Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health

Cho, Moses Azong; Debba, Pravesh; Mutanga, Onisimo; Dudeni-Tlhone, Nontembeko; Magadla, Thandulwazi; Khuluse, Sibusisiwe A.

doi:10.1016/j.jag.2011.12.005

Cited by 40 publications

(40 citation statements)

References 37 publications

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“…The study area stretches between latitute 28˝52 1 25 11 S and longitude 32˝17 1 23 11 E (Figure 1). The Dukuduku forest area covers more than 6000 hectares (ha) of indigenous coastal forest on the rolling savannahs of the inland across the dune line along the KwaZulu-Natal coast, from southern KwaZulu-Natal province to beyond the Mozambican boundary [2,62]. It is considered to be one of the largest remaining stretches of coastal lowland forest in South Africa.…”

Section: Study Areamentioning

confidence: 99%

“…It is considered to be one of the largest remaining stretches of coastal lowland forest in South Africa. However, due to a high number of individual illegal residence and intensive agricultural activities, the natural vegetation surrounding the forest has widely been removed [1,2,62]. Similarly, increasing human activities and settlements in the area have led to an increase in ecosystem fragmentation [1,2,62].…”

Section: Study Areamentioning

confidence: 99%

“…In KwaZulu-Natal Province, coastal lowland indigenous forests occur in small, fragmented and largely scattered patches in relatively dry landscapes [1,2]. One such fragmented forest in KwaZulu-Natal is the Dukuduku indigenous forest, which is one of the largest and the best preserved remnants of South African coastal forests [3].…”

Section: Introductionmentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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Leaf area index (LAI) is an important biophysical trait for forest ecosystem and ecological modeling, as it plays a key role for the forest productivity and structural characteristics. The groundbased methods like the handheld optical instruments for predicting LAI are subjective, pricy and time-consuming. The advent of very high spatial resolutions multispectral data and robust machine learning regression algorithms like support vector machines (SVM) and artificial neural networks (ANN) has provided an opportunity to estimate LAI at tree species level. The objective of the this study was therefore to test the utility of spectral vegetation indices (SVI) calculated from the multispectral WorldView-2 (WV-2) data in predicting LAI at tree species level using the SVM and ANN machine learning regression algorithms. We further tested whether there are significant differences between LAI of intact and fragmented (open) indigenous forest ecosystems at tree species level. The study shows that LAI at tree species level could accurately be estimated using the fragmented stratum data compared with the intact stratum data. Specifically, our study shows that the accurate LAI predictions were achieved for Hymenocardia ulmoides using the fragmented stratum data and SVM regression model based on a validation dataset (R 2 Val = 0.75, RMSE Val = 0.05 (1.37% of the mean)). Our study further showed that SVM regression approach achieved more accurate models for predicting the LAI of the six endangered tree species compared with ANN regression method. It is concluded that the successful application of the WV-2 data, SVM and ANN methods in predicting LAI of six endangered tree species in the Dukuduku indigenous forest could help in making informed decisions and policies regarding management, protection and conservation of these endangered tree species.

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Section: Study Areamentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

et al. 2016

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“…FRAGSTATS software measures (McGarigal et al 2012) are widely adopted for fragmentation, estimated using Landsat (Tang et al 2012;Wang et al 2011;Liu et al 2014;Virtanen and Ek 2014;García-Gigorro and Saura 2005), IKONOS-2, GeoEye-1 (Newman et al 2014), QuickBird, ASTER (Virtanen and Ek 2014), or Wide Field Sensor (WiFS) (García-Gigorro and Saura 2005) imagery. Variogram analysis is also employed to assess forest heterogeneity (Cho et al 2012). In addition, a number of indices are used to assess connectivity, e.g.…”

Section: Fragmentation / Connectivitymentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…The importance and efficiency of high-resolution multispectral and hyperspectral imagery for the goals mentioned above have been highlighted by Entcheva-Campbell et al (2004), Somers et al (2010) and Cho et al (2012). By recording a high number of narrow bands and increasing the spectral resolution, hyperspectral images make it possible to discern very narrow spectral features (Aspinall et al 2002, Shaw & Manolakis 2002.…”

Section: Introductionmentioning

confidence: 99%

Detecting mistletoe infestation on Silver fir using hyperspectral images

Ančić¹,

Pernar²,

Bajić³

et al. 2014

iForest

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© iForest -Biogeosciences and Forestry IntroductionThe narrow ecological niche and the extensive exposure to pollution makes silver fir (Abies alba Mill.) a highly endangered species. It is currently the most damaged tree species in Croatian forests (Prpić & Seletković 2001, Potočić et al. 2008. In addition to insects and fungi, fir is increasingly attacked by mistletoe (Viscum album L. ssp. abietis [Weisb.] Abrom), which physiologically weakens the tree by its remarkable consumption of water (Fisher 1983, Lamont 1983, Ehleringer et al. 1986) and mineral nutrients (Watson 2001), thus predisposing the host to pest and disease attacks. Mistletoe is also considered an indicator of existing stress (Atzberger & Werner 1998, Smith et al. 2004, Moorthy et al. 2008, Wang & Li 2012. It has been reported that an excessive number of shrubs may even cause tree death (Plagnat & Brossier 1969, Cervera & Villaescusa 1977, Zuber 1983, Chéret et al. 1987, Tsopelas et al. 2004, Olivia & Colinas 2007. Morevover, mistletoe-infected trees have lower increment than uninfected trees (Klepac 1955, Uščuplić 1992, Uščuplić & Dautbašić 1998, Noetzli et al. 2003, Barbu 2009) and should therefore have a cutting priority as long as their timber still retains its commercial value. A high correlation between fir infestation and mistletoe distribution has been reported (Hofstetter 1988, Idžojtić et al. 2008, Barbu 2009) with an almost exponential trend in the mistletoe population increase (Noetzli et al. 2003). An increase in the intensity of mistletoe infestation was also noted, favoring its spread to healthy trees (Idžojtić et al. 2003). Tree health status can be determined in two ways: by ground and by remote sensing observations, of which color infrared (CIR) images are particularly important (Pernar 1997, Pernar et al. 2007a. In Croatia, forest damage over large areas has been assessed by means of CIR aerial imagery since 1988, though mistletoe could not be detected based on these images. Preliminary pilot tests aimed at assessing fir damage using multispectral digital images have been carried out in (Pernar et al. 2007b), confirming that mistletoe could be successfully detected using a high-resolution multispectral digital camera. In contrast, standard field methods for mistletoe detection are generally based on visual survey (using binoculars) of edge trees along roads or in clearings, from which only the upper crown is visible (Idžo-jtić et al. 2005), and therefore do not often reflect the real conditions in the field. For example, Diminić et al. (2011) suggests that ground analyses of mistletoe presence in the crowns of standing trees are not fully reliable.The limitations of standard field methods call for new approaches to assess tree health and quantify mistletoe infestation. The importance and efficiency of high-resolution multispectral and hyperspectral imagery for the goals mentioned above have been highlighted by Entcheva-Campbell et al. (2004), Somers et al. (2010) and Cho et al. (2012). By recording a high number of narrow ban...

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Potential utility of the spectral red-edge region of SumbandilaSat imagery for assessing indigenous forest structure and health

Cited by 40 publications

References 37 publications

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Detecting mistletoe infestation on Silver fir using hyperspectral images

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