Mapping Maize, Tobacco, and Soybean Fields in Large-Scale Commercial Farms of Zimbabwe Based on Multitemporal NDVI Images in MAXENT

Maguranyanga, Caleb; Murwira, Amon

doi:10.1080/07038992.2014.999914

Cited by 9 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML has gained widespread use in soil mapping [35,36], species distribution modeling [37,38], land use mapping, and land cover classification [39]. The most common ML approach to land suitability assessment relies on models trained with the Maxent algorithm using all the available land use data with no socioeconomic covariates [40][41][42][43][44][45][46]. This approach builds on the assumption that farmers cultivate crops in the areas where they have the best growing conditions [47].…”

Section: Introductionmentioning

confidence: 99%

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

et al. 2021

View full text Add to dashboard Cite

It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

show abstract

Section: Introductionmentioning

confidence: 99%

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

et al. 2021

View full text Add to dashboard Cite

show abstract

“…There exist previous research done regarding this effect, but they all used high temporal resolution MODIS time series. These include Sibanda and Murwira [15], Maguranyanga and Murwira [16], last but not least Hentze et al [14]. The main limitation of MODIS is its low spatial resolution of 500 m, since most farm sizes are very small and heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification

Useya

Chen

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Crops mapping unequivocally becomes a daunting task in humid, tropical, or subtropical regions due to unattainability of adequate cloud-free optical imagery. Objective of this study is to evaluate the comparative performance between decision-and pixel-levels data fusion ensemble classified maps using Landsat 8, Landsat 7, and Sentinel-2 data. This research implements parallel and concatenation approach to ensemble classify the images. The multiclassifier system comprises of Maximum Likelihood, Support Vector Machines, and Spectral Information Divergence as base classifiers. Decision-level fusion is achieved by implementing plurality voting method. Pixel-level fusion is achieved by implementing fusion by mosaicking approach, thus appending cloud-free pixels from either Sentinel-2 or Landsat 7. The comparison is based on the assessment of classification accuracy. Overall accuracy results show that decision-level fusion achieved an accuracy of 85.4%, whereas pixel-level fusion classification attained 82.5%, but their respective kappa coefficients of 0.84 and 0.80 but are not significantly different according to Z-test at α = 0.05. F1-score values reveal that decision-level performed better on most individual classes than pixel-level. Regression coefficient between planted areas from both approaches is 0.99. However, Support Vector Machines performed the best of the three classifiers. The conclusion is that both decision-level and pixel-level fusion approaches produced comparable classification results. Therefore, either of the procedures can be adopted in areas with inescapable cloud problems for updating crop inventories and acreage estimation at regional scales. Future work can focus on performing more comparison tests on different areas, run tests using different multiclassifier systems, and use different imagery.

show abstract

“…As determined by Hentze et al [30], there is an alleged deficiency of precise and spatially explicit approaches to derive independent and objective information pertaining to the extent of agricultural areas in Zimbabwe. The previous research on crop type mapping [31,32,33] have utilized MODIS NDVI imagery that has a coarse spatial resolution of 250 m, henceforth, limits its ability to depict small and heterogeneous farms. Zimbabwe cropland comprises small, fragmented farm fields that cannot be reliably depicted from coarse spatial resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Chen

Useya

Mugiyo

2020

Heliyon

View full text Add to dashboard Cite

Radar imagery have few polarization bands which can limit the ability to do traditional digital classification. Harmonization of Sentinel-1 and Landsat 8 data despite having complementary texture information can be a challenge. The objectives of this paper are to explore texture features derived from Landsat 8 OLI and dualpolarized Sentinel-1 SAR speckle filtered and unfiltered backscatter, to aggregate classification results using Decision-Level Fusion (DLF), and to evaluate the performance of decision-level fused maps. Gray Level Cooccurrence Matrix (GLCM) is employed to derive sets of seven texture features for Landsat 8 bands and VV þ VH backscatter using 5 Â 5, 7 Â 7, 9 Â 9, and 11 Â 11 window sizes. Each texture feature is stacked with a respective source image and classified using Support Vector Machine (SVM). Classified maps from the best three performers from both speckle filtered and unfiltered are aggregated with classified maps from Landsat 8 using plurality voting algorithm and compared using Z-test. Results indicate an overall classification accuracy of 96.02% from DLF images of Landsat and non-speckle filtered maps, whereas Landsat and speckle filtered achieved 94.69%. The best texture information are derived from the blue band followed by the red band, whereas speckle unfiltered textures performed better than speckle filtered textures. We conclude that integration of Landsat 8 and Sentinel-1, either speckle filtered or unfiltered, improves crop classification and speckles do not have statistically significant effects (p ¼ 0.1208).

show abstract

Mapping Maize, Tobacco, and Soybean Fields in Large-Scale Commercial Farms of Zimbabwe Based on Multitemporal NDVI Images in MAXENT

Cited by 9 publications

References 36 publications

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification

Decision-level fusion of Sentinel-1 SAR and Landsat 8 OLI texture features for crop discrimination and classification: case of Masvingo, Zimbabwe

Contact Info

Product

Resources

About