Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries

Jhonnerie, Romie; Siregar, Vincentius P.; Nababan, Bisman; Prasetyo, Lilik Budi; Wouthuyzen, Sam

doi:10.1016/j.proenv.2015.03.028

Cited by 111 publications

(73 citation statements)

References 15 publications

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“…They are based on expert knowledge of the known characteristics of land cover classes, such as urban, vegetation, and water. Thus, we adopt three popular spectral indices: the normalized difference built-up index (NDBI) [29], the normalized difference vegetation index (NDVI) [30], and the modified normalized difference water index (MNDWI) [31], which are a recommended feature combination for land cover classification in [32]. These three spectral indices are closely related to the land cover classes defined in our research; and their formulas are summarized in Table 2.…”

Section: Preprocessingmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone.

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Section: Preprocessingmentioning

confidence: 99%

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Qiu

et al. 2020

Remote Sensing

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“…The ML approach is one of the classical parametric statistical classifiers and is widely used for LULC classification [53]. Several studies have also evaluated alternative, recently-developed machine-learning algorithms for land-cover classification, such as support vector machine (SVM) methods [54], decision trees (DT) [55], and random-forest models (RF) [56,57]. Schneider [55] observed that the DT and SVM classifiers outperformed the ML classifier in the context of highly dynamic land-cover change and 'fuzzy' multi-signal classes around the Chinese cities of Chengdu, Xi'An, and Kunming.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Urban Expansion Occurred at the Expense of Agricultural Lands in the Tarai Region of Nepal from 1989 to 2016

et al. 2018

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Recent rapid urbanization in developing countries presents challenges for sustainable environmental planning and peri-urban cropland management. An improved understanding of the timing and pattern of urbanization is needed to determine how to better plan urbanization for the near future. Here, we describe the spatio-temporal patterns of urbanization and related land-use/land-cover (LULC) changes in the Tarai region of Nepal, as well as discuss the factors underlying its rapid urban expansion. Analyses are based on regional time-series Landsat 5, 7 and 8 image classifications for six years between 1989 and 2016, representing the first long-term observations of their kind for Nepal. During this 27-year period, gains in urban cover and losses of cultivated lands occurred widely. Urban cover occupied 221.1 km 2 in 1989 and increased 320% by 2016 to a total 930.22 km 2 . Cultivated land was the primary source of new urban cover. Of the new urban cover added since 1989, 93% was formerly cultivated. Urban expansion occurred at moderately exponential rates over consecutive observation periods, with nearly half of all urban expansion occurring during 2006-2011 (305 km 2 ). The annual rate of urban growth during 1989-1996 averaged 3.3% but reached as high as 8. 09% and 12.61% during 1996-2001 and 2011-2016, respectively. At the district level, the rate of urban growth and, by extension, agricultural loss, were weakly related to total population growth. Variability in this relationship suggests that concerted urban-growth management may reduce losses of agricultural lands relative to historic trends despite further population growth and urbanization. Urbanization and LULC change in the Tarai region are attributable to significant inter-regional migration in a context of poor urban planning and lax policies controlling the conversion and fragmentation of peri-urban cultivated lands. Urban expansion and farmland loss are expected to continue in the future.

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“…Light detection and ranging (LiDAR) and optical remote sensing combined with the support vector machine (SVM) algorithm has been used to map and create a mangrove inventory (David, 2015;Pada, 2016). Other remotely sensed data like Landsat and ALOS-PALSAR have also been used to map mangroves with the random forest (RF) classification (Jhonnerie, 2015).…”

Section: Introductionmentioning

confidence: 99%

Mangrove classification using support vector machines and random forest algorithm: a comparative study

Campomanes

Pada

Silapan

2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:Mangrove forest ecosystems fulfil a number of important functions like supporting the conservation of biological diversity by providing habitats, nurseries, and nutrients for animal species. In the Philippines, mangrove forests are declining due to the growth of aquaculture production. Mangrove forests are slowly being replaced by fishponds. An accurate inventory of what are left of these natural resources is important to know how we can conserve and manage them. This study aims to compare the performance of support vector machines (SVM) with random forest (RF) algorithm in automatically classifying mangrove forests using LiDAR data and orthophotographs in an object based approach. The site is a 36 sq. km. coastal area in Manapla, Negros Occidental, Philippines. Various derivatives were created from the LiDAR data like the pit-free canopy height model (CHM) and intensity. The CHM was used in contrast split segmentation to distinguish between ground and non-ground objects. Only the non-ground objects were segmented further knowing that majority of mangroves are tall. Inventory of short mangroves is not yet included in this study. The non-ground objects were further segmented using multiresolution segmentation with the CHM and RGB bands of the orthophoto using a scale of 15. The non-ground class was further separated into four classes namely: mangrove, built-up, other trees, and sugarcane. 120 training points and 30 validation points per class were collected by visual inspection using the orthophoto as reference. Several features of the training objects were computed from both the LiDAR and orthophoto derivatives and used for classification. SVM with radial basis function was used to classify the rest of the image and resulted in an overall accuracy of 95.83%. For mangroves, its precision and recall reached 83.33% and 100%, respectively. In the SVM classification, mangroves were confused with other trees and sugarcane. Another machine learning algorithm, random forest (RF) was used to classify the same area to compare their performance and accuracy. Using the same features, the RF classification achieved an overall accuracy of 99.1667%. For mangroves, the RF classification obtained 100% and 96.70% for its precision and recall, respectively. The RF classifier confused other trees with mangroves which caused the error. The accuracies from both machine learning algorithms show that the RF classifier performed better than the SVM classifier and further implies the potential of using RF in classifying mangroves in other areas.

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Random Forest Classification for Mangrove Land Cover Mapping Using Landsat 5 TM and Alos Palsar Imageries

Cited by 111 publications

References 15 publications

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine

Urban Expansion Occurred at the Expense of Agricultural Lands in the Tarai Region of Nepal from 1989 to 2016

Mangrove classification using support vector machines and random forest algorithm: a comparative study

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