Abstract
Africa has the largest grassland area among all grassland ecosystems in the world. As a typical agricultural and animal husbandry country in Africa, animal husbandry plays an important role in this region. The investigation of grassland resources and timely grasping the quantity and spatial distribution of grassland resources are of great significance to the stable development of local animal husbandry economy. Therefore, this paper uses Kenya as the study area to investigate the effective and fast approach for grassland mapping with 100-m resolution using the open resources in the Google Earth Engine cloud platform. The main conclusions are as follows. (1) In the feature combination optimization part of this paper, the machine learning algorithm is used to compare the scores and standard deviations of several common algorithms combined with RFE. It is concluded that the combination of RFE and random forest algorithm has the highest stability in modeling and the best feature optimization effect. (2) After feature optimization by the RFE-RF algorithm, the number of features is reduced from 12 to 8, which compressed the original feature space and reduced the redundancy of features. The optimal combination features are applied to random forest classification, and the overall accuracy and Kappa coefficient of classification are 0.87 and 0.85, respectively. The eight features are: elevation, NDVI, EVI, SWIR, RVI, BLUE, RED, and LSWI. (3) There are great differences in topographic features among the local land types in the study area, and the addition of topographic features is more conducive to the recognition and classification of various land types. There exists “salt-and-pepper phenomenon” in pixel-oriented classification. Later research focus will combine the RFE-RF algorithm and the segmentation algorithm to achieve object-oriented land cover classification.
Highlights
Traditional land resource survey methods are slow to update, have high labor costs, and are not conducive to long-term and continuous detection
Chen et al [1] used moderate resolution imaging spectroradiometer (MODIS)-enhanced vegetation index (EVI) time series data to monitor winter wheat information in Hebei, showing these data to be feasible with great potential for extracting spatial distribution information of crops in large areas
It can be seen that the scores and standard deviations of different algorithms are different when they are wrapped by Recursive feature elimination (RFE)
Summary
Traditional land resource survey methods are slow to update, have high labor costs, and are not conducive to long-term and continuous detection. Satellite remote sensing has the advantages of wide coverage, high monitoring frequency, and low labor cost, and is the most advanced means to carry out land resources surveys in Africa. Chen et al [1] used MODIS-EVI time series data to monitor winter wheat information in Hebei, showing these data to be feasible with great potential for extracting spatial distribution information of crops in large areas. Compared with MODIS data, Sentinel-2 data have the advantages of higher spatial resolution and several spectral bands. Peng Fang et al [2] used Sentinel-2 10-m resolution data to extract winter wheat planting areas in the Henan province based on a machine learning algorithm. Fang P et al [3] used a correlation study of biomass based
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