scholarly journals Crop Suitability Mapping for Rice, Cassava, and Yam in North Central Nigeria

2016 ◽  
Vol 9 (1) ◽  
pp. 96 ◽  
Author(s):  
Roland Clement Abah ◽  
Brilliant Mareme Petja
Keyword(s):  
Crop Yield ◽  
Satellite Imagery ◽  
Agricultural Development ◽  
Vegetation Index ◽  
Policy Makers ◽  
Rural Economic Development ◽  
Crop Suitability ◽  
Normalised Difference Vegetation Index ◽  
Development Data ◽  
Elevation Model

<p>Agricultural production has contributed over time to food security and rural economic development in developing countries particularly supporting the countryside. Evidence of crop yield decline exist in the Lower River Benue Basin. This was a crop suitability mapping for rice, cassava, and yam to guide policy makers in strategic planning for sustainable agricultural development. Data was collected on various themes including climate, drainage, soil, satellite imagery, and maps. Remote Sensing was used to analyse satellite imagery to produce a digital elevation model, land use and land cover map, and normalised difference vegetation index map. GIS was used to produce thematic maps, weighted percentages of attribute data, and to produce crop suitability maps through weighted overlay. Soils in the study area require fertility enhancement with inorganic fertilisers for better crop yield. Soils in the Lower River Benue Basin are suitable for yam, cassava, and rice cultivation on maps of suitable areas. Some areas were found to be highly suitable for the cultivation of rice (34.22%), cassava (17.08%) and yam (16.08%). Some other areas were found to be moderately suitable for the cultivation of cassava (48.18%), rice (45.46%), and yam (48.85%). Areas with low suitability were 14.99% (rice), 33.68% (cassava), and 29.57% (yam). This study has demonstrated the importance of crop suitability mapping and recommends that farmers’ cooperative societies and policy makers utilise the information presented to improve decision making methods and policies for agricultural development.</p>

scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

scholarly journals Estimation and monitoring of aboveground carbon stocks using spatial technology

2015 ◽  
Vol 111 (9/10) ◽  
Author(s):  
Adolph Nyamugama ◽  
Vincent Kakembo
Keyword(s):  
Regression Model ◽  
Satellite Imagery ◽  
Geographical Information Systems ◽  
Vegetation Index ◽  
Anthropogenic Activities ◽  
Temporal Changes ◽  
Geographical Information ◽  
Aboveground Carbon ◽  
Normalised Difference Vegetation Index ◽  
Spot 5

Monitoring temporal changes of aboveground carbon (AGC) stocks distribution in subtropical thicket is key to understanding the role of vegetation in carbon sequestration. The main objectives of this research paper were to model and quantify the temporal changes of AGC stocks between 1972 and 2010 in the Great Fish River Nature Reserve and its environs, Eastern Cape Province, South Africa. We used a method based on the integration of remote sensing and geographical information systems to estimate AGC stocks in a time series framework. A non-linear regression model was developed using Normalised Difference Vegetation Index values generated from SPOT 5 High Resolution Geometric satellite imagery of 2010 as an independent variable and AGC stock estimates from field plots as the dependent variable. The regression model was used to estimate AGC stocks from satellite imagery for 1972 (Landsat TM), 1982 (Landsat 4 TM), 1992 (Landsat 7 ETM), 2002 (Landsat ETM+) and 2010 (SPOT 5) satellite imagery. AGC stocks for the respective years were compared by means of change detection analysis at the subtropical thicket class level. The results showed a decline of AGC stocks in all the classes from 1972 to 2010. Degraded and transformed thicket classes had the highest AGC stock losses. The decline of AGC stocks was attributed to thicket transformation and degradation, which were attributed to anthropogenic activities.

Mapping prescribed fire severity in south-east Australian eucalypt forests using modelling and satellite imagery: a case study

2017 ◽  
Vol 26 (6) ◽  
pp. 491 ◽  
Author(s):  
John Loschiavo ◽  
Brett Cirulis ◽  
Yingxin Zuo ◽  
Bronwyn A. Hradsky ◽  
Julian Di Stefano
Keyword(s):  
Satellite Imagery ◽  
Prescribed Fire ◽  
Vegetation Index ◽  
Fire Severity ◽  
Kappa Statistic ◽  
Fire Spread ◽  
Management Tool ◽  
Eucalypt Forest ◽  
Normalised Difference Vegetation Index ◽  
Eucalypt Forests

Accurate fire severity maps are fundamental to the management of flammable landscapes. Severity mapping methods have been developed and tested for wildfire, but need further refinement for prescribed fire. We evaluated the accuracy of two severity mapping methods for a low-intensity, patchy prescribed fire in a south-eastern Australian eucalypt forest: (1) the Normalised Difference Vegetation Index (NDVI) derived from RapidEye satellite imagery, and (2) PHOENIX RapidFire, a fire-spread simulation model. We used each method to generate a fire severity map (four-category: unburnt, low, moderate and severe), and then validated the maps against field-based data. We used error matrices and the Kappa statistic to assess mapping accuracy. Overall, the satellite-based map was more accurate (75%; Kappa±95% confidence interval 0.54±0.06) than the modelled map (67%; Kappa 0.40±0.06). Both methods overestimated the area of unburnt forest; however, the satellite-based map better represented moderately burnt areas. Satellite- and model-based methods both provide viable approaches for mapping prescribed fire severity, but refinements could further improve map accuracy. Appropriate severity mapping methods are essential given the increasing use of prescribed fire as a forest management tool.

Predicting accurate paddock-average pasture cover in Waikato dairy farms using satellite images

2007 ◽  
pp. 23-28 ◽  
Author(s):  
G. Mata ◽  
D.A. Clark ◽  
A. Edirisinghe ◽  
D. Waugh ◽  
E. Minneé ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Model Development ◽  
Critical Time ◽  
Dairy Farm ◽  
Dairy Farms ◽  
Exponential Model ◽  
Inflexion Point ◽  
Pasture Management ◽  
Normalised Difference Vegetation Index

Effective monitoring of pasture cover on a regular basis is essential if dairy farmers are to increase profitability by making better pasture management decisions. We present results of a two-year study on the use of satellite imagery to estimate pasture cover on dairy farms in the Waikato region. Data collection concentrated on the critical time for dairy farm pasture management between June and December. Two distinct relationships between the remotely-sensed normalised difference vegetation index (NDVI) and pasture cover were observed, with an inflexion point in the relationship at NDVI = 0.74. A two-part exponential model was fitted to the data, allowing the prediction error to be minimised both above and below the inflexion point. Model development showed that an algorithm based on NDVI and time-of-year accounted for approximately 80% of the variability in pasture cover measured within paddocks. The validation studies show that pasture cover was estimated with an error of prediction of approximately 10%, which equates to 260 kg DM/ha for a pasture cover range of 1500 to 3400 kg DM/ha. The accuracy demonstrated in this study has given the project's funders the confidence to explore a staged rollout of development, validation and commercial delivery, to make the technology available to all major dairy regions in New Zealand over the next five years. Keywords: Pasture cover, dairy, satellite imagery, rising plate meter, normalised difference vegetation index

Monitoring Land and Water Use in the Gefara Region by Satellite Imagery

1981 ◽  
Vol 12 ◽  
pp. 79-80
Author(s):  
J. A. Allan ◽  
J. S. Latham ◽  
R. Warwick-Smith
Keyword(s):  
Water Balance ◽  
Water Use ◽  
Satellite Imagery ◽  
Agricultural Development ◽  
Private Investment ◽  
Public And Private ◽  
Policy Makers ◽  
North West ◽  
Before And After ◽  
Development Plans

Over ninety-seven per cent of Libya's water for agricultural and other uses came from groundwater in 1980 (Pallas). By then it was obvious that the renewable water in the coastal aquifers was seriously depleted and would not be sufficient to sustain the levels of water use implied by the national agricultural development plans. Meanwhile the potential of the southern aquifers had become apparent through their development in Kufrah, Sarir and Fezzan.The Gefara Plain has always been the major agricultural area of the country and until 1973, both before and after independence, had always attracted the bulk of public and private investment in irrigation. The decline in the level of the coastal groundwater was observed, or predicted, by all hydro-geologists who looked at the Gefara Plain after 1950, although it should be remarked that the observations were made on the basis of imprecise and unrepresentative data. Even by 1980 figures on groundwater recharge (estimated from landuse and assumed pumping levels) were numerous and inconsistent making it difficult for policy makers to determine the economies required and the measures necessary to achieve the optimum management of water resources. Depending upon which estimates of recharge and withdrawal were used, the amount by which withdrawal exceeded recharge varied from three to eight times.In these circumstances it was obviously prudent to attempt to determine the water balance from the Gefara Plain, and to this end a study was commissioned by FAO, on behalf of the Secretariat of Agriculture in Tripoli, for a study of recent satellite imagery to determine whether this inexpensive system of resources survey could make a contribution to planning water use in north-west Libya. By providing data on the irrigated area. From such data the water use element of the water balance equation could be estimated.

scholarly journals Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment

2019 ◽  
Vol 11 (4) ◽  
pp. 436 ◽  
Author(s):  
Aleem Khaliq ◽  
Lorenzo Comba ◽  
Alessandro Biglia ◽  
Davide Ricauda Aimonino ◽  
Marcello Chiaberge ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Aerial Images ◽  
Spatial And Temporal Variability ◽  
Multispectral Imagery ◽  
Remotely Sensed Data ◽  
Spectral Contribution ◽  
Normalised Difference Vegetation Index ◽  
Soil Status

In agriculture, remotely sensed data play a crucial role in providing valuable information on crop and soil status to perform effective management. Several spectral indices have proven to be valuable tools in describing crop spatial and temporal variability. In this paper, a detailed analysis and comparison of vineyard multispectral imagery, provided by decametric resolution satellite and low altitude Unmanned Aerial Vehicle (UAV) platforms, is presented. The effectiveness of Sentinel-2 imagery and of high-resolution UAV aerial images was evaluated by considering the well-known relation between the Normalised Difference Vegetation Index (NDVI) and crop vigour. After being pre-processed, the data from UAV was compared with the satellite imagery by computing three different NDVI indices to properly analyse the unbundled spectral contribution of the different elements in the vineyard environment considering: (i) the whole cropland surface; (ii) only the vine canopies; and (iii) only the inter-row terrain. The results show that the raw s resolution satellite imagery could not be directly used to reliably describe vineyard variability. Indeed, the contribution of inter-row surfaces to the remotely sensed dataset may affect the NDVI computation, leading to biased crop descriptors. On the contrary, vigour maps computed from the UAV imagery, considering only the pixels representing crop canopies, resulted to be more related to the in-field assessment compared to the satellite imagery. The proposed method may be extended to other crop typologies grown in rows or without intensive layout, where crop canopies do not extend to the whole surface or where the presence of weeds is significant.

scholarly journals Exploring the Potential of High Resolution Satellite Imagery for Yield Prediction of Avocado and Mango Crops

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 154
Author(s):  
Moshiur Rahman ◽  
Andrew Robson ◽  
Surantha Salgadoe ◽  
Kerry Walsh ◽  
Mila Bristow
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Mangifera Indica ◽  
Linear Regression Analysis ◽  
Vegetation Indices ◽  
Persea Americana ◽  
Yield Estimation ◽  
Yield Parameters ◽  
Normalised Difference Vegetation Index

Accurate pre-harvest yield estimation of high value fruit tree crops provides a range of benefits to industry and growers. Currently, yield estimation in Avocado (Persea americana) and Mango (Mangifera indica) orchards is undertaken by a visual count of a limited number of trees. However, this method is labour intensive and can be highly inaccurate if the sampled trees are not representative of the spatial variability occurring across the orchard. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of yield. A stratified sampling technique was applied in each block to measure relevant yield parameters from eighteen sample trees representing high, medium and low vigour zones (6 from each) based on classified normalised difference vegetation index (NDVI) maps. For avocado crops, principal component analysis (PCA) and non-linear regression analysis were applied to 18 derived vegetation indices (VIs) to determine the index with the strongest relationship to the measured yield parameters. For mango, an integrated approach of geometric (tree crown area) and optical (spectral vegetation indices) data using artificial neural network (ANN) model produced more accurate predictions. The results demonstrate that accurate maps of yield variability and total orchard yield can be achieved from WV imagery and targeted sampling; whilst accurate maps of fruit size and the incidence of phytophthora can also be achieved in avocado. These outcomes offer improved forecasting than currently adopted practices and therefore offer great benefit to both the avocado and mango industries.

scholarly journals Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

2021 ◽  
Vol 13 (18) ◽  
pp. 3550
Author(s):  
David Moravec ◽  
Jan Komárek ◽  
Serafín López-Cuervo Medina ◽  
Iñigo Molina
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Atmospheric Correction ◽  
Correction Method ◽  
Landsat 8 ◽  
Substantial Impact ◽  
Corrected Image ◽  
Normalised Difference Vegetation Index ◽  
Urban Rural ◽  
Sentinel 2

Sentinel-2 and Landsat 8 satellites constitute an unprecedented source of freely accessible satellite imagery. To produce precise outputs from the satellite data, however, proper use of atmospheric correction methods is crucial. In this work, we tested the performance of six different atmospheric correction methods (QUAC, FLAASH, DOS, ACOLITE, 6S, and Sen2Cor), together with atmospheric correction given by providers, non-corrected image, and images acquired using an unmanned aerial vehicle while working with the normalised difference vegetation index (NDVI) as the most widely used index. We tested their performance across urban, rural, and vegetated land cover types. Our results show a substantial impact from the choice of the atmospheric correction method on the resulting NDVI. Moreover, we demonstrate that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery.

Using deep learning to transfer knowledge between satellite datasets for automated agricultural land discrimination in Afghanistan

2020 ◽  
Author(s):  
Alex Hamer ◽  
Daniel Simms ◽  
Toby Waine
Keyword(s):  
Deep Learning ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Spectral Characteristics ◽  
Classification Performance ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Normalised Difference Vegetation Index ◽  
Drugs And Crime

&lt;p&gt;Accurate mapping of agricultural area is essential for Afghanistan&amp;#8217;s annual opium poppy monitoring programme. Access to labelled data remains the main barrier for utilising deep learning from satellite imagery to automate the process of land cover classification. In this study, we aim to transfer knowledge from historical labelled data of agricultural land, from work on poppy cultivation estimates undertaken between 2007 and 2010, to classify imagery from a range of sensors using deep learning. Fully Convolutional Networks (FCNs) have been used to learn the complex features of agriculture in southern Afghanistan using their inherent spatial and spectral characteristics from satellite imagery. FCNs are trained and validated using labelled Disaster Monitoring Constellation (DMC) data (32 m) to transfer knowledge of agricultural land to classify other imagery, such as Landsat (30 m). The dependency on spatial and spectral characteristics are explored using intensity, Normalised Difference Vegetation Index (NDVI), top of atmosphere reflectance and tasselled cap transformation. The underlying spatial features associated with agriculture are found to play a significant role in agriculture discrimination. High classification performance has been achieved with over 92% overall accuracy and 0.58 intersection over union. The ability to transfer knowledge from historical datasets to new satellite sensors is an exciting prospect for future automated agricultural land discrimination in the United Nations Office on Drugs and Crime annual opium survey.&lt;/p&gt;

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