Assessing the effect of band selection on accuracy of pansharpened imagery: application to young woody vegetation mapping

Expansion of woody vegetation has adverse effects on ecosystem services, and thus it is desirable to contain the problem at the early developmental stages. This can be aided by using high spatial resolution remotely-sensed data. The study investigated the effect of band selection during pansharpening on the ability to discriminate young woody vegetation from coexisting land cover types. Red-green-blue (RGB) spectral bands (30 m) of Landsat 8 imagery was pansharpened using the panchromatic band (15 m) of the same image to improve spatial resolution. Near-infrared (NIR), shortwave-infrared 1 (SWIR1) and shortwave-infrared 2 (SWIR2), bands were used respectively as the fourth spectral band during pansharpening, resulting in three pansharpened images. Unsupervised classification was performed on each pansharpened image as well as non-pansharpened multispectral image. The overall accuracies of classification derived from the pansharpened image was higher (87% − 89%) than that derived from the non-pansharpened multispectral image (83%). The study shows that band selection did not affect the classification accuracy of woody vegetation significantly. In addition, the study shows the potential of pansharpened Landsat data in detecting woody vegetation encroachment at the early growth stage.Keywords: Young woody vegetation, Landsat, pansharpening, unsupervised classificatio

Spatio-temporal analyses of woody vegetation cover using remote sensing techniques: the case of Alice - King Williams Town route, Eastern Cape, South Africa

Expansion of woody vegetation results in the transformation of a grass-dominated ecosystem to a tree-dominated ecosystem causing land degradation in most semi-arid areas. The imbalance in the natural ecosystem between herbaceous plants and woody vegetation poses a threat to the natural environment. Such changes alter the flow, availability and quality of nutrient resources in the biogeochemical cycle. Most of the dominating woody plants are often unpalatable to domestic livestock. Therefore, the objective is to assess the spatial extent of woody vegetation over time. Knowledge of the spatial and temporal characteristics of woody vegetation dynamics will enable the development of management plans. These characteristics can be derived using remote sensing techniques which have become efficient in such studies. This study aimed to characterize woody vegetation dynamics along the route between Alice and King Williams’s town in Eastern Cape Province South Africa using Landsat data. This aim was achieved by focussing on three specific objectives. The first objective was to compare the performance of multispectral data and Normalized Difference Vegetation Index (NDVI) data of Landsat imagery in mapping woody vegetation cover. The second objective was to investigate the effect of the spatial resolution of remotely-sensed data on discrimination of woody vegetation from other land cover types. The third objective characterised woody vegetation dynamics between 1986 and 2013/2014 using the results from the first objective. The study used Landsat imagery acquired in November or February of 1986, 1994/1995, 2002/2003 and 2013/2014. Due to lack of data which covered the study area two separate dates (November and February) where used for the study resulting in naming the study area western and eastern parts. Unsupervised classification was performed on the multispectral, NDVI and pan-sharpened images to generate four generic land cover classes, namely water, bare land, grassland and woodland. Accuracy assessments of the classified images was done using error matrix. The results showed that the classification based on NDVI images yielded a better overall accuracy than the classification based on multispectral images for the western (83 percent and 75 percent, respectively) and eastern (82 percent and 76 percent, respectively) parts of the study area. Similarly, pan-sharpening resulted in better overall classification accuracy than multispectral, but comparable to the classification of the NDVI images for both the western (82 percent) and eastern (83 percent) parts of the study area. Remote sensing is an effective tool in assessing changes in the physical environment. Landsat imagery is suitable in assessing land cover dynamics given the long-term and free availability of the image. In addition, the large spatial coverage it provides, enables Landsat data to be used on studies that have wide spatial coverage. Classification for the purpose of time-series analysis was then performed on the NDVI images of each date (1986, 1994/1995, 2002/2003 and 2013/2014). Both woody vegetation and grassland experienced changes from 1986 to 2013/2014 with grassland occupying (75 percent) compared to woodland (17 percent) in 1986. In the year 2013/14 grassland occupied 32 percent and woodland occupied 51 percent of the study area. The increase in woody vegetation in the study area can be attributed to livestock rearing and migration of people from the rural to urban areas post-Apartheid. The study output will aid in the development of a database on land cover distribution of the area between King William’s town and Alice town, providing useful information to decision-making and further studies on woody vegetation

Estimation of woody plant species diversity during a dry season in a savanna environment using the spectral and textural information derived from WorldView-2 imagery

Abstract: Remote sensing techniques are useful in the monitoring of woody plant species diversity in different environments including in savanna vegetation types. However, the performance of satellite imagery in assessing woody plant species diversity in dry seasons has been understudied. This study aimed to assess the performance of multiple Gray Level Co-occurrence Matrices (GLCM) derived from individual bands of WorldView-2 satellite imagery to quantify woody plant species diversity in a savanna environment during the dry season. Woody plant species were counted in 220 plots (20 m radius) and subsequently converted to a continuous scale of the Shannon species diversity index. The index regressed against the GLCMs using the all-possible-subsets regression approach that builds competing models to choose from. Entropy GLCM yielded the best overall accuracy (adjusted R2: 0.41−0.46; Root Mean Square Error (RMSE): 0.60−0.58) in estimating species diversity. The effect of the number of predicting bands on species diversity estimation was also explored. Accuracy generally increased when three–five bands were used in models but stabilised or gradually decreased as more than five bands were used. Despite the peak accuracies achieved with three–five bands, performances still fared well for models that used fewer bands, showing the relevance of few bands for species diversity estimation. We also assessed the effect of GLCM window size (3×3, 5×5 and 7×7) on species diversity estimation and generally found inconsistent conclusions. These findings demonstrate the capability of GLCMs combined with high spatial resolution imagery in estimating woody plants species diversity in a savanna environment during the dry period. It is important to test the performance of species diversity estimation of similar environmental set-ups using widely available moderate-resolution imagery

Survey of community livelihoods and landscape change along the Nzhelele and Levuvhu river catchments in the Limpopo Province, South Africa

Abstract:Landscape change studies have attracted increasing interest because of their importance 29 to land management and sustainable livelihoods of rural communities. However, empirical studies 30 on landscape change and its drivers are often poorly understood, particularly, in small rural 31 communities in developing countries such as South Africa. The present study surveyed local 32 community livelihoods and perceptions of landscape change in the Nzhelele and Levuvhu river 33 catchments in Limpopo Province, South Africa. These areas have experienced land reform and are 34 also characterized by environmental degradation, poverty, inequality and environmental justice 35 concerns among other issues. Land cover maps derived from Landsat satellite imagery were used 36 for purposes of correlating and validating the survey data findings and results. The survey results 37 showed that education levels, working status and marital status have statistically significant effects 38 on community livelihoods (indicated by levels of income, p < 0.05). Maize, fruits and vegetables are 39 the main cultivated crop varieties in the study area, and these crops are mainly used for subsistence 40 to meet household self-consumption requirements.

Remote sensing of woody plant species diversity estimation in a heterogeneous Savanna vegetation ecosystem

Abstract: Accurate assessment of savanna woody plant species diversity using remote sensing technologies can help ecologists by providing timely and cost-effective information for sustainable ecosystem management. With the ever-increasing variety of remotely-sensed data becoming available to the public, there is a need to understand the performances of different sensors (optical and structural) in discriminating multiple woody plant species in the savanna environment with morphologically similar features. The overall aim of the present study was to investigate the utility of remote sensing techniques in estimating woody plant species diversity (n = 30) including three different land cover types identified in a complex and localised savanna environment. This was achieved through the following specific objectives: The first objective was to characterise woody plant species diversity during a dry season or period within in a savanna environment, using textural information derived from WorldView- 2 imagery. The second objective was to compare the utility of three multispectral images (WorldView-2, Sentinel-2A and SPOT-6) in classifying woody plant species during a dry season or period. The third objective focussed on the comparison of the classification of savanna woody plant species between wet and dry seasons using Sentinel-2A and SPOT-6 images. The fourth objective assessed the impact of data fusion between Sentinel-1 C band Radio Detection and Ranging (RADAR) and Sentinel-2A multispectral data on discrimination of savanna woody plant species...Ph.D. (Geography

A bi-seasonal classification of woody plant species using Sentinel-2A and SPOT-6 in a localised species-rich savanna environment

Sustainable management of biodiversity benefit from cost-effective multi-temporal classification schemes afforded by remote sensing techniques. This study compared classification accuracies of woody plant species (n = 27) and three coexisting land cover types using dry and wet seasons data. Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN), were applied to Sentinel-2A and SPOT-6 images. The results showed higher overall classification accuracies for wet season data (65%–72%) for both images and classifiers (DNN, RF and SVM), compared to dry season classification (52%–59%). Near infrared region bands, available in both Sentinel-2A and SPOT-6 imagery, produced high performance for both wet (83%) and dry (80%) seasons. Overall, the findings show potential of multispectral remote-sensing for woody plant species diversity in different seasons. Such a study should be extended to higher frequency species diversity classification, to capture dynamics that may manifest at short time intervals of the year