A multi-temporal phenology based classification approach for Crop Monitoring in Kenya

The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps

Commercial forest species discrimination and mapping using cost effective multispectral remote sensing in midlands region of KwaZulu-Natal province, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg, 2018.Discriminating forest species is critical for generating accurate and reliable information necessary for sustainable management and monitoring of forests. Remote sensing has recently become a valuable source of information in commercial forest management. Specifically, high spatial resolution sensors have increasingly become popular in forests mapping and management. However, the utility of such sensors is costly and have limited spatial coverage, necessitating investigation of cost effective, timely and readily available new generation sensors characterized by larger swath width useful for regional mapping. Therefore, this study sought to discriminate and map commercial forest species (i.e. E. dunii, E.grandis, E.mix, A.mearnsii, P.taedea and P.tecunumanii, P.elliotte) using cost effective multispectral sensors. The first objective of this study was to evaluate the utility of freely available Landsat 8 Operational Land Imager (OLI) in mapping commercial forest species. Using Partial Least Square Discriminant Analysis algorithm, results showed that Landsat 8 OLI and pan-sharpened version of Landsat 8 OLI image achieved an overall classification accuracy of 79 and 77.8%, respectively, while WorldView-2 used as a benchmark image, obtained 86.5%. Despite low spatial of resolution 30 m, result show that Landsat 8 OLI was reliable in discriminating forest species with reasonable and acceptable accuracy. This freely available imagery provides cheaper and accessible alternative that covers larger swath-width, necessary for regional and local forests assessment and management. The second objective was to examine the effectiveness of Sentinel-1 and 2 for commercial forest species mapping. With the use of Linear Discriminant Analysis, results showed an overall accuracy of 84% when using Sentinel 2 raw image as a standalone data. However, when Sentinel 2 was fused with Sentinel’s 1 Synthetic Aperture Radar (SAR) data, the overall accuracy increased to 88% using Vertical transmit/Horizontal receive (VH) polarization and 87% with Vertical transmit/Vertical receive (VV) polarization datasets. The utility of SAR data demonstrates capability for complementing Sentinel-2 multispectral imagery in forest species mapping and management. Overall, newly generated and readily available sensors demonstrated capability to accurately provide reliable information critical for mapping and monitoring of commercial forest species at local and regional scales

Spruce Budworm Defoliation Detection and Host Species Mapping Using Sentinel Satellite Imagery

Insects are one of the most significant agents causing landscape level disturbances in North American forests, and among them, spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and U.S. The SBW occurrence, its damage extent and severity are highly dependent on characteristics of the forests and availability of the host species (spruce (Picea spp.) and balsam fir (Abies balsamea (L.) Mill.)). This study developed novel methodologies to detect and classify SBW defoliation and to map SBW host species using remote sensing techniques. Optical multispectral remote sensing satellite imagery presents a valuable data source for regional-scale mapping of forest composition as well as defoliation severity and can be effectively used for monitoring insect outbreaks. This study developed two separate models to map both the distribution and abundance of SBW host species as well as the severity of defoliation at 20 m spatial resolution utilizing Sentinel imagery. The two models were integrated to effectively monitor the SBW defoliation. For the detection and severity classification of SBW defoliation, we used Sentinel-2 imagery and site variables (elevation, aspect, and slope) and compared the capabilities of various spectral vegetation indices (SVIs), in particular red-edge SVIs, to detect and classify SBW defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The study was carried out in the Northern part of New Brunswick, Canada. Results showed the superiority of RF in model building for defoliation detection and classification into three classes (non-defoliated, light and moderate) with overall errors of 17% and 32%, respectively. The most important Sentinel-2 based variables for the best model were Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index 7 (EVI7), Normalized Difference Infrared Index 11 (NDII11), Modified Chlorophyll Absorption in Reflectance Index (MCARI), and Modified Simple Ratio (MSR). Elevation was the only site variable significant in the final model. The study concluded that red-edge SVIs were more effective variables for light defoliation detection compared to the traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve the current remote-sensing based SBW defoliation detection techniques. For SBW host species classification, Sentinel-1 synthetic aperture radar (SAR) and multi-spectral Sentinel-2 imagery were used in combination with several site variables (elevation, slope, aspect, topographic wetness index, soil types, projected climate site index for year 2030, and improved Biomass Growth Index (iBGI)). The study was carried out in the same location where the first study was conducted but extended to a larger area (northern parts of New Brunswick, Canada) using a total of 191 variables. We found Sentinel-2 time series in combination with single spectral bands and spectral vegetation indices (SVIs) promising to map SBW host species using a RF algorithm, with an overall accuracy (OA) of 71.34% and kappa coefficient (K) of 0.64. The use of Sentinel-1 SAR data alone with elevation showed a decent result (OA: 57.5 and K: 0.47). Furthermore, the combination of Sentinel-1, Sentinel-2 and elevation provided us with an OA of 72.3% and K of 0.65. The most important Sentinel-2 variables for the best model were from the images of late spring and fall seasons including three single spectral bands and seven SVIs mostly from near-infrared, red-edge and shortwave-infrared regions. Prediction of spatially explicit SBW host species data is essential for identifying vulnerable forests, tracking the SBW defoliation and minimizing the forest loss as well as serving as a vital input for modelling and managing insect impacts at the landscape and regional scales

Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

[EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis, and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications-many times in combination-whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.Part of this work was funded by the Spanish Ministry of Science, innovation and University through the project AGL2016-76769-C2-1-R "Influence of natural disturbance regimes and management on forests dynamics. structure and carbon balance (FORESTCHANGE)".Gómez, C.; Alejandro, P.; Hermosilla, T.; Montes, F.; Pascual, C.; Ruiz Fernández, LÁ.; Álvarez-Taboada, F.... (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems. 28(1):1-33. https://doi.org/10.5424/fs/2019281-14221S133281Ungar S, Pearlman J, Mendenhall J, Reuter D, 2003. Overview of the Earth Observing-1 (EO-1) mission. IEEE T Geosci Remote 41: 1149−1159.Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA, 2011. Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. Remote Sens Environ 115(8): 1942-1956.Valbuena R, Mauro F, Rodríguez-Solano R, Manzanera JA, 2012. Partial Least Squares for Discriminating Variance Components in GNSS Accuracy Obtained Under Scots Pine Canopies. Forest Sci 58(2): 139-153.Valbuena R, De Blas A, Martín Fernández S, Maltamo M, Nabuurs GJ, Manzanera JA, 2013a. Within-Species Benefits of Back-projecting Laser Scanner and Multispectral Sensors in Monospecific P. sylvestris Forests. Eur J Remote Sens 46: 401-416.Valbuena R, Maltamo M, Martín-Fernández S, Packalen P, Pascual C, Nabuurs G-J, 2013b. Patterns of covariance between airborne laser scanning metrics and Lorenz curve descriptors of tree size inequality. Can J Remote Sens 39(1): 18-31.Valbuena R, Packalen P, García-Abril A, Mehtätalo L, Maltamo M, 2013c. Characterizing Forest Structural Types and Shelterwood Dynamics from Lorenz-based Indicators Predicted by Airborne Laser Scanning. Can J For Res 43: 1063-1074.Valbuena R, Maltamo M, Packalen P, 2016a. Classification of Multi-Layered Forest Development Classes from Low-Density National Airborne LiDAR Datasets. Forestry 89: 392-341.Valbuena R, Maltamo M, Packalen P, 2016b. Classification of Forest Development Stages from National Low-Density LiDAR Datasets: a Comparison of Machine Learning Methods. Revista de Teledetección 45: 15-25.Valbuena R, Hernando A, Manzanera JA, Martínez-Falero E, García-Abril A, Mola-Yudego B, 2017a. Most Similar Neighbour Imputation of Forest Attributes Using Metrics Derived from Combined Airborne LIDAR and Multispectral Sensors. Int J Digit Earth 11 (12): 1205-1218.Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DRA, Mauro F, García-Abril A, Coomes DA, 2017b. Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation. Eco Mod 622: 15-26.Valbuena-Rabadán M, Santamaría-Pe-a J, Sanz-Adán F, 2016. Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the National Forest Inventory data. For Sys 25(1): e046Varo-Martínez MA, Navarro-Cerrillo RM, Hernández-Clemente R, Duque-Lazo J, 2017. Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density. Int J Appl Earth Obs 56: 54-64.Vázquez de la Cueva A, 2008. Structural attributes of three forest types in central Spain and Landsat ETM+ information evaluated with redundancy analysis. Int J Remote Sens 29: 5657-5676.Verdú F, Salas J, 2010. Cartografía de áreas quemadas mediante análisis visual de imágenes de satélite en la Espa-a peninsular para el periodo 1991–2005. Geofocus 10: 54–81.Viana-Soto A, Aguado I, Martínez S, 2017. Assessment of post-fire vegetation recovery using fire severity and geographical data in the Mediterranean region (Spain). Environments 4: 90.Vicente-Serrano SG, Pérez-Cabello F, Lasanta T, 2011. Pinus halepensis regeneration after a wildfire in a semiarid environment: assessment using multitemporal Landsat images. Int J Wildland Fire 20Ñ 195-208.Viedma O, Quesada J, Torres I, De Santis A, Moreno JM, 2015. Fire severity in a large fire in a Pinus pinaster forest is highly predictable from burning conditions, stand structure, and topography. Ecosystems 18: 237-250.Yebra M, Chuvieco E, 2009. Generation of a species-specific look-up table for fuel moisture content assessment. IEEE J Selected topics in applied earth observation and RS 2 (1): 21-26.White JC, Wulder MA, Varhola A, Vastaranta M, Coops NC, Cook BD, Pitt D, Woods M, 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC. 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Integrating Landsat pixel composites and change metrics with LiDAR plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sens Environ 176: 188-201.Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P, 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89-99.Zarco-Tejada PJ, Hornero A, Hernández-Clemente R, Beck PSA, 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2A imagery. ISPRS J Photogramm 137: 134-148

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.publishedVersio

Predicting forest cover in distinct ecosystems: the potential of multi-source sentinel-1 and -2 data fusion

The fusion of microwave and optical data sets is expected to provide great potential for the derivation of forest cover around the globe. As Sentinel-1 and Sentinel-2 are now both operating in twin mode, they can provide an unprecedented data source to build dense spatial and temporal high-resolution time series across a variety of wavelengths. This study investigates (i) the ability of the individual sensors and (ii) their joint potential to delineate forest cover for study sites in two highly varied landscapes located in Germany (temperate dense mixed forests) and South Africa (open savanna woody vegetation and forest plantations). We used multi-temporal Sentinel-1 and single time steps of Sentinel-2 data in combination to derive accurate forest/non-forest (FNF) information via machine-learning classifiers. The forest classification accuracies were 90.9% and 93.2% for South Africa and Thuringia, respectively, estimated while using autocorrelation corrected spatial cross-validation (CV) for the fused data set. Sentinel-1 only classifications provided the lowest overall accuracy of 87.5%, while Sentinel-2 based classifications led to higher accuracies of 91.9%. Sentinel-2 short-wave infrared (SWIR) channels, biophysical parameters (Leaf Area Index (LAI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)) and the lower spectrum of the Sentinel-1 synthetic aperture radar (SAR) time series were found to be most distinctive in the detection of forest cover. In contrast to homogenous forests sites, Sentinel-1 time series information improved forest cover predictions in open savanna-like environments with heterogeneous regional features. The presented approach proved to be robust and it displayed the benefit of fusing optical and SAR data at high spatial resolution

An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa

The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019

Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future