Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue

Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue}, author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference}, year={2013}

Modelling flood hazard in dry climates of southern Africa: a case of Beitbridge, Limpopo Basin, Zimbabwe

Floods are among the natural hazards that have adverse effects on human lives, livelihoods, economies and infrastructure. Dry climates of southern Africa have, over the years, experienced an increase in the frequency of tropical cyclone induced floods. However, understanding the key factors that influence susceptibility to floods has remained largely unexplored in these dry climates. Therefore, this study sought to model flood hazards and determine key factors that significantly explain the probability of flood occurrence in the southern parts of Beitbridge District, Zimbabwe. To achieve these objectives, logistic regression was used to predict spatial variations in flood hazards following cyclone Dineo in 2017. Before spatial prediction of flood hazard, environmental variables were tested for multicollinearity using the Pearson correlation coefficient. Only two environmental variables, i.e., elevation and rainfall, were not significantly correlated and were thus used in the subsequent flood hazard modelling. Results demonstrate that two variables significantly(p < 0.05) predicted spatial variations in flood hazard in the southern parts of the Beitbridge District with relatively high accuracy defined by the area under the curve (AUC = 0.98). In addition, results indicate that~56 % of the study area is regarded as highly susceptible to floods. Given the projected increase in extreme events such as intense rainfall as a result of climate change, floods will be expected to correspondingly increase in these semi-arid regions. Results presented in this study underscore the importance of geospatial techniques in flood-hazard modelling, which is the key input in sustainable land-use planning. It can thus be concluded that spatial analytical techniques play a key role in flood early warning systems aimed at supporting and building resilient communities in the face of climate change–induced floods

Trends in elephant poaching in the Mid-Zambezi Valley, Zimbabwe: Lessons learnt and future outlook

Background: The conservation of African elephants (Loxodonta africana) has important ecological, economical, cultural and aesthetic values, at both local and global levels (Pittiglio et al., 2014). Despite the important role elephants play as keystone species, their populations have been dwindling due to human activities (Sibanda et al., 2016). The most serious threats to elephant's survival across most of its range include illegal wildlife trade which has been exacerbated by an increase in organized poaching (Ouko, 2013). Poaching for both meat and ivory is by far the most acute problem across Africa according to data derived from the Monitoring the Illegal Killing of Elephants (MIKE) and Elephant Trade Information System (ETIS; WWF, 2017). This is a complex global threat to the survival of the African elephant across most of its range (Dejene et al., 2021; Ouko, 2013; Wittemyer et al., 2014)

Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane-dominated African savannah dry forest

This study examined the integration of two satellite data sets, that is Landsat 7 ETM+ and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of north-western Zimbabwe. Mopane woodlands cover large spatial extents and provide ecosystem benefits to the rural economies and grazing resources for both livestock and wildlife. In this study, artificial neural networks (ANN) were used to estimate carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS PALSAR. To determine the utility of the two satellite-derived metrics, a two-pronged modelling framework was adopted. Firstly, we used spectral bands and vegetation indices from the two satellite data sets independently, and subsequently, we integrated the metrics from the two satellite data sets into the final model. Results showed that the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded accurate estimations of carbon stocks. Integrating spectral bands and vegetation indices from both sensors significantly improved the estimation of carbon stocks (R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating satellite data in vegetation biophysical assessment and monitoring in poorly documented ecosystems such as the mopane woodlands

Modelling spatial variations in wood volume and forest carbon stocks in dry forests of Southern Africa using remotely sensed data

The estimation of forest carbon is important to generate knowledge on the extent to which forests contribute to climate change mitigation. Several studies on the estimation of forest carbon stocks have mainly focused on tropical rainforests. However, only a few studies have focused on dry forests tropical savanna, yet they constitute about 33% of the terrestrial biomes. Moreover, most work on the estimation of forest carbon stocks has traditionally relied on fieldwork which covers only small spatial extents. Work that has global proportions needs a method of estimating forest carbon stocks that covers large spatial extents. To this end, remote sensing provides an opportunity to estimate dendrometric characteristics of forests and woodlands such as wood volume and forest carbon stocks over large spatial extents. In this thesis, we predicted wood volume and forest carbon stocks as a function of remotely sensed vegetation indices. Specifically, we tested whether high spatial resolution satellite imagery (WorldView-2 and GeoEye-1) improves accuracy in wood volume and forest carbon stocks estimation based on two study sites in dry forests in Zimbabwe with contrasting annual rainfall amounts. Firstly, we compared the predictive ability of vegetation indices (i.e., Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI) and Normalized Difference Vegetation Index (NDVI) derived the high spatial resolution sensors (GeoEye-1 and WorldView-2) for Mukuvisi and Malipati respectively with the indices derived from the medium resolution sensor, i.e., Landsat 5 TM (Thematic Mapper) in predicting wood volume. Secondly, we mapped the spatial variations in wood volume in the two study sites using best predictive model relating wood volume to remotely sensed vegetation indices. Thirdly, we tested whether the inclusion of the red edge band as an explanatory variable to vegetation indices derived from WorldView-2 can improve the estimation of forest carbon stocks in dry forests of Malipati Safari Area. Finally, we mapped the spatial variations in forest carbon stocks in Malipati using best predictive model relating forest carbon stocks to vegetation indices and the red edge band. Our results showed that vegetation indices derived from WorldView-2 and GeoEye-1 significantly (p< 0.05) predicted wood volume better Landsat 5 TM derived vegetation indices Our results also 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% and 12.9%. However, when the reflectance in the red edge band was included 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 produced the best predictor of forest carbon stocks. We concluded that vegetation indices derived from high spatial resolution improves accuracy in estimating wood volume and forest carbon stocks and thus can be successfully used to map forest carbon stocks in dry forests.,Ministère Français des Affaires Etrangères through the French Embassy in Zimbabwe (RP-PCP grant/Project ECO#5), the DAAD (Deutscher Akademischer Austausch Dienst) In-Country Scholarship programme (A/10/02922) and GeoEye Foundatio

Forest leaf mass per area (LMA) through the eye of optical remote sensing: A review and future outlook

Quantitative remote sensing of leaf traits offers an opportunity to track biodiversity changes from space. Augmenting field measurement of leaf traits with remote sensing provides a pathway for monitoring essential biodiversity variables (EBVs) over space and time. Detailed information on key leaf traits such as leaf mass per area (LMA) is critical for understanding ecosystem structure and functioning, and subsequently the provision of ecosystem services. Although studies on remote sensing of LMA and related constituents have been conducted for over three decades, a comprehensive review of remote sensing of LMA—a key driver of leaf and canopy reflectance—has been lacking. This paper reviews the current state and potential approaches, in addition to the challenges associated with LMA estimation/retrieval in forest ecosystems. The physiology and environmental factors that influence the spatial and temporal variation of LMA are presented. The scope of scaling LMA using remote sensing systems at various scales, i.e., near ground (in situ), airborne, and spaceborne platforms is reviewed and discussed. The review explores the advantages and disadvantages of LMA modelling techniques from these platforms. Finally, the research gaps and perspectives for future research are presented. Our review reveals that although progress has been made, scaling LMA to regional and global scales remains a challenge. In addition to seasonal tracking, three-dimensional modeling of LMA is still in its infancy. Over the past decade, the remote sensing scientific community has made efforts to separate LMA constituents in physical modelling at the leaf level. However, upscaling these leaf models to canopy level in forest ecosystems remains untested. We identified future opportunities involving the synergy of multiple sensors, and investigated the utility of hybrid models, particularly at the canopy and landscape levels

Mapping leaf area index in a mixed temperate forest using Fenix airborne hyperspectral data and Gaussian processes regression

Machine learning algorithms, in particular, kernel-based machine learning methods such as Gaussian processes regression (GPR) have shown to be promising alternatives to traditional empirical methods for retrieving vegetation parameters from remotely sensed data. However, the performance of GPR in predicting forest biophysical parameters has hardly been examined using full-spectrum airborne hyperspectral data. The main objective of this study was to evaluate the potential of GPR to estimate forest leaf area index (LAI) using airborne hyperspectral data. To achieve this, field measurements of LAI were collected in the Bavarian Forest National Park (BFNP), Germany, concurrent with the acquisition of the Fenix airborne hyperspectral images (400−2500 nm) in July 2017. The performance of GPR was further compared with three commonly used empirical methods (i.e., narrowband vegetation indices (VIs), partial least square regression (PLSR), and artificial neural network (ANN)). The cross-validated coefficient of determination (Rcv2) and root mean square error (RMSEcv) between the retrieved and field-measured LAI were used to examine the accuracy of the respective methods. Our results showed that using the entire spectral data (400−2500 nm), GPR yielded the most accurate LAI estimation (Rcv2 = 0.67, RMSEcv = 0.53 m2 m−2) compared to the best performing narrowband VIs SAVI2 (Rcv2 = 0.54, RMSEcv = 0.63 m2 m−2), PLSR (Rcv2 = 0.74, RMSEcv = 0.73 m2 m−2) and ANN (Rcv2 = 0.68, RMSEcv = 0.54 m2 m−2). Consequently, when a spectral subset obtained from the analysis of VIs was used as model input, the predictive accuracies were generally improved (GPR RMSEcv = 0.52 m2 m−2; ANN RMSEcv = 0.55 m2 m−2; PLSR RMSEcv = 0.69 m2 m−2), indicating that extracting the most useful information from vast hyperspectral bands is crucial for improving model performance. In general, there was an agreement between measured and estimated LAI using different approaches (p > 0.05). The generated LAI map for BFNP using GPR and the spectral subset endorsed the LAI spatial distribution across the dominant forest classes (e.g., deciduous stands were generally associated with higher LAI values). The accompanying LAI uncertainty map generated by GPR shows that higher uncertainties were observed mainly in the regions with low LAI values (low vegetation cover) and forest areas which were not well represented in the collected sample plots. This study demonstrated the potential of GPR for estimating LAI in forest stands using airborne hyperspectral data. Owing to its capability to generate accurate predictions and associated uncertainty estimates, GPR is evaluated as a promising candidate for operational retrieval applications of vegetation traits

African elephant (Loxodonta africana) select less fragmented landscapes to connect core habitats in human‐dominated landscapes

African elephants (Loxodonta africana) utilise corridors to access limited resources, that is forage and water scattered across heterogeneous habitats they roam. The existence of small elephant metapopulations depend on the intactness of these corridors to access the scarce resources. Due to the sedentarisation of the previously nomadic Maasai people, elephant corridors have been exposed to increased fragmentation from human-induced activities across the Amboseli ecosystem in Kenya. In this study, we sought to compare the scale of fragmentation between corridors and their immediate landscapes (noncorridors) in the Amboseli ecosystem, Kenya. We used a Brownian Bridge Movement Model (BBMM) to identify corridors used by elephants from global positioning system (GPS) collar data. The scale of fragmentation between corridors and noncorridors was determined using the effective mesh size fragmentation metric (m eff). Our results showed that elephant corridors were significantly less fragmented (Wilcoxon sum rank test: W = 6,121.5, p < 0.05) when compared to the noncorridors. The presence of fragmentation geometries in the corridors remains a major cause of concern for wildlife managers as they have the potential to invade and constrict the existing corridors. Our results underscore the need to extend management of elephant habitats to migration corridors outside protected areas

Comparing methods for mapping canopy chlorophyll content in a mixed mountain forest using Sentinel-2 data

The Sentinel-2 Multi-Spectral Imager (MSI) has three spectral bands centered at 705, 740, and 783 nm wavelengths that exploit the red-edge information useful for quantifying plant biochemical traits. This sensor configuration is expected to improve the prediction accuracy of vegetation chlorophyll content. In this work, we assessed the performance of several statistical and physical-based methods in retrieving canopy chlorophyll content (CCC) from Sentinel-2 in a heterogeneous mixed mountain forest. Amongst the algorithms presented in the literature, 13 different vegetation indices (VIs), a non-parametric statistical approach, and two radiative transfer models (RTM) were used to assess the CCC prediction accuracy. A field campaign was conducted in July 2017 to collect in situ measurements of CCC in Bavarian forest national park, and the cloud-free Sentinel-2 image was acquired on 13 July 2017. The leave-one-out cross-validation technique was used to compare the VIs and the non-parametric approach. Whereas physical-based methods were calibrated using simulated data and validated using the in situ reference dataset. The statistical-based approaches, such as the modified simple ratio (mSR) vegetation index and the partial least square regression (PLSR) outperformed all other techniques. As such the modified simple ratio (mSR3) (665, 865) gave the lowest cross-validated RMSE of 0.21 g/m2 (R2 = 0.75). The PLSR resulted in the highest R2 of 0.78, and slightly higher RMSE =0.22 g/m2 than mSR3. The physical-based approach-INFORM inversion using look-up table resulted in an RMSE =0.31 g/m2, and R2 = 0.67. Although mapping CCC using these methods revealed similar spatial distribution patterns, over and underestimation of low and high CCC values were observed mainly in the statistical approaches. Further validation using in situ data from different terrestrial ecosystems is imperative for both the statistical and physical-based approaches' effectiveness to quantify CCC before selecting the best operational algorithm to map CCC from Sentinel-2 for long-term terrestrial ecosystems monitoring across the globe