Trend Analysis of Small Scale Commercial Sugarcane Production in Post Resettlement Areas of Mkwasine Zimbabwe, Using Hyper-Temporal Satellite Imagery

The paper presented is dedicated to the evaluation of the influence of various improvements to the numerical weather prediction (NWP) systems exploited at the Slovak Hydrometeorological Institute (SHMÚ). The impact was illustrated in a case study with multicell thunderstorms and the results were confronted with the reference analyses from the INCA nowcasting system, regional radar reflectivity data, and METEOSAT satellite imagery. The convective cells evolution was diagnosed in non-hydrostatic dynamics experiments to study weak mesoscale vortices and updrafts. The growth of simulated clouds and evolution of the temperature at their top were compared with the brightness temperature analyzed from satellite imagery. The results obtained indicated the potential for modeling and diagnostics of small-scale structures within the convective cloudiness, which could be related to severe weather. Furthermore, the non-hydrostatic dynamics experiments related to the stability and performance improvement of the time scheme led to the formulation of a new approach to linear operator definition for semi-implicit scheme (in text referred as NHHY). We demonstrate that the execution efficiency has improved by more than 20%. The exploitation of several high resolution measurement types in data assimilation contributed to more precise position of predicted patterns and precipitation representation in the case study. The non-hydrostatic dynamics provided more detailed structures. On the other hand, the potential of a single deterministic forecast of prefrontal heavy precipitation was not as high as provided by the ensemble system. The prediction of a regional ensemble system A-LAEF (ALARO Limited Area Ensemble Forecast) enhanced the localization of precipitation patterns. Though, this was rather due to the simulation of uncertainty in the initial conditions and also because of the stochastic perturbation of physics tendencies. The various physical parameterization setups of A-LAEF members did not exhibit a systematic effect on precipitation forecast in the evaluated case. Moreover, the ensemble system allowed an estimation of uncertainty in a rapidly developing severe weather case, which was high even at very short range.

Download Full-text

Estimating Areas of Land under Small-Scale Irrigation Using Satellite Imagery and Ground Data for a Study Area in N.E. Nigeria

Geographical Journal ◽

10.2307/3059687 ◽

1997 ◽

Vol 163 (1) ◽

pp. 65 ◽

Cited By ~ 2

Author(s):

N. D. Pratt ◽

A. C. Bird ◽

J. C. Taylor ◽

R. C. Carter

Keyword(s):

Satellite Imagery ◽

Small Scale ◽

Small Scale Irrigation

Download Full-text

The Rise and Decline of Small-Scale Sugarcane Production in South Africa: A Historical Perspective

Journal of Agrarian Change ◽

10.1111/joac.12107 ◽

2015 ◽

Vol 16 (4) ◽

pp. 518-542 ◽

Cited By ~ 12

Author(s):

Alex Dubb

Keyword(s):

South Africa ◽

Historical Perspective ◽

Small Scale ◽

Sugarcane Production

Download Full-text

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne lidar data

10.5194/tc-2020-15 ◽

2020 ◽

Cited By ~ 2

Author(s):

César Deschamps-Berger ◽

Simon Gascoin ◽

Etienne Berthier ◽

Jeffrey Deems ◽

Ethan Gutmann ◽

...

Keyword(s):

Satellite Imagery ◽

Random Error ◽

Snow Depth ◽

Depth Map ◽

Airborne Lidar ◽

Small Scale ◽

High Mountain ◽

Catchment Scale ◽

Absolute Deviation ◽

Tuolumne River

Abstract. An accurate knowledge of snow depth distribution in mountain catchments is critical for applications in hydrology and ecology. A recent new method was proposed to map the snow depth at meter-scale resolution from very-high resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.50 m. However, the validation was mainly done using probe measurements which sampled a limited fraction of the topographic and snow depth variability. We deepen this evaluation using accurate maps of the snow depth derived from ASO airborne lidar measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 137 km2 on a 3 m grid with a positive bias for Pléiades snow depth of 0.08 m, a root-mean-square error of 0.80 m and a normalized median absolute deviation of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale, and also small-scale features like snow drifts and avalanche deposits. The random error on snow depth can be reduced by a factor two (up to approximately 0.40 m) when the snow depth map is spatially averaged to a ~ 20 m grid. The random error at the pixel level is lower on snow-free areas than on snow-covered areas, but errors on both terrain type converge at coarser resolutions, which is important for further applications of the method in areas without snow depth reference data. We conclude that satellite photogrammetry stands out as an efficient method to estimate the spatial distribution of snow depth in high mountain catchments.

Download Full-text

Shining Light on Data-Poor Coastal Fisheries

Frontiers in Marine Science ◽

10.3389/fmars.2020.625766 ◽

2021 ◽

Vol 7 ◽

Author(s):

Owen M. Exeter ◽

Thaung Htut ◽

Christopher R. Kerry ◽

Maung Maung Kyi ◽

Me'ira Mizrahi ◽

...

Keyword(s):

Southeast Asia ◽

Satellite Imagery ◽

Low Income ◽

Marine Ecosystem ◽

Small Scale ◽

Marine Biodiversity ◽

Artificial Light ◽

Coastal Fisheries ◽

Fishing Vessels ◽

Biomass Removal

Coastal fisheries provide livelihoods and sustenance for millions of people globally but are often poorly documented. Data scarcity, particularly relating to spatio-temporal trends in catch and effort, compounds wider issues of governance capacity. This can hinder the implementation and effectiveness of spatial tools for fisheries management or conservation. This issue is acute in developing and low-income regions with many small-scale inshore fisheries and high marine biodiversity, such as Southeast Asia. As a result, fleets often operate unmonitored with implications for target and non-target species populations and the wider marine ecosystem. Novel and cost-effective approaches to obtain fisheries data are required to monitor these activities and help inform sustainable fishery and marine ecosystem management. One such example is the detection and numeration of fishing vessels that use artificial light to attract catch with nighttime satellite imagery. Here we test the efficiency and application value of nighttime satellite imagery, in combination with landings data and GPS tracked vessels, to estimate the footprint and biomass removal of an inshore purse seine fishery operating within a region of high biodiversity in Myanmar. By quantifying the number of remotely sensed vessel detections per month, adjusted for error by the GPS tracked vessels, we can extrapolate data from fisher logbooks to provide fine-scale spatiotemporal estimates of the fishery's effort, value and biomass removal. Estimates reveal local landings of nearly 9,000 mt worth close to $4 million USD annually. This approach details how remote sensed and in situ collected data can be applied to other fleets using artificial light to attract catch, notably inshore fisheries of Southeast Asia, whilst also providing a much-needed baseline understanding of a data-poor fishery's spatiotemporal activity, biomass removal, catch composition and landing of vulnerable species.

Download Full-text

Adoption, Cost and Livelihood Impact of Machinery Services Used in Small-Scale Sugarcane Production in Thailand

Sugar Tech ◽

10.1007/s12355-018-0651-x ◽

2018 ◽

Vol 21 (4) ◽

pp. 543-556 ◽

Cited By ~ 6

Author(s):

Wirawat Chaya ◽

Boosya Bunnag ◽

Shabbir H. Gheewala

Keyword(s):

Small Scale ◽

Sugarcane Production

Download Full-text

Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data

The Cryosphere ◽

10.5194/tc-14-2925-2020 ◽

2020 ◽

Vol 14 (9) ◽

pp. 2925-2940 ◽

Cited By ~ 4

Author(s):

César Deschamps-Berger ◽

Simon Gascoin ◽

Etienne Berthier ◽

Jeffrey Deems ◽

Ethan Gutmann ◽

...

Keyword(s):

Satellite Imagery ◽

Snow Depth ◽

Laser Scanning ◽

Unmanned Aircraft ◽

Small Scale ◽

High Mountain ◽

Catchment Scale ◽

Absolute Deviation ◽

Covered Area ◽

Tuolumne River

Abstract. Accurate knowledge of snow depth distributions in mountain catchments is critical for applications in hydrology and ecology. Recently, a method was proposed to map snow depth at meter-scale resolution from very-high-resolution stereo satellite imagery (e.g., Pléiades) with an accuracy close to 0.5 m. However, the validation was limited to probe measurements and unmanned aircraft vehicle (UAV) photogrammetry, which sampled a limited fraction of the topographic and snow depth variability. We improve upon this evaluation using accurate maps of the snow depth derived from Airborne Snow Observatory laser-scanning measurements in the Tuolumne river basin, USA. We find a good agreement between both datasets over a snow-covered area of 138 km2 on a 3 m grid, with a positive bias for a Pléiades snow depth of 0.08 m, a root mean square error of 0.80 m and a normalized median absolute deviation (NMAD) of 0.69 m. Satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits at a typical scale of tens of meters. The random error at the pixel level is lower in snow-free areas than in snow-covered areas, but it is reduced by a factor of 2 (NMAD of approximately 0.40 m for snow depth) when averaged to a 36 m grid. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountain catchments.

Download Full-text