Brazilian National Forest Inventory: a landscape scale approach to monitoring and assessing forested landscapes.

A importância estratégica dos recursos florestais, tanto em escala nacional quanto global, assim como a falta de informações qualitativas e quantitativas confiáveis acerca das florestas brasileiras, está entre as motivações que levaram à realização de um novo Inventário Florestal Nacional do Brasil (IFN-BR). Além do tradicional levantamento de campo por meio de amostragem por conglomerados, o IFN-BR incorporou um componente geoespacial, as unidades amostrais de paisagem. A partir da análise do uso e cobertura da terra nessas unidades amostrais, são gerados indicadores e índices de paisagem, capazes de apresentar informações a respeito da sua composição, morfologia, padrão de mosaico, similaridade de habitats adjacentes, conectividade, fragmentação e situação das zonas ripárias. No presente trabalho são descritos os indicadores selecionados para avaliar a paisagem de amostras piloto no estado do Paraná, bem como sua forma de cálculo e composição de índices e scores.Artigo de revisão

Mapping natural habitats using remote sensing and Sparse partial least square discriminant analysis

This work presents a novel approach for mapping the spatial distribution of natural habitats in the "Foothills of Larzac" Natura 2000 listed site located in a French Mediterranean Biogeographical Region. Sparse Partial Least Square Discriminant Analysis was used to analyze two RapidEye datasets (June 2009 and July 2010) with the purpose of choosing the most informative spectral, textural and thematic variables that allow discriminating the classes of habitats. The Sparse Partial Least Square Discriminant Analysis selected relevant and stable variables for the discrimination of habitat classes that could be linked to ecological or biophysical characteristics. It also gave insight into the similarities and the differences between habitats classes with comparable physiognomic characteristics. The highest user accuracy was obtained for dry improved grasslands (u=91.97%) followed by riparian ash woods (u= 88.38%). These results are very encouraging given that these two classes were identified in Annex 1 of the EC Habitats Directive as of community interest. Due to limited data input requirements and to its computational efficiency, the approach developed in this paper is a good alternative to other types of variable selection approaches in a supervised classification framework and can be easily transferred to other Natura 2000 sites

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

Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

Riparian and deltaic areas exhibit a high biodiversity and offer a number of ecosystem services but are often degraded by human activities. Dams, for example, alter the hydrologic and sediment regimes of rivers and can negatively affect riparian areas and deltas. In order to sustainably manage these ecosystems, it is, therefore, essential to assess and monitor the impacts of dams. To this end, site-assessments and in-situ measurements have commonly been used in the past, but these can be laborious, resource demanding and time consuming. Here, we investigated the impact of three dams on the riparian forest of the Nestos River Delta in Greece by employing multi-temporal satellite data. We assessed the evolution in the values of eight vegetation indices over 27 years, derived from 14 dates of Landsat data. We also employed a modelling approach, using a machine learning Random Forests model, to investigate potential linkages between the observed changes in the indices and a host of climatic and terrestrial predictor variables. Our results show that low density vegetation (0–25%) is more affected by the construction of the dams due to its proximity to anthropogenic influences and the effects of hydrologic regime alteration. In contrast, higher density vegetation cover (50–75%) appears to be largely unaffected, or even improving, due to its proximity to the river, while vegetation with intermediate coverage (25–49%) exhibits no clear trend in the Landsat-derived indices. The Random Forests model found that the most important parameters for the riparian vegetation (based on the Mean Decrease Gini and the Mean Decrease Accuracy) were the distance to the dams, the sea and the river. Our results suggest that management plans of riparian and deltaic areas need to incorporate and take into consideration new innovative management practices and monitoring studies that employ multi-temporal satellite data archives

Remote Sensing of Riparian Areas and Invasive Species

Riparian areas are critical landscape features situated between terrestrial and aquatic environments, which provide a host of ecosystem functions and services. Although important to the environmental health of an ecosystem, riparian areas have been degraded by anthropogenic disturbances. These routine disturbances have decreased the resiliency of riparian areas and increased their vulnerability to invasive plant species. Invasive plant species are non-native species which cause harm to the ecosystem and thrive in riparian areas due to the access to optimal growing conditions.Remote sensing provides an opportunity to manage riparian habitats at a regional and local level with imagery collected by satellites and unmanned aerial systems (UAS). The aim of this study was two-fold: firstly, to investigate riparian delineation methods using moderate resolution satellite imagery; and secondly, the feasibility of UAS to detect the invasive plant Fallopia japonica (Japanese Knotweed) within the defined areas. I gathered imagery from the Landsat 8 OLI and Sentinel-2 satellites to complete the regional level study and collected UAS imagery at a study site in northern New Hampshire for the local level portion. I obtained a modest overall accuracy from the regional riparian classification of 59% using the Sentinel-2 imagery. The local invasive species classification yielded thematic maps with overall accuracies of up to 70%, which is comparable to other studies with the same focus species. Remote sensing is a valuable tool in the management of riparian habitat and invasive plant species

Synergies for Improving Oil Palm Production and Forest Conservation in Floodplain Landscapes

Lowland tropical forests are increasingly threatened with conversion to oil palm as global demand and high profit drives crop expansion throughout the world’s tropical regions. Yet, landscapes are not homogeneous and regional constraints dictate land suitability for this crop. We conducted a regional study to investigate spatial and economic components of forest conversion to oil palm within a tropical floodplain in the Lower Kinabatangan, Sabah, Malaysian Borneo. The Kinabatangan ecosystem harbours significant biodiversity with globally threatened species but has suffered forest loss and fragmentation. We mapped the oil palm and forested landscapes (using object-based-image analysis, classification and regression tree analysis and on-screen digitising of high-resolution imagery) and undertook economic modelling. Within the study region (520,269 ha), 250,617 ha is cultivated with oil palm with 77% having high Net-Present-Value (NPV) estimates ($413/ha?yr–$637/ha?yr); but 20.5% is under-producing. In fact 6.3% (15,810 ha) of oil palm is commercially redundant (with negative NPV of $-299/ha?yr-$-65/ha?yr) due to palm mortality from flood inundation. These areas would have been important riparian or flooded forest types. Moreover, 30,173 ha of unprotected forest remain and despite its value for connectivity and biodiversity 64% is allocated for future oil palm. However, we estimate that at minimum 54% of these forests are unsuitable for this crop due to inundation events. If conversion to oil palm occurs, we predict a further 16,207 ha will become commercially redundant. This means that over 32,000 ha of forest within the floodplain would have been converted for little or no financial gain yet with significant cost to the ecosystem. Our findings have globally relevant implications for similar floodplain landscapes undergoing forest transformation to agriculture such as oil palm. Understanding landscape level constraints to this crop, and transferring these into policy and practice, may provide conservation and economic opportunities within these seemingly high opportunity cost landscapes

Images from unmanned aircraft systems for surveying aquatic and riparian vegetation

Aquatic and riparian vegetation in lakes, streams, and wetlands has important ecological and regulatory functions and should be monitored to detect ecosystem changes. Field surveys are often tedious and in countries with numerous lakes and streams a nationwide assessment is difficult to achieve. Remote sensing with unmanned aircraft systems (UASs) provides aerial images with high spatial resolution and offers a potential data source for detailed vegetation surveys. The overall objective of this thesis was to evaluate the potential of sub-decimetre resolution true-colour digital images acquired with a UAS for surveying non-submerged (i.e., floating-leaved and emergent) aquatic and riparian vegetation at a high level of thematic detail. At two streams and three lakes in northern Sweden we applied several image analysis methods: Visual interpretation, manual mapping, manual mapping in combination with GPS-based field surveys, and automated object-based image analysis and classification of both 2D images and 3D point data. The UAS-images allowed for high taxonomic resolution, mostly at the species level, with high taxa identification accuracy (>80%) also in mixed-taxa stands. UAS-images in combination with ground-based vegetation surveys allowed for the extrapolation of field sampling results, like biomass measurement, to areas larger than the sampled sites. In automatically produced vegetation maps some fine-scale information detectable with visual interpretation was lost, but time-efficiency increased which is important when larger areas need to be covered. Based on spectral and textural features and height data the automated classification accuracy of non-submerged aquatic vegetation was ~80% for all test sites at the growth-form level and for four out of five test sites at the dominant-taxon level. The results indicate good potential of UAS-images for operative mapping and monitoring of aquatic, riparian, and wetland vegetation. More case studies are needed to fully assess the added value of UAS-technology in terms of invested labour and costs compared to other survey methods. Especially the rapid technical development of multi- and hyperspectral lightweight sensors needs to be taken into account

Comparing ultra-high spatial resolution remote-sensing methods in mapping peatland vegetation

Peer reviewe

Mapping wetlands and potential wetland restoration areas in Black Hawk County, Iowa using object-oriented classification and a GIS-based model

Wetlands are transitional lands between terrestrial and aquatic systems that provide many benefits, including: floodwater retention, non-point pollution treatment, wildlife habitat, and soil-erosion control. Wetlands in Iowa have decreased over 95% in the last 200 years. Therefore, there is a need to map and monitor these resources, as well as to determine potential sites for wetland restoration. In Black Hawk County, wetland maps are outdated, and ground surveys have proved to be too time-consuming and expensive. Traditional pixel-based automated classifiers of remotely-sensed imagery have also proven to be inaccurate in classifying wetlands because of spectral confusion. This study tests multispectral data, hybrid data, hyperspectral data, a seasonal matrix, and a new object-oriented classifier. These are tested against traditional multispectral, pixelbased (ISODATA and Maximum-Likelihood) classifiers both to see if wetland classification accuracies from remotely-sensed imagery can be increased and to produce an updated wetlands map for Black Hawk County. A hyperspectral image of Eddyville, Iowa is tested to evaluate how well wetlands are classified when a hyperspectral image is used with an object-oriented classifier and a hyperspectral pixel-based (Spectral Angle Mapper or SAM) classifier. A GIS-based wetland restoration model is developed to identify potential wetland restoration sites in Black Hawk County. This study shows that the object-oriented classifier is more accurate in identifying wetlands and overall land-cover than pixel-based ones (ISODATA, Maximum-Likelihood, SAM) in both multispectral, hybrid-multispectral, and hyperspectral imagery. The summer/fall seasonal matrix produced unacceptable accuracies. Wetlands in Black Hawk County decreased by 1500 acres (plus or minus an error margin of 375 acres) from 1983 to 2003. The restoration model identified 2,971 acres in Black Hawk County as being highly suitable, 34,307 acres as being moderately suitable, and 121,271 acres as having low suitability for wetland restoration. The results are available at http://gisrl-9.geog.uni.edu/wetland. Limitations of the study include file size when using the object-oriented classifier, image availability for the seasonal matrix, and the number of variables employed in the GIS-based restoration model. The future direction of the study lies in obtaining hyperspectral data for Black Hawk County, more current Landsat multispectral imagery for the seasonal matrix, and testing of more non-parametric classifiers, such as the CART algorithm