Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis

Mayes, Marc; Mustard, John F.; Melillo, Jerry M.

doi:10.1016/j.rse.2015.05.006

Cited by 72 publications

(72 citation statements)

References 35 publications

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“…Relatedly, Hamzah (2001), also observed that GIS has gained a lot of credence in forest resources management. Hence, alongside remote sensing, GIS has turned into an indispensable scientific technology for local, regional as well as global landuse and landcover change studies (Saatchi et al, 2011;Mayes et al, 2015). Within the past decades, GIS has often been used in combination with remote sensing especially in studies on ecological models, agricultural intensification, pests and diseases, forest fires and droughts monitoring as well as wetlands and forest conservation (Saatchi et al, 2013).…”

Section: Remote Sensing Gis and Tropical Forest Managementmentioning

confidence: 99%

Perspectives of Remote Sensing and GIS Applications in Tropical Forest Management

Beckline¹,

Sun²,

Baninla³

et al. 2017

AJAF

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Abstract:Tropical forest management requires could be improved through the use of current technologies including remote sensing and Geographic Information System (GIS). In this paper, we characterize and evaluate forest management patterns and relate this to modern technologies such as geographical information systems and remote sensing. We further examine the application of these modern technologies in tropical forestry and conservation. To achieve this, we carried out a comprehensive survey of published scientific literature obtained through Web of Science, Mendeley, Researchgate and Google Scholar. We observed that, the relationships between forestry management, modern technologies have shifted over time. These have depended on how management activities such as planting and harvesting, interact with other drivers and disturbances (fire, pests and diseases) to influence the adaptive capacity of forests. Forest management and new technologies are interrelated because the technologies support management actions; hence contribute to global forest resources management and conservation.

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Section: Remote Sensing Gis and Tropical Forest Managementmentioning

confidence: 99%

Perspectives of Remote Sensing and GIS Applications in Tropical Forest Management

Beckline¹,

Sun²,

Baninla³

et al. 2017

AJAF

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“…This was adequate as undisturbed forests typically maintain stable spectral reflectances over many years, while non-forest land cover types have both seasonal and inter-annual variability [11]. Similarly a linear spectral mixture analysis was used on 8 Landsat images to improve the net loss in sub-Saharan African forests between 1995 and 2011 [12]. In recent work an iteratively re-weighted multivariate alteration detection (IRMAD) was used to detect land cover change in monthly composited Landsat images between 2008 and 2011 [13].…”

Section: Introductionmentioning

confidence: 99%

Applying Model Parameters as a Driving Force to a Deterministic Nonlinear System to Detect Land Cover Change

Salmon

Holloway

Kleynhans

et al. 2017

IEEE Trans. Geosci. Remote Sensing

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Abstract-In this paper we propose a new method for extracting features from time series satellite data to detect land cover change. We propose to make use of the behavior of a deterministic nonlinear system driven by a time dependent force. The driving force comprises a set of concatenated model parameters regressed from fitting a model to a MODerate-resolution Imaging Spectroradiometer time series. The goal is to create behavior in the nonlinear deterministic system which appears predictable for time series undergoing no change, while erratic for time series undergoing land cover change. The differential equation used for the deterministic nonlinear system is that of a large amplitude pendulum, where the displacement angle is observed over time. If there has been no change in land cover the mean driving force will approximate zero, hence the pendulum will behave as if in free motion under the influence of gravity only. If however there has been a change in land cover this will for a brief initial period introduce a non-zero mean driving force, which does work on the pendulum, changing its energy and future evolution which we demonstrate is observable. This we show is sufficient to introduce an observable change to the state of the pendulum, thus enabling change detection. We extend this method to a higher dimensional differential equation to improve the false alarm rate in our experiments. Numerical results show change detection accuracy of nearly 96% when detecting new human settlements, with a corresponding false alarm rate of 0.2% (omission error rate of 4%). This compares very favourably with other published methods, which achieved less than 90% detection but with false alarm rates all above 9% (omission error rate of 66%).

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“…Furthermore, studies which cover larger areas can be more costly if commercial satellite images are used. However, the free access to Landsat images offers opportunities to researchers who cannot afford commercial satellite images because of the higher prices [16,23,24]. This solves the problem of many resource constrained researchers as these images can be accessed free of charge.…”

Section: Developments Of Landsat Datamentioning

confidence: 99%

“…The output of SMA is represented as a fraction of each land cover type called endmembers [74]. For example, Mayes, Mustard and Melillo [23] applied SMA in establishing the extent of dry tropical forests in Tanzania by establishing the fraction of forest and non-forest endmembers. Most studies have indicated that SMA is important in improving area estimation of land cover types [23,75,76].…”

Section: Spectral Mixture Analysis (Sma)mentioning

confidence: 99%

“…The common forms of SMA are linear spectral mixture analysis (LSMA) and multiple endmember spectral mixture analysis (MESMA). LSMA is designed to work with a fixed number of endmembers while MESMA can be used on pixels with different numbers of endmembers [23,77]. The major challenge for SMA is the errors in the final allocation of fractional endmembers resulting from spectral variability and similarity during the selection of endmembers [66,67].…”

Section: Spectral Mixture Analysis (Sma)mentioning

confidence: 99%

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Developments in Landsat Land Cover Classification Methods: A Review

2017

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Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification.

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Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis

Cited by 72 publications

References 35 publications

Perspectives of Remote Sensing and GIS Applications in Tropical Forest Management

Perspectives of Remote Sensing and GIS Applications in Tropical Forest Management

Applying Model Parameters as a Driving Force to a Deterministic Nonlinear System to Detect Land Cover Change

Developments in Landsat Land Cover Classification Methods: A Review

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