ETD Collection

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    The remote sensing of forest canopy gaps in a selectively logged submontane tropical forest reserve in Kenya
    (2022) Jackson, Colbert Mutiso
    Forests constitute 31% (about 4 billion ha) of the land area of the earth, and tropical forests cover is about 2 billion ha. Tropical forests play a significant role in supporting the earth's life and natural ecosystems. But many conservation and protection efforts have not been effective, as they are being cleared in many countries for timber and expansion of agricultural land. Few undisturbed tropical forests remain today, and unsustainable selective logging (SL) is probably the single biggest factor contributing to the global degradation of tropical forests. The amount of forest degradation that is not detected using currently available remote sensing (RS) techniques is unknown. Many methods used to map SL in tropical forests using low/medium spatial resolution datasets have a high rate of false detections. As a result, reliable and operational methods for monitoring SL in tropical forests ought to be utilized. Recently, very high resolution (VHR) RS datasets have caught the interest of researchers studying SL in tropical forests. Therefore, this study was aimed to apply spectral-texture analysis approach to detect canopy gaps caused by illegal logging of Ocotea usambarensis (East African camphor) in Mt. Kenya Forest Reserve (MKFR) in Chuka, Tharaka Nithi County, Kenya using the VHR WorldView-3 (WV-3) satellite data. Several features were derived from the WV-3 data— however, a large number of features results to longer computing time, and might result in reduced classification accuracy. Therefore, feature ranking measures—the mean decrease accuracy, the mean decrease Gini, and the pairwise feature correlation were used. The JeffriesMatusita distance, the transformed divergence index, the G-statistic, and the Euclidean distance were used to calculate the separability of the forest landscape classes. First, the study reviewed and discussed RS techniques used to map SL in the tropical forests. Second, the threatened trees species (TS) in the selectively logged MKFR were mapped. Third, gaps in the forest canopy were detected and quantified using two approaches—initially, only spectral features were used to detect gaps in the forest canopy. A total of 55 spectral features were extracted from the WV-3 dataset—23 means (of 15 vegetation indices–VIs and 8 visible-near-infrared– VNIR bands), and 23 standard deviations–SDs (of 15 VIs and 8 VNIR bands). Also extracted were 8 ratios (of 8 VNIR bands), and 1 brightness feature (average of the means of bands 1 to 8). The study also explored the potential of rich textural features combined with color to model canopy gaps using GLCM-, LBP-, and MLBP-based rotation-invariant feature descriptors derived from WV-3 imagery. Due to their excellent performance and clear logic, two advanced machine learning (ML) classification models—the random forest (RF) and support vector machine (SVM) models were used to identify and classify canopy gaps in the spectral and texture domains of the WV-3 data. During the training process the learning parameters of RF (mtry and ntree) and SVM (γ and C) algorithms were optimised to obtain the best possible settings. Finally, the study reviewed Kenya’s forest policy and law on participatory forest management (PFM). The best tree species classification results reported F1-scores of 68.56 ± 2.6% and 64.64 ± 3.4% for RF and SVM, respectively. The RF and SVM models used to map canopy gaps using the spectral features reported average overall accuracies (OAs) of 92.3 ± 2.6% and 94.0 ± 2.1%, respectively. Average kappa coefficients (ĸ) were 0.88 ± 0.03 for RF and 0.90 ± 0.02 for SVM. The user’s accuracy (UA) and producer’s accuracy (PA) were in the range of 84– 100%. The OA for the classification of canopy gaps using textural/spectral features reported values between 80 (RF, block F’s MLBP/ASM) and 95.1% (SVM, block E’s MLBP/CON). The average OA scores were 84.68 ±3.1, 84.54 ±2.5, 84.86 ±3.0, 86.46 ±3.9, 87 ±4.0, and 85.44 ±3.7 for image blocks A, B, C, D, E, and F, respectively, for the RF classifier, and 85.44 ±3.6, 87.2 ±1.8, 86.3 ±4.3, 89.84 ±2.8, 87.28 ±4.5 and 86.12 ±3.6 for the SVM classifier. The iii UA and PA were in the range of 67-75% and 77-100% for the univariate LBP and MLBP models, respectively. Texture fused with colour resulted to higher classification accuracies. Overall, the approach used in this study demonstrated improved ability of VHR satellite data and ML classification models to accurately map fine canopy gaps resulting from SL. Knowledge about where canopy gaps are located and how they are distributed is critical in accurate estimation of carbon densities of forests, and also for managing the proliferation of invasive species, among other applications. LiDAR datasets acquire the three-dimensional (3- D) structure of forest vegetation—repeat surveys can thus detect the removal of individual trees. The integration of optical images and LiDAR data may boost canopy gap classification.