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"Pellikka, Petri K. E"
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Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests
Tropical montane forest ecosystems are pivotal for sustaining biodiversity and essential terrestrial ecosystem services, including the provision of high-quality fresh water. Nonetheless, the impact of montane deforestation and climate change on the capacity of forests to deliver ecosystem services is yet to be fully understood. In this study, we offer observational evidence demonstrating the response of air temperature and cloud base height to deforestation in African montane forests over the last two decades. Our findings reveal that approximately 18% (7.4 ± 0.5 million hectares) of Africa’s montane forests were lost between 2003 and 2022. This deforestation has led to a notable increase in maximum air temperature (1.37 ± 0.58 °C) and cloud base height (236 ± 87 metres), surpassing shifts attributed solely to climate change. Our results call for urgent attention to montane deforestation, as it poses serious threats to biodiversity, water supply, and ecosystem services in the tropics.
The authors show that recent deforestation induced more warming and cloud level rise than that caused by climate change, threatening biodiversity and water supply in African montane forests.
Journal Article
Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series
The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.
Journal Article
Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection
Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methods
Journal Article
Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices
Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.
Journal Article
Contribution of Tree Size and Species on Aboveground Biomass across Land Cover Types in the Taita Hills, Southern Kenya
Tropical landscapes comprise a variety of land cover (LC) types with characteristic canopy structure and tree species. Depending on the LC type, large-diameter trees and certain tree species can contribute disproportionately to aboveground biomass (AGB), and these patterns are not described at landscape-level in LC type specific studies. Therefore, we investigated the impact of large trees and tree species on AGB across a range of LC types in Taita Hills, Kenya. Data included 239 field plots from seven LC types: Montane forest, Plantation forest, Mixed forest, Riverine forest, Bushland, Grassland, and Cropland and homestead. Our results show that the contribution of large trees (DBH > 60 cm) on AGB was greatest in Riverine forest, Montane forest and Mixed forest (34–87%). Large trees were also common in Plantation forests and Cropland and homestead. Small trees (DBH < 20 cm) covered less than 10% of the total AGB in all forest types. In Grassland, and Cropland and homestead, smaller DBH classes made a greater contribution. Bushland differed from other classes as large trees were rare. Furthermore, the results show that each LC type had characteristic species with high AGB. In the Montane and Mixed forest, Albizia gummifera contributed 21.1% and 18.3% to AGB, respectively. Eucalyptus spp., exotic species planted in the area, were important in Mixed and Plantation forests. Newtonia hildebrandtii was the most important species in Riverine forests. In Bushland, Acacia mearnsii, species with invasive character, was abundant among trees with DBH < 30 cm. Vachellia tortillis, a common species in savannahs of East Africa, made the largest contribution in Grassland. Finally, in Cropland and homestead, Grevillea robusta was the most important species (>25% of AGB). Our results highlight the importance of conserving large trees and certain species to retain AGB stocks in the landscape. Furthermore, the results demonstrate that exotic tree species, even though invasive, can have large contribution to AGB.
Journal Article
Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data
Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers.
Journal Article
Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model
Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.
Journal Article
Can distribution models help refine inventory-based estimates of conservation priority? A case study in the Eastern Arc forests of Tanzania and Kenya
Data shortages mean that conservation priorities can be highly sensitive to historical patterns of exploration. Here, we investigate the potential of regionally focussed species distribution models to elucidate fine-scale patterns of richness, rarity and endemism. Eastern Arc Mountains, Tanzania and Kenya. Generalized additive models and land cover data are used to estimate the distributions of 452 forest plant taxa (trees, lianas, shrubs and herbs). Presence records from a newly compiled database are regressed against environmental variables in a stepwise multimodel. Estimates of occurrence in forest patches are collated across target groups and analysed alongside inventory-based estimates of conservation priority. Predicted richness is higher than observed richness, with the biggest disparities in regions that have had the least research. North Pare and Nguu in particular are predicted to be more important than the inventory data suggest. Environmental conditions in parts of Nguru could support as many range-restricted and endemic taxa as Uluguru, although realized niches are subject to unknown colonization histories. Concentrations of rare plants are especially high in the Usambaras, a pattern mediated in models by moisture indices, whilst overall richness is better explained by temperature gradients. Tree data dominate the botanical inventory; we find that priorities based on other growth forms might favour the mountains in a different order. Distribution models can provide conservation planning with high-resolution estimates of richness in well-researched areas, and predictive estimates of conservation importance elsewhere. Spatial and taxonomic biases in the data are essential considerations, as is the spatial scale used for models. We caution that predictive estimates are most uncertain for the species of highest conservation concern, and advocate using models and targeted field assessments iteratively to refine our understanding of which areas should be prioritised for conservation.
Journal Article
Determinants of Aboveground Biomass across an Afromontane Landscape Mosaic in Kenya
Afromontane tropical forests maintain high biodiversity and provide valuable ecosystem services, such as carbon sequestration. The spatial distribution of aboveground biomass (AGB) in forest-agriculture landscape mosaics is highly variable and controlled both by physical and human factors. In this study, the objectives were (1) to generate a map of AGB for the Taita Hills, in Kenya, based on field measurements and airborne laser scanning (ALS), and (2) to examine determinants of AGB using geospatial data and statistical modelling. The study area is located in the northernmost part of the Eastern Arc Mountains, with an elevation range of approximately 600–2200 m. The field measurements were carried out in 215 plots in 2013–2015 and ALS flights conducted in 2014–2015. Multiple linear regression was used for predicting AGB at a 30 m × 30 m resolution based on canopy cover and the 25th percentile height derived from ALS returns (R2 = 0.88, RMSE = 52.9 Mg ha−1). Boosted regression trees (BRT) were used for examining the relationship between AGB and explanatory variables at a 250 m × 250 m resolution. According to the results, AGB patterns were controlled mainly by mean annual precipitation (MAP), the distribution of croplands and slope, which explained together 69.8% of the AGB variation. The highest AGB densities have been retained in the semi-natural vegetation in the higher elevations receiving more rainfall and in the steep slope, which is less suitable for agriculture. AGB was also relatively high in the eastern slopes as indicated by the strong interaction between slope and aspect. Furthermore, plantation forests, topographic position and the density of buildings had a minor influence on AGB. The findings demonstrate the utility of ALS-based AGB maps and BRT for describing AGB distributions across Afromontane landscapes, which is important for making sustainable land management decisions in the region.
Journal Article