A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services

Masenyama, Anita; Mutanga, Onisimo; Dube, Timothy; Bangira, Tsitsi; Sibanda, Mbulisi; Mabhaudhi, Tafadzwanashe

doi:10.1080/15481603.2022.2088652

Cited by 28 publications

(10 citation statements)

References 150 publications

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“…Masenyama et al [16] have stated that the average R 2 value for remote sensinggrassland productivity studies ranges between 0.65 (65%) and 0.75 (75%). In comparison, the performance of both the ANN and CNN in this study is commendable, with a model accuracy of 75% for the ANN and 83% for the CNN.…”

Section: Discussionmentioning

confidence: 99%

“…There is a substantial lack of grassland biomass studies, in relation to remote sensing, in South Africa, as reported by Masenyama et al [16]. Furthermore, in a South African context, no research has attempted to investigate the performance of conventional ANNs and contemporary CNNs in estimating aboveground grass biomass.…”

Section: Introductionmentioning

confidence: 99%

“…The study of grasslands is imperative in the face of climate change. Hence, the use of machine learning techniques to observe and assess grassland health would be valuable to both researchers and practitioners [1,16]. Therefore, the objectives of this study were to: (1) compare the predictive performance of shallow ANNs and deep CNNs in estimating aboveground grass biomass using Sentinel-2 MSI and (2) utilise both neural networks on Sentinel-2 MSI to predict dry season aboveground biomass for the Vulindlela communal area in South Africa.…”

Section: Introductionmentioning

confidence: 99%

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Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass

Vawda,

Lottering,

Mutanga

et al. 2024

Sustainability

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Grasslands are biomes of significant fiscal, social and environmental value. Grassland or rangeland management often monitors and manages grassland productivity. Productivity is determined by various biophysical parameters, one such being grass aboveground biomass. Advancements in remote sensing have enabled near-real-time monitoring of grassland productivity. Furthermore, the increase in sophisticated machine learning algorithms has provided a powerful tool for remote sensing analytics. This study compared the performance of two neural networks, namely, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN), in predicting dry season aboveground biomass using open-access Sentinel-2 MSI data. Sentinel-2 spectral bands and derived vegetation indices were used as input data for the two algorithms. Overall, findings in this study showed that the deep CNN outperformed the ANN in estimating aboveground biomass with an R2 of 0.83, an RMSE of 3.36 g/m2 and an RMSE% of 6.09. In comparison, the ANN produced an R2 of 0.75, an RMSE of 5.78 g/m2 and an RMSE% of 8.90. The sensitivity analysis suggested that the blue band, Green Chlorophyll Index (GCl), and Green Normalised Difference Vegetation Index (GNDVI) were the most significant for model development for both neural networks. This study can be considered a pilot study as it is one of the first to compare different neural network performances using freely available satellite data. This is useful for more rapid biomass estimation, and this study exhibits the great potential of deep learning for remote sensing applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass

Vawda,

Lottering,

Mutanga

et al. 2024

Sustainability

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“…The VOSviewer (version 1.6.20) software [12] was used to perform a bibliographic analysis of the retrieved articles and visualize key terms' occurrence and co-occurrence networks. Bibliographic analysis is a standard meta-analytical tool used to identify the interconnections of key terms from published articles in the form of linked clusters [13]. According to Van Eck et al [12], a bibliometric map is created following four steps, which include (i) selecting the counting (binary or full) method, (ii) selecting the minimum number of term occurrences, (iii) calculating the relevance score for co-occurrence terms and (iv) using the selected terms to display the map.…”

Section: Data Extraction and Analysismentioning

confidence: 99%

Assessing the Seasonal Water Requirement of Fully Mature Japanese Plum Orchards: A Systematic Review

Mashabatu,

Motsei,

Jovanović

et al. 2024

Applied Sciences

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Japanese plums have relatively high water requirements, which depend on supplementing rainfall volumes with accurately quantified irrigation water. There is a lack of knowledge on the seasonal water requirements of plum orchards. This gap in the literature poses an imminent threat to the long-term sustainability of the South African plum industry, which is particularly plagued by climate change and diminishing water resources. The systematic literature review conducted in this study aimed to provide a foundation for supporting water management in irrigated Japanese plum [Prunus salicina Lindl.] orchards. Seventeen peer-reviewed articles obtained from the literature were analyzed. Approximately 66% of the cultivars were cultivated under different regulated deficit irrigation regimes for water-saving purposes and to increase fruit quality. This review of our knowledge provided benchmark figures on the annual water requirements of Japanese plums. The full-year plum crop water requirements obtained from the literature ranged between 921 and 1211 mm a−1. Canopy growth, pruning and growing season length were the most common causes of differences in the water requirement estimates. Further research is required to measure the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, especially in the South African context.

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“…The derived RF models were assessed for accuracy based on the coefficient of determination (R 2 ), root mean square error (RMSE), and root mean square error percentage (RMSE%). The R 2 is an accuracy assessment parameter that is used to measure the magnitude of variation between observed and predicted samples [107]. Its values range from 0 to 1, and values closer to 1 indicate better goodness of fit of a model [108].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

et al. 2023

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Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.

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A systematic review on the use of remote sensing technologies in quantifying grasslands ecosystem services

Cited by 28 publications

References 150 publications

Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass

Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass

Assessing the Seasonal Water Requirement of Fully Mature Japanese Plum Orchards: A Systematic Review

Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform

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