Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.
ABSTRACT:Cultural heritage structural documentation is of great importance in terms of historical preservation, tourism, educational and spiritual values. Cultural heritage across the world, and in Nepal in particular, is at risk from various natural hazards (e.g. earthquakes, flooding, rainfall etc), poor maintenance and preservation, and even human destruction. This paper evaluates the feasibility of low-cost photogrammetric modelling cultural heritage sites, and explores the practicality of using photogrammetry in Nepal. The full pipeline of 3D modelling for heritage documentation and conservation, including visualisation, reconstruction, and structure analysis, is proposed. In addition, crowdsourcing is discussed as a method of data collection of growing prominence.
After the political change in Nepal of 1951, leapfrog land policy improvements have been recorded, however, the land reform initiatives have been short of full success. Despite a land administration system based on cadaster and land registries in place, 25% of the arable land with an estimated 10 million spatial units on the ground are informally occupied and are off-register. Recently, a strong political will has emerged to ensure land rights for all. Providing tenure security to all these occupants using the conventional surveying and land administration approach demands a large amount of skilled human resources, a long timeframe and a huge budget. To assess the suitability of the fit-for-purpose land administration (FFPLA) approach for nationwide mapping and registration of informality in the Nepalese context, the identification, verification and recordation (IVR) of the people-to-land relationship was conducted through two pilot studies using a participatory approach covering around 1500 and 3400 parcels, respectively, in an urban and a rural setting. The pilot studies were based on the FFPLA National Strategy and utilized satellite imageries and smartphones for identification and verification of land boundaries. Data collection to verification tasks were completed within seven months in the urban settlements and for an average cost of 7.5 USD per parcel; within the rural setting, the pilot study was also completed within 7 months and for an average cost of just over 3 USD per parcel. The studies also informed the discussions on building the legislative and institutional frameworks, which are now in place. With locally trained ‘grassroots surveyors’, the studies have provided a promising alternative to the conventional surveying technologies by providing a fast, inexpensive and acceptable solution. The tested approach may fulfill the commitment to resolve the countrywide mapping of informality. The use of consistent data model and mapping standards are recommended.
ABSTRACT:In literature and in photogrammetric workstations many approaches and systems to automatically reconstruct buildings from remote sensing data are described and available. Those building models are being used for instance in city modeling or in cadastre context. If a roof overhang is present, the building walls cannot be estimated correctly from nadir-view aerial images or airborne laser scanning (ALS) data. This leads to inconsistent building outlines, which has a negative influence on visual impression, but more seriously also represents a wrong legal boundary in the cadaster. Oblique aerial images as opposed to nadir-view images reveal greater detail, enabling to see different views of an object taken from different directions. Building walls are visible from oblique images directly and those images are used for automated roof overhang estimation in this research. A fitting algorithm is employed to find roof parameters of simple buildings. It uses a least squares algorithm to fit projected wire frames to their corresponding edge lines extracted from the images. Self-occlusion is detected based on intersection result of viewing ray and the planes formed by the building whereas occlusion from other objects is detected using an ALS point cloud. Overhang and ground height are obtained by sweeping vertical and horizontal planes respectively. Experimental results are verified with high resolution ortho-images, field survey, and ALS data. Planimetric accuracy of 1cm mean and 5cm standard deviation was obtained, while buildings' orientation were accurate to mean of 0.23• and standard deviation of 0.96• with ortho-image. Overhang parameters were aligned to approximately 10cm with field survey. The ground and roof heights were accurate to mean of −9cm and 8cm with standard deviations of 16cm and 8cm with ALS respectively. The developed approach reconstructs 3D building models well in cases of sufficient texture. More images should be acquired for completeness of overhang results and automatic accuracy check of roof parameters.
A drought is a period of time when an area or region experiences below-normal precipitation, with characteristics and impacts that can vary from region to region. Agricultural Drought analyzes and reflects the extent of the soil moisture and morphology of crop. Deficient rainfall in the winter of 2008 resulted in a severe drop in crop production right across the country. So, there is a necessity of assessment of drought events to make informed and timely decisions. The main focus of our study is to monitor the agricultural drought in Karnali and Sudurpashchim provinces of Nepal. The condition of drought in Karnali and Sudurpashchim provinces from 2001- 2020 were analysed with the help of Drought Severity Index. MODIS NDVI (MOD13) and MODIS ET-PET (MOD16) datasets were used to monitor and analyze the trend and pattern of agricultural drought scenario. Both datasets were then normalized for DSI calculation and the DSI result was used to monitor and to analyze the trend and pattern in the agricultural drought scenario. Further, trend and pattern analyses were performed in terms of landcover, ecological zones, and the variation of DSI. After completion of this project, we can conclude that the Maximum dryness was found in March, it might be due to less NDVI and increase in evapotranspiration rate and maximum wetness in November. Agricultural area experienced more drought variation than other landcover zones
Abstract. Forest biomass is the sum of above ground living organic material contained in trees which is expressed as dry weight per unit area. Forest biomass acts as substantial terrestrial carbon sinks, they are estimated to absorb 2.7 Petagrams of carbon per year, as such accurate estimation of forest carbon stock is very important. The estimation of biomass is also important because of its application in commercial exploitation as well as in global carbon cycle. Particularly in the latter context, the estimation of the total above-ground biomass (TAGB) with sufficient accuracy is vital in reporting the spatial and temporal state of forest under the United Nations Framework Convention on Climate Change (UNFCCC), Reducing Emissions from Deforestation in Developing Countries (REDD). In this research, tree height, DBH and crown cover were measured using field instruments. Individual ultra-high-resolution UAV images acquired using customized Visible-NIR, were georeferenced and tree crown were extracted using multi-resolution segmentation. A regression equation between field measured biomass and Crown Projection Area (CPA) was developed. The paper presents results from Barandabhar Forest of Chitwan District, Nepal. RMSE of ortho-mosaic was found to be 18 cm. While R2 value of 89% was obtained for relationship between DBH and biomass, that of 61% was attained for relationship between CPA and biomass.
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