Elhadi Adam

Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation.

High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

a b s t r a c t The saturation problem associated with the use of NDVI for biomass estimation in high canopy density vegetation is a well known phenomenon. Recent field spectroscopy experiments have shown that narrow band vegetation indices computed from the red edge and the NIR shoulder can improve the estimation of biomass in such situations. However, the wide scale unavailability of high spectral resolution satel- lite sensors with red edge bands has not seen the up-scaling of these techniques to spaceborne remote sensing of high density biomass. This paper explored the possibility of estimate biomass in a densely veg- etated wetland area using normalized difference vegetation index (NDVI) computed from WorldView-2 imagery, which contains a red edge band centred at 725 nm. NDVI was calculated from all possible two band combinations of WorldView-2. Subsequently, we utilized the random forest regression algorithm as variable selection and a regression method for predicting wetland biomass. The performance of random forest regression in predicting biomass was then compared against the widely used stepwise multiple linear regression. Predicting biomass on an independent test data set using the random forest algorithm and 3 NDVIs computed from the red edge and NIR bands yielded a root mean square error of prediction (RMSEP) of 0.441 kg/m 2 (12.9% of observed mean biomass) as compared to the stepwise multiple lin- ear regression that produced an RMSEP of 0.5465 kg/m2 (15.9% of observed mean biomass). The results demonstrate the utility of WorldView-2 imagery and random forest regression in estimating and ulti- mately mapping vegetation biomass at high density - a previously challenging task with broad band satellite sensors.

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

Mapping of patterns and spatial distribution of land-use/cover (LULC) has long been based on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps have seen a proliferation of image classification techniques. Despite these efforts, derived LULC maps are still often judged to be of insufficient quality for operational applications, due to disagreement between generated maps and reference data. In this study we sought to pursue two objectives: first, to test the new-generation multispectral RapidEye imagery classification output using machine-learning random forest (RF) and support vector machines (SVM) classifiers in a heterogeneous coastal landscape; and second, to determine the importance of different RapidEye bands on classification output. Accuracy of the derived thematic maps was assessed by computing confusion matrices of the classifiers’ cover maps with respective independent validation data sets. An overall classification accuracy of 93.07% with a kappa value of 0.92, and 91.80 with a kappa value of 0.92 was achieved using RF and SVM, respectively. In this study, RF and SVM classifiers performed comparatively similarly as demonstrated by the results of McNemer’s test (Z = 1.15). An evaluation of different RapidEye bands using the two classifiers showed that incorporation of the red-edge band has a significant effect on the overall classification accuracy in vegetation cover types. Consequently, pursuit of high classification accuracy using high-spatial resolution imagery on complex landscapes remains paramount.

Exploiting machine learning algorithms for tree species classification in a semiarid woodland using RapidEye image

Abstract Classification of different tree species in semiarid areas can be challenging as a result of the change in leaf structure and orientation due to soil moisture constraints. Tree species mapping is, however, a key parameter for forest management in semiarid environments. In this study, we examined the suitability of 5-band RapidEye satellite data for the classification of five tree species in mopane woodland of Botswana using machine leaning algorithms with limited training samples.We performed classification using random forest (RF) and support vector machines (SVM) based on EnMap box. The overall accuracies for classifying the five tree species was 88.75 and 85% for both SVM and RF, respectively. We also demonstrated that the new red-edge band in the RapidEye sensor has the potential for classifying tree species in semiarid environments when integrated with other standard bands. Similarly, we observed that where there are limited training samples, SVM is preferred over RF. Finally, we demonstrated that the two accuracy measures of quantity and allocation disagreement are simpler and more helpful for the vast majority of remote sensing classification process than the kappa coefficient. Overall, high species classification can be achieved using strategically located RapidEye bands integrated with advanced processing algorithms.

Estimating standing biomass in papyrus Cyperus papyrus L. swamp: exploratory of in situ hyperspectral indices and random forest regression

Accurate estimates of papyrus (Cyperus papyrus) biomass are critical for an efficient papyrus swamp monitoring and management system. The objective of this study was to test the utility of random forest (RF) regression and two narrow-band vegetation indices in estimating above-ground biomass (AGB) for complex and densely vegetated swamp canopies. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were calculated from field spectrometry data and fresh AGB was measured in 82 quadrats at three different areas in the iSimangaliso Wetland Park, South Africa. NDVI was calculated from all possible band combinations of the electromagnetic spectrum (350 and 2500 nm), while EVI was calculated from possible band combinations in the blue, red, and near infrared of the spectrum. Backward feature elimination and RF regression were used as variable selection and modelling techniques to predict papyrus AGB. Results showed that the effective portions of electromagnetic spectrum for estimation AGB of papyrus swamp were located within the blue, red, red-edge, and near-infrared regions. The three best selected EVIs were computed from bands located at (i) 445, 682, and 829 nm, (ii) 497, 676, and 1091 nm, and (iii) 495, 678, and 1120 nm. These indices produced better predictive accuracies (R2 = 0.90; root mean square error of prediction (RMSEP) = 0.289 kg m−2; 7.99% of the mean) than the best selected NDVIs (R2 = 0.85; RMSEP = 0.343 kgm−2; 9.49% of the mean) that were calculated from bands located at (i) 739 and 829 nm, (ii) 739 and 814 nm, (iii) 744 and 789 nm, and (iv) 734 and 909 nm. The results of the present study demonstrate the utility of narrow-band vegetation indices (EVI and NDVI) and RF regression in estimating papyrus AGB at high density, a previously challenging task with broadband satellite sensors.

Spectral Discrimination of Insect Defoliation Levels in Mopane Woodland Using Hyperspectral Data

Mopane woodland are a source of valuable resources that contribute substantially to rural economies and nutrition across Southern Africa. However, a number of factors have, of late, brought the sustainability of the mopane woodland resources into question. One of such factors is the difficulty in monitoring of defoliation process within the woodland. In this study we set out to discriminate the levels of change in forest canopy cover detectable after insect defoliation using ground based hyperspectral measurements in mopane woodland. Canopy spectral measurements were taken from three levels of defoliation: Undefoliated (UD), Partly defoliated (PD) and Refoliating plants (R) using ASD FieldSpec HandHeld 2. A pre-filtering approach (ANOVA) was compared with random forest independent variable selector in selecting the significant wavelengths for classification. Furthermore, a backward feature elimination method was used to select optimal wavelengths for discriminating the different levels of defoliation in mopane woodland. Results show that optimal wavelengths located at 707 nm, 710 nm, 711 nm, 712 nm, 713 nm, 714 nm, 727 nm, and 1066 nm were able to discriminate between the three levels of defoliation. The results further show that there was no significant difference in the overall accuracy of classification when random forest variable selector was used 82.42% (Kappa = 0.64) and the pre-filtering approach (ANOVA) 81.21% (Kappa = 0.68) used before building the classification. Overall, the study clearly demonstrated that the dynamic process of defoliation in mopane woodland can be assessed and detected using hyperspectral dataset and effective algorithm for discrimination.

Performance of Support Vector Machines and Artificial Neural Network for Mapping Endangered Tree Species Using WorldView-2 Data in Dukuduku Forest, South Africa

Endangered tree species (ETS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, and security-based and well-being benefits. The development of techniques for mapping and monitoring ETS is thus critical for understanding functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to map ETS over fragmenting areas. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vector machines (SVM) and artificial neural network (ANN). However, delineation of ETS in a fragmenting ecosystem using high-resolution imagery has largely remained elusive due to the complexity of the species structure and their distribution. Therefore, the aim of the present study was to examine the utility of the advanced WV-2 data for mapping ETS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. Specifically, the study looked at testing the advent of the additional WV-2 bands in mapping six ETS. WV-2 image was spectrally resized to separate four standard bands (SB) and four additional bands (AB). WV-2 image (8 bands: 8B) together with the SB and AB subsets was classified using SVM and ANN methods. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% for SVM and 75.00% for ANN using 8B. The SB produced OA of 65.00% for SVM and 64.00% for ANN. The AB produced almost the same OA of 70.00% for both SVM and ANN. There were significant differences between the performances of the two algorithms as demonstrated by the results of McNemars test (

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

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Determining the susceptibility of Eucalyptus nitens forests to Coryphodema tristis cossid moth occurrence in Mpumalanga, South Africa

). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to map ETS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of ETS.

Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods

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 ground-based 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 (R2Val = 0.75, RMSEVal = 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.