Characteristics. The area under the curve showed a ratio of 0.8957 (of 89.57%). The modified approach is timely and economically effective and can be applied in semi-arid regions to help hydrologists.     

Prioritisation of sub-watersheds based on earth observation data of agricultural dominated northern river basin of India

The Upper Tons River Basin of North India has been selected for prioritisation of sub-watersheds (SW) based on morphometric parameters with respect to groundwater derived from topographic sheets and CARTOSAT data. There are 10 SW have been delineated in the region, high stream frequency (F_s) values of SW (1–5) and SW-9 indicated the occurrence of steep slopes, less permeable rocks, greater runoff, less infiltration possibility. Further, these regions have been predicted as poor groundwater potentialities. SW-2 has been identified as poorest groundwater potential zone, whereas SW-4 and SW (6–8) regions possess good permeable bed rocks. The Drainage density (D_d) map demonstrated that the middle south-west region possesses higher D_d whereas northeastern regions contain lower D_d. Further, the areal parameters indicate elongated shape of the basin, hilly region has moderate to steeper ground slope. The outcomes of work have potential to manage groundwater and to ameliorate the flash flood and droughts.     

Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system

The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).     

Comparing the spectral settings of the new generation broad and narrow band sensors in estimating biomass of native grasses grown under different management practices

The challenge of assessing and monitoring the influence of rangeland management practices on grassland productivity has been hampered in southern Africa, due to the lack of cheap earth observation facilities. This study, therefore, sought to evaluate the capability of the newly launched Sentinel 2 multispectral imager (MSI) data, in relation to Hyperspectral infrared imager (HyspIRI) data in estimating grass biomass subjected to different management practices, namely, burning, mowing and fertilizer application. Using sparse partial least squares regression (SPLSR), results showed that HyspIRI data exhibited slightly higher grass biomass estimation accuracies (RMSE = 6.65 g/m², R² = 0.69) than Sentinel 2 MSI (RMSE = 6.79 g/m², R² = 0.58) across all rangeland management practices. Student t-test results then showed that Sentinel 2 MSI exhibited a comparable performance to HyspIRI in estimating the biomass of grasslands under burning, mowing and fertilizer application. In comparing the RMSEs derived using wave bands and vegetation indices of HyspIRI and Sentinel, no statistically significant differences were exhibited (α = 0.05). Sentinel (Bands 5, 6 and 7) and HyspIRI (Bands 730 nm, 740 nm, 750 nm, 710 nm), as well as their derived vegetation indices, yielded the highest predictive accuracies. These findings illustrate that the accuracy of Sentinel 2 MSI data in estimating grass biomass is acceptable when compared with HyspIRI. The findings of this work provide an insight into the prospects of large-scale grass biomass modeling and prediction, using cheap and readily available multispectral data.     

A bias-free geodetic boundary value problem approach to height datum unification

A geodetic boundary value problem (GBVP) approach has been formulated which can be used for solving the problem of height datum unification. The developed technique is applied to a test area in Southwest Finland with approximate size of 1.5° × 3° and the bias of the corresponding local height datum (local geoid) with respect to the geoid is computed. For this purpose the bias-free potential difference and gravity difference observations of the test area are used and the offset (bias) of the height datum, i.e., Finnish Height Datum 2000 (N2000) fixed to Normaal Amsterdams Peil (NAP) as origin point, with respect to the geoid is computed. The results of this computation show that potential of the origin point of N2000, i.e., NAP, is (62636857.68 ± 0.5) (m²/s²) and as such is (0.191 ± 0.003) (m) under the geoid defined by W ₀ = 62636855.8 (m²/s²). As the validity test of our methodology, the test area is divided into two parts and the corresponding potential difference and gravity difference observations are introduced into our GBVP separately and the bias of height datums of the two parts are computed with respect to the geoid. Obtaining approximately the same bias values for the height datums of the two parts being part of one height datum with one origin point proves the validity of our approach. Besides, the latter test shows the capability of our methodology for patch-wise application.