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African savanna elephants (Loxodonta africana) can substantially modify their habitat through their interactions with woody vegetation. Nonetheless, the scale, intensity and characteristics of these relations are not yet fully understood. Consequently, it is unclear how vegetation-megafauna interactions can be disrupted by external factors, such as land management. This study attempted to quantify and characterize structural changes in vegetation caused by elephants, from landscape to tree level scales. We applied multi-scale geospatial tools, including airborne (ALS) and terrestrial laser scanning (TLS), to address the following questions: (1) How do elephants shape landscape level vegetation structure in conservation areas? (2) Are the impacts of elephants evident on individual tree architecture? Our study area was located at the Taita Hills Wildlife Sanctuary in South-eastern Kenya. The occurrence of elephants was estimated using elephant observation records and proximity to elephant tracks. Landscape level structure was assessed using tree density maps calculated based on individually detected treetops from ALS data. Next, TLS measurements of 72 trees were processed using quantitative structural modelling to characterize their architecture. Our results demonstrate a widespread influence of elephants on both landscape and tree level structural characteristics. This influence was strongly mediated by management, as we observed differences in vegetation structure inside and outside conservation areas. Tree density was up to 42% lower (5.84 trees/ha) in conservation areas than in non-conservation areas (10.17 trees/ha). Trees were relatively larger with closer proximity to elephant tracks, while smaller trees were more often observed in areas further away from elephants. At an architectural level, trees closer to elephant tracks had lower ratio between the crown length and the tree height, demonstrating a substantial influence of elephants on the morphological characteristics of trees. Our results highlight the importance of accounting for vegetation fauna interactions when planning conservation areas in African savannahs. • We quantified structural changes in vegetation caused by elephants at multiple scales. • The impacts of elephants were assessed using airborne and terrestrial LiDAR. • We demonstrate the influence of elephants on landscape and individual tree levels. • Tree density was up to 42% lower in conservation areas than in non-conservation areas. • Trees consumed by elephants had reduced crown length-to-height ratio. [ABSTRACT FROM AUTHOR]

With the increasing temporal resolution of medium spatial resolution data, seasonal features are becoming more readily available for land cover characterization. However, in the tropical regions, images can be severely contaminated by clouds during the rainy season and fires during the dry season, with possible effects to seasonal features. In this study, we evaluated the performance of seasonal features based on an annual Landsat time series (LTS) of 35 images for land cover characterization in West Sudanian savanna woodlands. First, the burnt areas were detected and removed. Second, the reflectance seasonality was modelled using a harmonic model, and model parameters were used as inputs for land cover classification and tree crown cover prediction using the random forest algorithm. Furthermore, to study the sensitivity of the approach to the burnt areas, we repeated the analyses without the first step. Our results showed that seasonal features improved classification accuracy significantly from 68.7% and 66.1% to 76.2%, and decreased root mean square error (RMSE) of tree crown cover predictions from 11.7% and 11.4% to 10.4%, in comparison to the dry and rainy season single date images, respectively. The burnt areas biased the seasonal parameters in near-infrared and shortwave infrared bands, and decreased the accuracy of classification and tree crown cover prediction, suggesting that burnt areas should be removed before fitting the harmonic model. We conclude that seasonal features from annual LTS improved land cover characterization performance, and the harmonic model, provided a simple method for computing annual seasonal features with burnt area removal. [ABSTRACT FROM AUTHOR]

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Vegetation plays an important role in the climate system. The extent to which vegetation impacts climate through its structure and function varies across space and time, and it is also affected by land cover changes. In areas with both multiple growing periods and significant land cover changes, such as the Horn of Africa, identifying vegetation influence on land surface temperature (LST) through radiative changes needs further investigation. In this study, we used a 13-year time series (2001−2013) of remotely sensed environmental data to estimate the contribution of radiative mechanism to LST change due to growing season albedo dynamics and land cover conversion. Our results revealed that in taller woody vegetation (forest and savanna), albedo increases during the growing period by up to 0.04 compared with the non-growing period, while it decreases in shorter vegetation (grassland and shrubland) by up to 0.03. The warming impact due to a decrease in albedo during the growing period in shorter vegetation is counteracted by a considerable increase in evapotranspiration, leading to net cooling. Analysis of land cover change impact on albedo showed a regional annual average instantaneous surface radiative forcing of −0.03 ± 0.02 W m−2. The land cover transitions from forest to cropland, and savanna to grassland, displayed the largest mean albedo increase across all seasons, causing an average instantaneous surface radiative forcing of −2.6 W m−2 and − 1.5 W m−2 and a decrease in mean LST of 0.12 K and 0.09 K, all in dry period (December, January, February), respectively. Despite the albedo cooling effect in these conversions, an average net warming of 1.3 K and 0.23 K was observed under the dominant influence of non-radiative mechanisms. These results show that the impact of radiative mechanism was small, highlighting the importance of non-radiative processes in understanding the climatic impacts of land cover changes, as well as in delineating effective mitigation strategies. Highlights • Growing season albedo increased in forest, savanna; decreased in shorter vegetation. • Land cover change caused regional instantaneous radiative forcings of −0.03 W m−2. • Forest and savanna conversions dominated the climate impacts of land cover changes. • Land-cover-change-induced albedo increase caused a cooling of up to −0.12 K. • The albedo cooling effect was dominated by warming from non-radiative processes. [ABSTRACT FROM AUTHOR]