Catalytic Conversion of Hydrocarbons to Hydrogen and High-Value Carbon

The present invention provides novel catalysts for accomplishing catalytic decomposition of undiluted light hydrocarbons to a hydrogen product, and methods for preparing such catalysts. In one aspect, a method is provided for preparing a catalyst by admixing an aqueous solution of an iron salt, at least one additional catalyst metal salt, and a suitable oxide substrate support, and precipitating metal oxyhydroxides onto the substrate support. An incipient wetness method, comprising addition of aqueous solutions of metal salts to a dry oxide substrate support, extruding the resulting paste to pellet form, and calcining the pellets in air is also discloses. In yet another aspect, a process is provided for producing hydrogen from an undiluted light hydrocarbon reactant, comprising contacting the hydrocarbon reactant with a catalyst as described above in a reactor, and recovering a substantially carbon monoxide-free hydrogen product stream. In still yet another aspect, a process is provided for catalytic decomposition of an undiluted light hydrocarbon reactant to obtain hydrogen and a valuable multi-walled carbon nanotube coproduct

Crop type mapping using high-resolution Sentinel-2 Satellite Data– A case study on Gujarat State

Mapping following products of Gujarat state using high-resolution Sentinel-2 satellite data 1. Crop type 2. Irrigated and Rainfed area Major Activities • Satellite imagery acquisition/ procurement and pre-processing (Sentinel-2, 10&20 m spatial resolution). • Field information (ground reference data) and farmer interviews at selected locations and collection of validation points. • Satellite Imagery analysis and interpretation for land use / land cover areas including irrigated and rainfed cultivated areas

Pilot studies on GP Crop yield estimation using Technology (Kharif 2019) using SENTINEL- 2 satellite data (in Andhra Pradesh, Telangana and Odisha States (Five Districts)) for Groundnut, Chickpea, Maize and Rice

The Government of India plans to optimize Crop Cutting Experiments (CCEs) using different technologies including satellite derived metrics on crop performance and spatial variability to guide the selection and number of ground data sites. This requires the development of an approach for different crops for the different agro-climatic regions of India. The present study plans to develop an approach for following crops viz., Groundnut, Chickpea, Rice and Maize. The above crops will be studied in five districts of three states viz. Andhra Pradesh, Telangana and Odisha. The study will use comprehensive and existing environmental, weather and management data along with satellite derived crop spatial data. This information will be modelled using statistical optimization techniques to assess the optimal numbers of CCE’s that can be undertaken

Impacts of irrigation tank restoration on water bodies and croplands in Telangana State of India using Landsat time series data and machine learning algorithms

In 2014, the State of Telangana in southern India began repairing and restoring more than 46,000 irrigation water tanks (artificial reservoirs) under the Mission Kakatiya project with an investment in excess of USD 2 billion. In this study, we attempted to map the temporal changes that have occurred in cropland areas and water bodies as a result of the project, using remote sensing imagery and applying land use/land cover (LULC) mapping algorithms. We used 16-day time series data from Landsat 8 to study the spatial distribution of changes in water bodies and cropland areas over the 2013–18 period. Ground survey information was used to assess the pixel-based accuracy of the Landsat-derived data. The areas served by these tanks were identified on the basis of training data and Random Forest algorithms using Google Earth Engine. Our spatial analysis revealed a substantial increase in cropped area under irrigation and expansion of water bodies over the study period. We observed a 20% increase in total tank area in 2017–18 and total cropland and irrigated area expansion of the order of 0.6M ha and 0.2M ha, respectively. A comparison of ground survey data and four LULC classes derived from Landsat temporal imagery showed an overall accuracy of 87%, significantly correlated with national agriculture statistics. Periodic monitoring based on remote sensing has proved to be an effective method of capturing LULC changes resulting from the Mission Kakatiya interventions. Higher-resolution satellite data can further improve the accuracy of estimates

Assessment of Cropland Changes Due to New Canals in Vientiane Prefecture of Laos using Earth Observation Data

The lower catchment area of a Mak Hiao river system is vulnerable to flash floods and water stress. So it is important to construct irrigation structures in this area to minimize floods during the rainy season and store water for the winter season. The Asian Development Bank (ADB) has been supporting the Government of Laos in constructing such small reservoirs like Donkhuay schemes 1 & 2, Mak Hiao, Nalong 3 and Sang Houabor projects in lower catchment areas. Our study evaluated the impacts of small irrigation schemes in terms of land-use/landcover (LULC), crop intensity, and productivity changes, using high resolution satellite imagery, socioeconomic, and ground data. We analyzed the temporal cropping pattern in the Vientiane prefecture of Laos using Planet and Sentinel-2 data. On the other hand, crop intensity and cropland changes were mapped using Sentinel-2 data and spectral matching techniques (SMTs). The crop classification accuracy based on field-plot data was 88.6%. Our results show that irrigation projects in the lower catchment areas brought about significant on-site changes in terms of cropland expansion and increased crop intensity. Remarkable changes in LULC were observed especially in the command areas owing to an increase of about 300% in crop area with access to irrigation and increase of water bodies by 31%. Our study found that interventions at the level of the command area do improved on-site soil, water and environmental services. They study emphasized underline the role of land-use regulations in reducing pressure on natural land-use systems and thereby serving the major goal of up-scaling sustainable natural resource management. The study documented the vital role of small/medium irrigation projects in restoring ecosystem services such as cropping patterns and LULC conversio

Identifying Suitable Watersheds across Nigeria Using Biophysical Parameters and Machine Learning Algorithms for Agri–Planning

Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development

Assessing the impacts of watershed interventions using ground data and remote sensing: a case study in Ethiopia

Quantifying the temporal and spatial changes due to watershed interventions is important for assessing the effectiveness of natural resource management practices on vegetative cover and sediment management. This study assessed the performance of natural resource management in a target site (Aba Gerima) and compared the collateral impacts on neighbouring watersheds in Ethiopia in terms of land-use land-cover change. Changes in the extent of cropland, grassland and shrubland were assessed in the target watershed and the non-treated neighbouring watersheds using temporal satellite imagery. In addition, ground monitoring was applied to quantify the impacts on sediment accumulation, fodder biomass and vegetative cover intensity. The study findings showed substantial changes over the study period: mainly, a change from degraded and barren land to restored vegetation in the target watershed, but a continued trend of land-use change from perennial vegetation to cropland in the neighbouring untreated watersheds. There was a decrease in the rate of conversion of vegetative land cover to cropland in the target watershed, and significant on-site changes in sediment retention, fodder productivity and vegetation intensity. The study findings demonstrate a link between management interventions and improvement in soil and vegetation ecosystem functions. These results not only indicate that watershed-level interventions improve on-site soil and water environmental services but also underline the role of community managed land-use regulations in reducing pressure on natural land-use systems and thereby serve the major goal of up-scaling sustainable land management

Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

Crop yield estimation is important to inform logistics management such as the prescription of nutrient inputs, financing, storage and transport, marketing as well as to inform for crop insurance appraisals due to loss incurred by abiotic and biotic stresses. In this study, we used a suite of methods to assess yields at the village level (\5 km2) using remote sensing technology and crop modeling in Indian states of Telangana, Andhra Pradesh and Odisha. Remote sensing products were generated using Sentinel-2 and Landsat 8 time series data and calibrated with data collected from farmers’ fields. We derived maps showing spatial variation in crop extent, crop growth stages and leaf area index (LAI), which are crucial in yield assessment. Crop classification was performed on Sentinel-2 time series data using spectral matching techniques (SMTs) and crop management information collected from field surveys along with ground data. The locations of crop cutting experiments (CCEs) was identified based on crop extent maps. LAI was derived based on the SAVI (soil-adjusted vegetation index) equation were using Landsat 8-time series data. We used the technique of re-parametrization of crop simulation models based on the several iterations using remote sensing leaf area index (LAI). The data assimilation approach helps in fine-tuning the initial parameters of the crop growth model and improving simulation with the help of remotely sensed observations. Results clearly show a good correlation between observed and simulated crop yields (R2 is greater than 0.7) for all the crops studied. Our study showed that by assimilation of remotely sensed data in to crop models, crop yields at harvest could be successfully predicted

Assessing potential locations for flood-based farming using satellite imagery: a case study of Afar region, Ethiopia

The dry lowlands of Ethiopia are seasonally affected by long periods of low rainfall and, coinciding with rainfall in the Amhara highlands, flood waters which flow onto the lowlands resulting in damage to landscapes and settlements. In an attempt to convert water from storm generated floods into productive use, this study proposes a methodology using remote sensing data and geographical information system tools to identify potential sites where flood spreading weirs may be installed and farming systems developed which produce food and fodder for poor rural communities. First, land use land cover maps for the study area were developed using Landsat-8 and MODIS temporal data. Sentinel-1 data at 10 and 20 m resolution on a 12-day basis were then used to determine flood prone areas. Slope and drainage maps were derived from Shuttle RADAR Topography Mission Digital Elevation Model at 90 m spatial resolution. Accuracy assessment using ground survey data showed that overall accuracies (correctness) of the land use/land cover classes were 86% with kappa 0.82. Coinciding with rainfall in the uplands, March and April are the months with flood events in the short growing season (belg) and June, July and August have flood events during the major (meher) season. In the Afar region, there is potentially >0.55 m ha land available for development using seasonal flood waters from belg or meher seasons. During the 4 years of monitoring (2015–2018), a minimum of 142,000 and 172,000 ha of land were flooded in the belg and meher seasons, respectively. The dominant flooded areas were found in slope classes of <2% with spatial coverage varying across the districts. We concluded that Afar has a huge potential for flood-based technology implementation and recommend further investigation into the investments needed to support new socio-economic opportunities and implications for the local agro-pastoral communities

Machine Learning Approaches and Sentinel-2 Data in Crop Type Mapping

Crop monitoring becomes essential in attaining food security for implementation of various agricultural serving programs. So, fast and reliable crop monitoring is must. Using traditional methods, crop monitoring maps need high amount of satellite data downloading and processing time. Google Earth Engine (GEE) cloud platform enables us to save time in downloading and processing of time series satellite data, the every satellite imagery is converted into Normalized Difference Vegetation Index (NDVI) image and stacked monthlywisemaximum images. The stacked image was used for conducting supervised classification. The main objective of this study is to evaluate the performance of different supervised machine learning (ML) classifiers in GEE platform and Spectral Matching Technique (SMT) using Sentinel-2 10 m satellite imagery in specific crop type classification. The crop classification for the year 2018–19 (rabi season) was carried for Jhansi District using supervised classifiers like Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Trees (CART) in GEE platform and also with SMT with the help of ground data. It was attained nearly 81.8% accuracy for RF, 68.8% for SVM, 64.9% for CART and 88% for SMT. The results obtained using RF classifier were nearly relative to SMT classification map. The study indicates that classifier’s performance depends on the quality of ground data used, RF can reduce the error samples in ground samples and produce satisfactory results. This study compared results obtained from all the above classifiers with agricultural statistics and also compared crop-wise accuracies. In the study, it was observed that RF classification is outperformed when compared with other classifiers considered in the study