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Article ; Online: Earth Observation-Based Detectability of the Effects of Land Management Programmes to Counter Land Degradation

Esther Barvels / Rasmus Fensholt

Remote Sensing, Vol 13, Iss 1297, p

A Case Study from the Highlands of the Ethiopian Plateau

2021 Volume 1297

Abstract: In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation ... ...

Abstract	In Ethiopia land degradation through soil erosion is of major concern. Land degradation mainly results from heavy rainfall events and droughts and is associated with a loss of vegetation and a reduction in soil fertility. To counteract land degradation in Ethiopia, initiatives such as the Sustainable Land Management Programme (SLMP) have been implemented. As vegetation condition is a key indicator of land degradation, this study used satellite remote sensing spatiotemporal trend analysis to examine patterns of vegetation between 2002 and 2018 in degraded land areas and studied the associated climate-related and human-induced factors, potentially through interventions of the SLMP. Due to the heterogeneity of the landscapes of the highlands of the Ethiopian Plateau and the small spatial scale at which human-induced changes take place, this study explored the value of using 30 m resolution Landsat data as the basis for time series analysis. The analysis combined Landsat derived Normalised Difference Vegetation Index (NDVI) data with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) derived rainfall estimates and used Theil-Sen regression, Mann-Kendall trend test and LandTrendr to detect changes in NDVI, rainfall and rain-use efficiency. Ordinary Least Squares (OLS) regression analysis was used to relate changes in vegetation directly to SLMP infrastructure. The key findings of the study are a general trend shift from browning between 2002 and 2010 to greening between 2011 and 2018 along with an overall greening trend between 2002 and 2018. Significant improvements in vegetation condition due to human interventions were found only at a small scale, mainly on degraded hillside locations, along streams or in areas affected by gully erosion. Visual inspections (based on Google Earth) and OLS regression results provide evidence that these can partly be attributed to SLMP interventions. Even from the use of detailed Landsat time series analysis, this study underlines the challenge and limitations to ...
Keywords	developing countries ; Google Earth Engine ; land degradation ; Landsat time series analysis ; semi-arid areas ; sustainable land management programmes ; Science ; Q
Subject code	910
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping tree species diversity of temperate forests using multi-temporal Sentinel-1 and -2 imagery

Yanbiao Xi / Wenmin Zhang / Martin Brandt / Qingjiu Tian / Rasmus Fensholt

Science of Remote Sensing, Vol 8, Iss , Pp 100094- (2023)

2023

Abstract: Accurate information on tree species diversity is critical for forest biodiversity, conservation and management, but mapping forest diversity over large and mixed forest areas using satellite remote sensing data remains a challenge because of scale- and ... ...

Abstract	Accurate information on tree species diversity is critical for forest biodiversity, conservation and management, but mapping forest diversity over large and mixed forest areas using satellite remote sensing data remains a challenge because of scale- and ecosystem-dependent relationships between spectral heterogeneity and tree species diversity. In this study, three different diversity indices (Simpson (λ), Shannon (H’), and Pielou (J’)), were tested to characterize forest tree species diversity using individual monthly and multi-temporal Sentinel-1 and -2 images during 2021. The performance of three different machine learning models, Random Forest (RF), Extreme Gradient Boosting (XGB), and Deep Neural Network (DNN) were tested. A collection of 1,020 plot measurements (comprising 47 tree species and 28,122 trees), randomly collected in a mixed broadleaf-conifer forest area in northeast China, was used to train (n = 816) and validate (n = 204) the models. The models dependent on multi-temporal Sentinel-1/2 imagery were found to outperform the models based on individual monthly data, in predicting forest tree species diversity, with average accuracies of 78% for H’, 77% for λ and 77% for J’. The use of DNN performed marginally better than the XGB and RF models, with accuracies of 81% for H’, 80% for λ and 79% for J’, respectively. Finally, a boosted regression model, involving environmental variable predictors and the DNN-based estimated tree species diversity, showed that on average 63 ± 4% of the spatial variations of tree species diversity was explained by environmental variables, including annual temperature (29.30%), followed by soil fertility (27.03%), snow cover (13.63%) and a digital elevation model (12.33%). Our results highlight that an empirical approach based on machine learning and multi-temporal Sentinel-1/2 data can accurately predict forest tree species diversity and we further show the important roles of air temperature and soil fertility in governing the spatial variability of tree species ...
Keywords	Forest diversity ; Sentinel-1&2 ; Deep neural network ; Mixed broadleaf-conifer forest ; Physical geography ; GB3-5030 ; Science ; Q
Subject code	333 ; 580
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Towards High-Resolution Land-Cover Classification of Greenland

Daniel Alexander Rudd / Mojtaba Karami / Rasmus Fensholt

Remote Sensing, Vol 13, Iss 3559, p

A Case Study Covering Kobbefjord, Disko and Zackenberg

2021 Volume 3559

Abstract: Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth ... ...

Abstract	Mapping of the Arctic region is increasingly important in light of global warming as land cover maps can provide the foundation for upscaling of ecosystem properties and processes. To this end, satellite images provide an invaluable source of Earth observations to monitor land cover in areas that are otherwise difficult to access. With the continuous development of new satellites, it is important to optimize the existing maps for further monitoring of Arctic ecosystems. This study presents a scalable classification framework, producing novel 10 m resolution land cover maps for Kobbefjord, Disko, and Zackenberg in Greenland. Based on Sentinel-2, a digital elevation model, and Google Earth Engine (GEE), this framework classifies the areas into nine classes. A vegetation land cover classification for 2019 is achieved through a multi-temporal analysis based on 41 layers comprising phenology, spectral indices, and topographical features. Reference data (1164 field observations) were used to train a random forest classifier, achieving a cross-validation accuracy of 91.8%. The red-edge bands of Sentinel-2 data proved to be particularly well suited for mapping the fen vegetation class. The study presents land cover mapping in the three study areas with an unprecedented spatial resolution and can be extended via GEE for further ecological monitoring in Greenland.
Keywords	Sentinel-2 ; google earth engine ; vegetation phenology ; random forest ; red-edge ; Science ; Q
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Satellite Remote Sensing of Savannas

Abdulhakim M. Abdi / Martin Brandt / Christin Abel / Rasmus Fensholt

Journal of Remote Sensing, Vol

Current Status and Emerging Opportunities

2022 Volume 2022

Abstract: Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such ...

Abstract	Savannas cover a wide climatic gradient across large portions of the Earth’s land surface and are an important component of the terrestrial biosphere. Savannas have been undergoing changes that alter the composition and structure of their vegetation such as the encroachment of woody vegetation and increasing land-use intensity. Monitoring the spatial and temporal dynamics of savanna ecosystem structure (e.g., partitioning woody and herbaceous vegetation) and function (e.g., aboveground biomass) is of high importance. Major challenges include misclassification of savannas as forests at the mesic end of their range, disentangling the contribution of woody and herbaceous vegetation to aboveground biomass, and quantifying and mapping fuel loads. Here, we review current (2010–present) research in the application of satellite remote sensing in savannas at regional and global scales. We identify emerging opportunities in satellite remote sensing that can help overcome existing challenges. We provide recommendations on how these opportunities can be leveraged, specifically (1) the development of a conceptual framework that leads to a consistent definition of savannas in remote sensing; (2) improving mapping of savannas to include ecologically relevant information such as soil properties and fire activity; (3) exploiting high-resolution imagery provided by nanosatellites to better understand the role of landscape structure in ecosystem functioning; and (4) using novel approaches from artificial intelligence and machine learning in combination with multisource satellite observations, e.g., multi-/hyperspectral, synthetic aperture radar (SAR), and light detection and ranging (lidar), and data on plant traits to infer potentially new relationships between biotic and abiotic components of savannas that can be either proven or disproven with targeted field experiments.
Keywords	Environmental sciences ; GE1-350 ; Physical geography ; GB3-5030
Subject code	710
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	American Association for the Advancement of Science (AAAS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Tracking Sustainable Restoration in Agro-Pastoral Ecotone of Northwest China

Lixiao Yang / Stéphanie Horion / Chansheng He / Rasmus Fensholt

Remote Sensing, Vol 13, Iss 5031, p

2021 Volume 5031

Abstract: Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of ... ...

Abstract	Large-scale ecological restoration (ER) projects have been implemented in northwest China in recent decades as a means to prevent desertification and improve ecosystem services. However, previous studies have demonstrated adverse impacts in the form of widespread soil water deficit caused by intensive ER activities. Understanding the role of climate change and ER efforts in vegetation dynamics and soil moisture consumption is essential for sustainable ecosystem management. Here, we used the break for additive season and trend (BFAST) method to analyse spatial patterns in the normalized difference vegetation index (NDVI) variation over the agro-pastoral ecotone of northwest China (APENC) for 2000–2015. From the combined use of generalized additive modelling (GAM) and residual-trend analysis (RESTREND), we distinguished and quantified the effects of climate and human management on vegetation and soil water dynamics. Approximately 78% of the area showed vegetation variations representing a significant change in NDVI, of which more than 68% were categorized as abrupt changes. Large areas of the abrupt change type, interrupted increase and monotonic increase in NDVI were observed before 2006, and small areas of the change type of negative reversals were observed after 2012. Anthropogenic activity was found to be the major driving factor of variation in vegetation (contribution rate of 56%) and soil moisture (contribution rate of 78%). The vegetation expansion, which was mainly related to the large number of ER programs that started in 2000, was found to increase soil moisture depletion. By comparing areas where anthropogenic activities had a high contribution rate to vegetation increase and areas where soil moisture consumption was severely increased, we identify and discuss hotspot areas of soil moisture consumption caused by the ER programs. The current methodological workflow and results represent a novel foundation to inform and support water resource management and ecological-restoration-related policy making.
Keywords	vegetation dynamics ; break for additive season and trend (BFAST) ; soil water ; ecological restoration ; temperature vegetation dryness index ; generalized additive model ; Science ; Q
Subject code	550 ; 333
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data

Kristian Skau Bjerreskov / Thomas Nord-Larsen / Rasmus Fensholt

Remote Sensing, Vol 13, Iss 5, p

2021 Volume 950

Abstract: Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and ... ...

Abstract	Mapping forest extent and forest cover classification are important for the assessment of forest resources in socio-economic as well as ecological terms. Novel developments in the availability of remotely sensed data, computational resources, and advances in areas of statistical learning have enabled the fusion of multi-sensor data, often yielding superior classification results. Most former studies of nemoral forests fusing multi-sensor and multi-temporal data have been limited in spatial extent and typically to a simple classification of landscapes into major land cover classes. We hypothesize that multi-temporal, multi-sensor data will have a specific strength in the further classification of nemoral forest landscapes owing to the distinct seasonal patterns in the phenology of broadleaves. This study aimed to classify the Danish landscape into forest/non-forest and further into forest types (broadleaved/coniferous) and species groups, using a cloud-based approach based on multi-temporal Sentinel 1 and 2 data and a random forest classifier trained with National Forest Inventory (NFI) data. Mapping of non-forest and forest resulted in producer accuracies of 99% and 90%, respectively. The mapping of forest types (broadleaf and conifer) within the forested area resulted in producer accuracies of 95% for conifer and 96% for broadleaf forest. Tree species groups were classified with producer accuracies ranging 34–74%. Species groups with coniferous species were the least confused, whereas the broadleaf groups, especially Quercus species, had higher error rates. The results are applied in Danish national accounting of greenhouse gas emissions from forests, resource assessment, and assessment of forest biodiversity potentials.
Keywords	forest resources ; forest and tree species distribution ; machine learning ; multi-sensor data fusion ; National Forest Inventory data ; Science ; Q
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping the Abundance of Multipurpose Agroforestry Faidherbia albida Trees in Senegal

Tingting Lu / Martin Brandt / Xiaoye Tong / Pierre Hiernaux / Louise Leroux / Babacar Ndao / Rasmus Fensholt

Remote Sensing, Vol 14, Iss 662, p

2022 Volume 662

Abstract: Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia ... ...

Abstract	Multi-purpose Faidherbia albida trees represent a vital component of agroforestry parklands in West Africa as they provide resources (fodder for livestock, fruits and firewood) and support water lifting and nutrient recycling for cropping. Faidherbia albida trees are characterized by their inverse phenology, growing leaf flowers and pods during the dry season, thereby providing fodder and shedding leaves during the wet season, which minimizes competition with pastures and crops for resources. Multi-spectral and multi-temporal satellite systems and novel computational methods open new doors for classifying single trees and identifying species. This study used a Multi-Layer Perception feedforward artificial neural network to classify pixels covered by Faidherbia albida canopies from Sentinel-2 time series in Senegal, West Africa. To better discriminate the Faidherbia albida signal from the background, monthly images from vegetation indices were used to form relevant variables for the model. We found that NDI54/NDVI from the period covering onset of leaf senescence (February) until end of senescence (leaf-off in June) to be the most important, resulting in a high precision and recall rate of 0.91 and 0.85. We compared our result with a potential Faidherbia albida occurrence map derived by empirical modelling of the species ecology, which deviates notably from the actual species occurrence mapped by this study. We have shown that even small differences in dry season leaf phenology can be used to distinguish tree species. The Faidherbia albida distribution maps, as provided here, will be key in managing farmlands in drylands, helping to optimize economic and ecological services from both tree and crop products.
Keywords	multi-layer perception ; savanna ; species distribution model ; Science ; Q
Subject code	580
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Beyond tree cover

Qian Li / Yuemin Yue / Siyu Liu / Martin Brandt / Zhengchao Chen / Xiaowei Tong / Kelin Wang / Jingyi Chang / Rasmus Fensholt

Remote Sensing in Ecology and Conservation, Vol 9, Iss 1, Pp 17-

Characterizing southern China's forests using deep learning

2023 Volume 32

Abstract: Abstract Mapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of ...

Abstract	Abstract Mapping forests with satellite images at local to global scale is done on a routine basis, but to go beyond the mapping of forest cover and towards characterizing forests according to their types, species and use, requires a dense time‐series of images. This knowledge is important, because ecological and economic values differ between forests. A new generation of low cost very high spatial resolution satellite images and the advent of deep learning enables improved abilities for distinguishing objects based on their structure, which could potentially also be applied to map different forest classes related to type, species and use. Here we use GF‐1 images at 2 m resolution and map six forest classes including different planted species for the karst region in southwest China, covering 806,900 km2. We compare the results with field data and show that accuracies range between 78% and 90%. We show a dominance of plantations (15%) and secondary forests (70%), and only remnants of natural forests (6%). The possibility to map forest classes based on their crown structure derived from low cost very high‐resolution satellite imagery paves the road towards sustainable forest management and restoration activities, supporting the creation of connected habitats, increasing biodiversity and improved carbon storage. No temporal information is needed for our approach, which saves costs and leads to rapid results that can be updated at a high temporal frequency.
Keywords	Deep learning ; forest types ; karst ; monoculture plantations ; remote sensing ; Technology ; T ; Ecology ; QH540-549.5
Subject code	333
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Temporal Changes in Coupled Vegetation Phenology and Productivity are Biome-Specific in the Northern Hemisphere

Lanhui Wang / Rasmus Fensholt

Remote Sensing, Vol 9, Iss 12, p

2017 Volume 1277

Abstract: Global warming has greatly stimulated vegetation growth through both extending the growing season and promoting photosynthesis in the Northern Hemisphere (NH). Analyzing the combined dynamics of such trends can potentially improve our current ... ...

Abstract	Global warming has greatly stimulated vegetation growth through both extending the growing season and promoting photosynthesis in the Northern Hemisphere (NH). Analyzing the combined dynamics of such trends can potentially improve our current understanding on changes in vegetation functioning and the complex relationship between anthropogenic and climatic drivers. This study aims to analyze the relationships (long-term trends and correlations) of length of vegetation growing season (LOS) and vegetation productivity assessed by the growing season NDVI integral (GSI) in the NH (>30°N) to study any dependency of major biomes that are characterized by different imprint from anthropogenic influence. Spatial patterns of converging/diverging trends in LOS and GSI and temporal changes in the coupling between LOS and GSI are analyzed for major biomes at hemispheric and continental scales from the third generation Global Inventory Monitoring and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset for a 32-year period (1982–2013). A quarter area of the NH is covered by converging trends (consistent significant trends in LOS and GSI), whereas diverging trends (opposing significant trends in LOS and GSI) cover about 6% of the region. Diverging trends are observed mainly in high latitudes and arid/semi-arid areas of non-forest biomes (shrublands, savannas, and grasslands), whereas forest biomes and croplands are primarily characterized by converging trends. The study shows spatially-distinct and biome-specific patterns between the continental land masses of Eurasia (EA) and North America (NA). Finally, areas of high positive correlation between LOS and GSI showed to increase during the period of analysis, with areas of significant positive trends in correlation being more widespread in NA as compared to EA. The temporal changes in the coupled vegetation phenology and productivity suggest complex relationships and interactions that are induced by both ongoing climate change and increasingly ...
Keywords	phenology ; AVHRR GIMMS3g NDVI ; vegetation greenness/productivity ; length of growing season ; growing season integral ; Northern Hemisphere biomes ; Science ; Q
Subject code	910
Language	English
Publishing date	2017-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Mapping the Dynamics of Winter Wheat in the North China Plain from Dense Landsat Time Series (1999 to 2019)

Wenmin Zhang / Martin Brandt / Alexander V. Prishchepov / Zhaofu Li / Chunguang Lyu / Rasmus Fensholt

Remote Sensing, Vol 13, Iss 1170, p

2021 Volume 1170

Abstract: Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, ... ...

Abstract	Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio ( β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 10 6 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces ( p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments ...
Keywords	winter wheat ; Landsat ; time series ; change detection ; cloud computation ; machine learning ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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