Tawanda W. Gara

Estimating wood volume from canopy area in deciduous woodlands of Zimbabwe

In this study we tested the predictive ability of canopy area in estimating wood volume in deciduous woodlands of Zimbabwe. The study was carried out in four sites of different climatic conditions. We used regression analysis to statistically quantify the prediction of wood volume from canopy area at species and stand level using field data. Our results revealed that canopy area significantly (P < 0.05) predicted wood volume at both levels. Furthermore, the results show that at the species-specific level, canopy area explained 54–81% of the variance in wood volume with standard error of estimate (SEE) ranging from 0.056 to 0.71 m3. At stand level, canopy area significantly (P < 0.05) explained 58–84% of the variance in total wood volume with SEE ranging from 0.15 to 3.99 m3 ha−1. Across all study sites, the relationship between canopy area and wood volume at stand level was best described by a logistic regression function, with a R2 value of 0.65 and SEE of 0.7 m3. We concluded that canopy area significantly (P < 0.05) predicted wood volume of dominant tree species in Zimbabwean deciduous woodlands. The relationship between wood volume and canopy area provides an opportunity of estimating wood volume using remote sensing as canopy area can be viewed and measured from aerial, as well as satellite-borne sensors.

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the models accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.

Indigenous forest wood volume estimation in a dry savanna, Zimbabwe: exploring the performance of high-and-medium spatial resolution multispectral sensors

Continuous, accurate estimation and mapping of forest dendrometric characteristics such as wood volume using remote sensing is fundamental for better understanding the role of forests in the carbon cycle and for informed management strategies for forests and woodlands. In this study, we tested whether and to what extent spectral vegetation indices derived from new generation high-spatial resolution multispectral sensors (WorldView-2 and GeoEye-1) estimate indigenous forest wood volume when compared to medium-spatial resolution broadband sensors (Landsat 5 Thematic Mapper) based on two savanna woodland types in Zimbabwe. Subsequently, the best regression model relating wood volume and vegetation indices was applied to map the indigenous forest wood volume of two study sites. Our results showed that spectral vegetation indices derived from new generation multispectral sensors of high resolution yielded plausible indigenous forest wood volume estimates when compared to spectral vegetation indices derived from medium resolution broadband Landsat 5 TM. The findings of our study demonstrated that accurate estimates of indigenous forest wood volume can be derived using vegetation indices derived from high spatial resolution images. Furthermore, these findings emphasise the importance of vegetation indices derived from high resolution satellite images for estimating forest structural attributes, such as wood volume, among others.

Estimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor

Abstract Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2 of 0.67 and a RMSE-CV of 2.2 t ha−1) when compared to when the red-edge band was included as a co-variate (R2 of 0.73 and a RMSE-CV of 2.04 t ha−1). Also, similar results were obseved when all WorldView-2 vegetation indices (without the red-edge computed ones), producing slightly low accuracies (R2 of about 0.67 and a RMSE-CV of 2.20 t ha−1), when compared to those obtained using all indices and RE-computed indices(R2 of 0.76 and a RMSE-CV of 1.88 t ha−1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint.

Elephants move faster in small fragments of low productivity in Amboseli ecosystems: Kenya

Abstract Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R2 < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R2 values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively.

Missing in action: Species competition is a neglected predictor variable in species distribution modelling

The central role of species competition in shaping community structure in ecosystems is well appreciated amongst ecologists. However species competition is a consistently missing variable in Species Distribution Modelling (SDM). This study presents results of our attempt to incorporate species competition in SDMs. We used a suit of predictor variables including Soil Adjusted Vegetation Index (SAVI), as well as distance from roads, settlements and water, fire frequency and distance from the nearest herbivore sighting (of selected herbivores) to model individual habitat preferences of five grazer species (buffalo, warthog, waterbuck, wildebeest and zebra) with the Ensemble SDM algorithm for Gonarezhou National Park, Zimbabwe. Our results showed that distance from the nearest animal sighting (a proxy for competition among grazers) was the best predictor of the potential distribution of buffalo, wildebeest and zebra but the second best predictor for warthog and waterbuck. Our findings provide evidence to that competition is an important predictor of grazer species’ potential distribution. These findings suggest that species distribution modelling that neglects species competition may be inadequate in explaining the potential distribution of species. Therefore our findings encourage the inclusion of competition in SDM as well as potentially igniting discussions that may lead to improving the predictive power of future SDM efforts.

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province, South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.

Impact of Vertical Canopy Position on Leaf Spectral Properties and Traits across Multiple Species

Understanding the vertical pattern of leaf traits across plant canopies provide critical information on plant physiology, ecosystem functioning and structure and vegetation response to climate change. However, the impact of vertical canopy position on leaf spectral properties and subsequently leaf traits across the entire spectrum for multiple species is poorly understood. In this study, we examined the ability of leaf optical properties to track variability in leaf traits across the vertical canopy profile using Partial Least Square Discriminatory Analysis (PLS-DA). Leaf spectral measurements together with leaf traits (nitrogen, carbon, chlorophyll, equivalent water thickness and specific leaf area) were studied at three vertical canopy positions along the plant stem: lower, middle and upper. We observed that foliar nitrogen (N), chlorophyll (Cab), carbon (C), and equivalent water thickness (EWT) were higher in the upper canopy leaves compared with lower shaded leaves, while specific leaf area (SLA) increased from upper to lower canopy leaves. We found that leaf spectral reflectance significantly (P ≤ 0.05) shifted to longer wavelengths in the ‘red edge’ spectrum (685–701 nm) in the order of lower > middle > upper for the pooled dataset. We report that spectral bands that are influential in the discrimination of leaf samples into the three groups of canopy position, based on the PLS-DA variable importance projection (VIP) score, match with wavelength regions of foliar traits observed to vary across the canopy vertical profile. This observation demonstrated that both leaf traits and leaf reflectance co-vary across the vertical canopy profile in multiple species. We conclude that canopy vertical position has a significant impact on leaf spectral properties of an individual plant’s traits, and this finding holds for multiple species. These findings have important implications on field sampling protocols, upscaling leaf traits to canopy level, canopy reflectance modelling, and subsequent leaf trait retrieval, especially for studies that aimed to integrate hyperspectral measurements and LiDAR data.

Characterization of the top sediment layer in coastal intertidal mudflats from medium-to-coarse resolution satellite imagery and field measurements

Intertidal sediments are critically important in controlling intertidal mudflat microphytobenthic primary productivity and the functioning of intertidal ecosystems. This paper demonstrates the possibility of deriving different intertidal sediment properties from coarse-to-medium resolution remote sensing imagery. Supervised and image based classification methods were used to map different substrate types based on the Spectral Angle Mapper (SAM) algorithm. The algorithm characterized different sediment properties from remote sensing data based on field collected and image-extracted endmembers. The results demonstrate that, different substrate types can be derived from coarse-to-medium resolution images using SAM algorithm. Supervised and image-based classification methods performed well in deriving intertidal sediment properties. From the results, sand sediments cover a wide area in extent than clay whereas Normalized Difference Vegetation Index (NDVI) validation results indicate that, clay sediments have higher NDVI values as compared to sand sediments. We conclude that, intertidal sediment properties can be successfully derived from coarse-to-medium resolution satellite imagery. Key words: Endmember, microphytobenthos, spectral signature, substrates, trios ramses, wadden sea.

Estimating forest carbon stocks in tropical dry forests of Zimbabwe: exploring the performance of high and medium spatial-resolution multispectral sensors

Estimation and mapping of forest dendrometric characteristics such as carbon stocks using remote sensing techniques is fundamental for improved understanding of the role of forests in the carbon cycle and climate change. In this study, we tested whether and to what extent spectral transforms, i.e. vegetation indices derived from new generation high-spatial-resolution multispectral sensors (WorldView-2 and GeoEye-1), estimate carbon stocks when compared with medium-spatial-resolution broadband sensors (Landsat 5 Thematic Mapper) based on two savanna woodland types in Zimbabwe. Subsequently, the best ordinary least squares regression model relating carbon stocks and vegetation indices was applied in mapping carbon stocks in two study sites. Based on k-fold cross-validated regression models, vegetation indices computed from new generation high-spatial-resolution multispectral sensors yielded high R 2 values ranging between 82% and 73% (RMSEcv: 5.55–6.87%) for Mukuvisi and 62–73% (RMSEcv: 11.5–13.6) for Malipati compared with Landsat 5 Thematic Mapper derived vegetation indices, which yielded R 2 values between 47% and 49% (RMSEcv: 9.6–10.1%) for Mukuvisi and 22–41% (RMSEcv: 11.5–19.1%) for Malipati. The findings demonstrated that medium-spatial-resolution sensors are less sensitive to attributes of sparsely distributed trees, especially in savanna woodlands, where the size of trees are often less that the spatial resolution of the mediumspatial-resolution sensors. These findings emphasise the importance of new generation high-spatial-resolution multispectral sensors in estimating forest structural attributes, such as carbon stocks in open woodlands.