scholarly journals Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa

Drones ◽  
2018 ◽  
Vol 2 (3) ◽  
pp. 28 ◽  
Author(s):  
Ibrahim Wahab ◽  
Ola Hall ◽  
Magnus Jirström
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Field Methods ◽  
Smallholder Farms ◽  
Sub Saharan ◽  
Remote Sensing Methods ◽  
Spad Readings

The application of remote sensing methods to assess crop vigor and yields has had limited applications in Sub-Saharan Africa (SSA) due largely to limitations associated with satellite images. The increasing use of unmanned aerial vehicles in recent times opens up new possibilities for remotely sensing crop status and yields even on complex smallholder farms. This study demonstrates the applicability of a vegetation index derived from UAV imagery to assess maize (Zea mays L.) crop vigor and yields at various stages of crop growth. The study employs a quadcopter flown at 100 m over farm plots and equipped with two consumer-grade cameras, one of which is modified to capture images in the near infrared. We find that UAV-derived GNDVI is a better indicator of crop vigor and a better estimator of yields—r = 0.372 and r = 0.393 for mean and maximum GNDVI respectively at about five weeks after planting compared to in-field methods like SPAD readings at the same stage (r = 0.259). Our study therefore demonstrates that GNDVI derived from UAV imagery is a reliable and timeous predictor of crop vigor and yields and that this is applicable even in complex smallholder farms in SSA.

scholarly journals In-season plot area loss and implications for yield estimation in smallholder rainfed farming systems at the village level in Sub-Saharan Africa

GeoJournal ◽  
2019 ◽  
Vol 85 (6) ◽  
pp. 1553-1572 ◽  
Author(s):  
Ibrahim Wahab
Keyword(s):  
Remote Sensing ◽  
Farming Systems ◽  
Sub Saharan Africa ◽  
Yield Estimation ◽  
Yield Measurement ◽  
Area Loss ◽  
Rainfed Farming ◽  
Plot Area ◽  
Two Measures ◽  
Sub Saharan

Abstract The shortfalls in the quality, quantity, and reliability of agriculture performance data are neither new nor confined to Sub-Saharan Africa (SSA). It is, however, a more dire challenge given the overwhelming importance of agriculture in the economies of most countries in the region in terms of food security and poverty reduction. While farmers’ self-reported (SR) data on crop outputs and farm sizes remain popular variables for computing plot productivity and yields, especially in SSA, other methods such GPS measurement and remote sensing measurement of crop area, crop cuts (CC) as well as whole plot harvests have been touted as the gold standard methods for yield measurement. All these approaches to yield estimation are insufficient in capturing real agriculture productivity in rainfed farming systems due to the significant area loss that characterizes these farming systems in the course of each cropping season. This paper compares yield data of smallholder maize plots from two farming communities in the Eastern Region of Ghana based on farmer self-reported outputs and crop cuts, as well as GPS and aerial imagery measurement of plot area. The study finds a high level of agreement between GPS-measured plot area and that measured using remote sensing methods (R2 = 0.80) with the minor deviations between the two measures attributable to changes in farmers’ plans in the course of the season with regards to their cultivation extent. More interestingly, the study finds a substantial disparity between measured CC yields and SR yields; 2174 kg/ha for CC yields compared to 651 kg/ha for SR yields. The significant disparity between the two measures of yield is partly attributable to the significant intra-plot variability in crop performance leading to plot area loss in the course of the season. This area loss (ranging from 15 to 30% of the planted area) is usually not taken into account in current yield measurement approaches. Delineating the productive and planted-but-unproductive sections of plots has important implications not only for yield estimation methodologies but also for shedding more light on the factors underlying current poor yields and pathways to improving productivity on smallholder rainfed maize farms.

scholarly journals Circular Bioeconomy Research for Development in Sub-Saharan Africa: Innovations, Gaps, and Actions

2021 ◽  
Vol 13 (4) ◽  
pp. 1926 ◽  
Author(s):  
Shiferaw Feleke ◽  
Steven Michael Cole ◽  
Haruna Sekabira ◽  
Rousseau Djouaka ◽  
Victor Manyong
Keyword(s):  
Gap Analysis ◽  
Farming Systems ◽  
Crop Productivity ◽  
Sub Saharan Africa ◽  
International Institute ◽  
The Public ◽  
Secondary Sources ◽  
Use Efficiency ◽  
Sub Saharan

The International Institute of Tropical Agriculture (IITA) has applied the concept of ‘circular bioeconomy’ to design solutions to address the degradation of natural resources, nutrient-depleted farming systems, hunger, and poverty in sub-Saharan Africa (SSA). Over the past decade, IITA has implemented ten circular bioeconomy focused research for development (R4D) interventions in several countries in the region. This article aims to assess the contributions of IITA’s circular bioeconomy focused innovations towards economic, social, and environmental outcomes using the outcome tracking approach, and identify areas for strengthening existing circular bioeconomy R4D interventions using the gap analysis method. Data used for the study came from secondary sources available in the public domain. Results indicate that IITA’s circular bioeconomy interventions led to ten technological innovations (bio-products) that translated into five economic, social, and environmental outcomes, including crop productivity, food security, resource use efficiency, job creation, and reduction in greenhouse gas emissions. Our gap analysis identified eight gaps leading to a portfolio of five actions needed to enhance the role of circular bioeconomy in SSA. The results showcase the utility of integrating a circular bioeconomy approach in R4D work, especially how using such an approach can lead to significant economic, social, and environmental outcomes. The evidence presented can help inform the development of a framework to guide circular bioeconomy R4D at IITA and other research institutes working in SSA. Generating a body of evidence on what works, including the institutional factors that create enabling environments for circular bioeconomy approaches to thrive, is necessary for governments and donors to support circular bioeconomy research that will help solve some of the most pressing challenges in SSA as populations grow and generate more waste, thus exacerbating a changing climate using the linear economy model.

Incorporating vegetation trends to the MEDALUS-ESA approach for assessing environmental sensitivity at the national scale: the case of Kenya

2021 ◽  
Author(s):  
Elias Symeonakis ◽  
Eva Arnau-Rosalén ◽  
Antony Wandera ◽  
Thomas Higginbottom ◽  
Bradley Cain
Keyword(s):  
National Parks ◽  
Land Degradation ◽  
Vegetation Index ◽  
Sub Saharan Africa ◽  
Mitigation Measures ◽  
Environmental Sensitivity ◽  
National Scale ◽  
Livestock Density ◽  
Normalised Difference Vegetation Index ◽  
Sub Saharan

<p>Land degradation is one of the main causes of loss of productivity and ecosystem services worldwide. According to the United Nations Convention to Combat Desertification (UNCCD), sub-Saharan Africa is on a path to experiencing some of the strongest increases in pressures on land and land-based resources than any other continent. Assessing the sensitivity of sub-Saharan African countries to land degradation is, therefore, important for identifying areas of concern, setting a baseline for national land degradation neutrality targets, and for the prioritisation of mitigation measures. The widely used MEDALUS-ESA framework is employed here to assess the sensitivity of Kenya to land degradation using the year 2010 as a baseline. We modify the MEDALUS-ESA approach by adding two important variables that are closely linked with observed land degradation in Kenya: soil erosion and livestock density. Altogether, 16 indicators are estimated from existing global-to-national-scale land cover, vegetation (MCD12Q1, MOD44B), soil (ISRIC African SoilGrids), elevation (SRTM), population and livestock density data, divided into 4 main environmental quality indices (vegetation, soil, climate and management). In order to address the dynamic nature of the land degradation process, we incorporate two additional vegetation indicators: the statistically significant (p≤ 0.05) trend over the last three decades in the Normalised Difference Vegetation Index (NDVI) and the Rain Use Efficiency (RUE; estimated using the GIMMS3g dense NDVI dense time-series and precipitation from CHIRPS). Our results show that ~40% of the country is in critical and ~48% in fragile condition, with respect to environmental sensitivity. Our approach is successful in identifying areas of known long-term degradation, for example the rangelands South and East of Nairobi (e.g. Machacos and Kitengela) and the parts of the northern rangelands (e.g. Yamicha and eastern parts of Isiolo District). It is also successful in mapping the areas of least concern, including some of the major protected areas(e.g. Tsavo National Parks, Meru National Park and the Masai Mara National Reserve) and forested areas (Mt Kenya and the Aberdares). Our modification of the MEDALUS-ESA is an important tool that can be employed at the national scale using free and open-access data to assess environmental sensitivity and assist in the UNCCD efforts to successfully define land degradation neutrality targets.</p>

scholarly journals A Remote Sensing-Based Approach to Management Zone Delineation in Small Scale Farming Systems

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1767
Author(s):  
Davide Cammarano ◽  
Hainie Zha ◽  
Lucy Wilson ◽  
Yue Li ◽  
William D. Batchelor ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Farming Systems ◽  
Small Scale ◽  
Sustainable Food ◽  
Environmentally Sustainable ◽  
Management Zone ◽  
The North ◽  
Landsat Satellite ◽  
The Village

Small-scale farms represent about 80% of the farming area of China, in a context where they need to produce economic and environmentally sustainable food. The objective of this work was to define management zone (MZs) for a village by comparing the use of crop yield proxies derived from historical satellite images with soil information derived from remote sensing, and the integration of these two data sources. The village chosen for the study was Wangzhuang village in Quzhou County in the North China Plain (NCP) (30°51′55″ N; 115°02′06″ E). The village was comprised of 540 fields covering approximately 177 ha. The subdivision of the village into three or four zones was considered to be the most practical for the NCP villages because it is easier to manage many fields within a few zones rather than individually in situations where low mechanization is the norm. Management zones defined using Landsat satellite data for estimation of the Green Normalized Vegetation Index (GNDVI) was a reasonable predictor (up to 45%) of measured variation in soil nitrogen (N) and organic carbon (OC). The approach used in this study works reasonably well with minimum data but, in order to improve crop management (e.g., sowing dates, fertilization), a simple decision support system (DSS) should be developed in order to integrate MZs and agronomic prescriptions.

scholarly journals Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

2019 ◽  
Vol 11 (20) ◽  
pp. 2456 ◽  
Author(s):  
Wanxue Zhu ◽  
Zhigang Sun ◽  
Yaohuan Huang ◽  
Jianbin Lai ◽  
Jing Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Near Infrared ◽  
Lookup Table ◽  
Field Scale ◽  
Area Index ◽  
Biophysical Parameter ◽  
Retrieval Accuracy

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

scholarly journals Remote Sensing Landslide Recognition Based on Convolutional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yu Wang ◽  
Xiaofei Wang ◽  
Junfan Jian
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spatial Information ◽  
Spectral Characteristics ◽  
Remote Sensing Images ◽  
Landslide Event

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

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