Integrating field data

Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector

While the savannah elephant (Loxodonta africana) is listed by the International Union for Conservation of Nature (IUCN) as “vulnerable” because of declining abundance in some regions of Africa (Blanc 2008), populations in some protected areas of South Africa are growing rapidly (van Aarde and Jackson 2007). These populations can cause extensive modification of vegetation structure when their density increases (Owen-Smith 1996; Whyte et al. 2003; Guldemond and van Aarde 2007). Management methods such as culling, translocation, and birth control have not reduced density in some cases (van Aarde et al. 1999; Pimm and van Aarde 2001). Providing more space for elephants is one alternative management strategy, yet fundamental to this strategy is a clear understanding of habitat and landscape use by elephants. Harris et al. (2008) combined remotely sensed data with Global Positioning System (GPS) and traditional ethological observations to assess elephant habitat use across three areas that span the ecological gradient of historical elephant distribution. They explored influences on habitat use across arid savannahs (Etosha National Park in Namibia) and woodlands (Tembe Elephant Park in South Africa and Maputo Elephant Reserve in Mozambique). The researchers focused on three main variables—distance to human settlements, distance to water, and vegetation type. The authors used Landsat 7 ETMþ imagery to create vegetation maps for each location, employing supervised classification and maximum likelihood estimation. Across all sites, they recorded the coordinates of patches with different vegetation and of vegetation transitions to develop signatures for the maps. Elephants do not use all vegetation types, and it can be expedient to focus on presence rather than both presence and absence. Accordingly, the researchers used GPS to record the locations of elephants with the aim of identifying important land cover types for vegetation mapping. The authors mapped water locations in the wet and dry seasons using remotely sensed data and mapped human settlements using GPS, aerial surveys, and regional maps. They tracked elephants with radiotelemetry collars that communicated with the ARGOS satellite system, sending location data for most of the elephants over 24 h, and then remaining quiescent for the next 48 h to extend battery life.

Author(s):  
Ned Horning ◽  
Julie A. Robinson ◽  
Eleanor J. Sterling ◽  
Woody Turner ◽  
Sacha Spector

The country of Vietnam has long been recognized as an important region for biodiversity (Sterling et al. 2006). High-profile discoveries in the 1990s of many species new to science including large ones such as the Saola (Pseudoryx nghetinhensis), an 85 kg basal member of the cattle subfamily Bovinae and the first new genus of large land-dwelling mammal described since the okapi (Okapia johnstoni) in 1901, have focused the attention of national and international conservation organizations on Vietnam and surrounding countries in mainland Southeast Asia (Hurley et al. in prep.). Conservation action for these endemic, endangered species relies on a clear understanding of trends in habitat conversion. To track deforestation rates through time in Vietnam, Meyfroidt and Lambin (2008) combined remotely sensed data with landscape metrics such as number of patches, mean patch size, mean proximity index, and total core area index. They tested their analyses across a variety of land cover studies including those using Advanced Very High Resolution Radiometer (AVHRR), Landsat, SPOT, and MODIS data sources. They found that forest cover decreased nationally from the 1980s to the 1990s and then showed an increase between 1990 and 2000, due to plantation forests as well as natural forest regeneration. However, the effects of this forest transition on fragmentation metrics noted above differed across the country. For instance, in some places, such as central Vietnam where forest cover is relatively large and well connected, reforestation led to a decrease in forest fragmentation and secondary forests recovered rapidly. However in others, such as areas in the north where forest fragmentation dates back centuries and forests have therefore long been isolated, reforestation did not seem to have an impact on continued fragmentation and habitat loss. In this chapter we detail the importance of fragmentation and landscape metrics to ecology and conservation, outlining when and where remotely sensed data can help in these analyses. We then discuss a subset of fragmentation metrics and point to some challenges in processing fragmentation data. We provide examples of composition and connectivity metrics illuminated with examples from the remote sensing literature.


2013 ◽  
Vol 27 (1) ◽  
pp. 23
Author(s):  
Bambang Sulistyo ◽  
Totok Gunawan ◽  
H Hartono ◽  
Projo Danoedoro

The research was aimed at studying Percentage of Canopy mapping derived from various vegetation indices of remotely-sensed data int Merawu Catchment. Methodology applied was by analyzing remote sensing data of Landsat 7 ETM+ image to obtain various vegetation indices for correlation analysis with Percentage of Canopy measured directly on the field (PTactual) at 48 locations. These research used 11 (eleven) vegetation indices of remotely-sensed data, namely ARVI, MSAVI, TVI, VIF, NDVI, TSAVI, SAVI, EVI, RVI, DVI and PVI. The analysis resulted models (PTmodel) for Percentage of Canopy mapping. The vegetation indices selected are those having high coefficient of correlation (>=0.80) to PTactual. Percentage of Canopy maps were validated using 39 locations on the field to know their accuracies. Percentage of Canopy map (PTmodel) is said to be accurate when its coefficient of correlation value to PTactual is high (>=0.80). The research result in Merawu Catchment showed that from 11 vegetation indices under studied, there were 6 vegetation indices resulted high accuracy of Percentage of Canopy maps (as shown in the value of coefficient of correlation as >=0.80), i.e. TVI, VIF, NDVI, TSAVI, RVI dan SAVI, while the rest, namely ARVI, PVI, DVI, EVI and MSAVI, have r values of < 0.80.


PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81944 ◽  
Author(s):  
Albertus J. Smit ◽  
Michael Roberts ◽  
Robert J. Anderson ◽  
Francois Dufois ◽  
Sheldon F. J. Dudley ◽  
...  

2014 ◽  
Vol 41 (4) ◽  
pp. 557 ◽  
Author(s):  
Jeff R. Harris ◽  
Juan X. He ◽  
Robert Rainbird ◽  
Pouran Behnia

The Geological Survey of Canada, under the Remote Predictive Mapping project of the Geo-mapping for Energy and Minerals program, Natural Resources Canada, has the mandate to produce up-to-date geoscience maps of Canada’s territory north of latitude 60°. Over the past three decades, the increased availability of space-borne sensors imaging the Earth’s surface using increasingly higher spatial and spectral resolutions has allowed geologic remote sensing to evolve from being primarily a qualitative discipline to a quantitative discipline based on the computer analysis of digital images.    Classification of remotely sensed data is a well-known and common image processing application that has been used since the early 1970s, concomitant with the launch of the first Landsat (ERTS) earth observational satellite. In this study, supervised classification is employed using a new algorithm known as the Robust Classification Method (RCM), as well as a Random Forest (RF) classifier, to a variety of remotely sensed data including Landsat-7, Landsat-8, Spot-5, Aster and airborne magnetic imagery, producing predictions (classifications) of bedrock lithology and Quaternary cover in central Victoria Island, Northwest Territories. The different data types are compared and contrasted to evaluate how well they classify various lithotypes and surficial materials; these evaluations are validated by confusion analysis (confusion matrices) as well as by comparing the generalized classifications with the newly produced geology map of the study area. In addition, three new Multiple Classification System (MCS) methods are proposed that leverage the best characteristics of all remotely sensed data used for classification.     Both RCM (using the maximum likelihood classification algorithm, or MLC) and RF provide good classification results; however, RF provides the highest classification accuracy because it uses all 43 of the raw and derived bands from all remotely sensed data. The MCS classifications, based on the generalized training dataset, show the best agreement with the new geology map for the study area.SOMMAIREDans le cadre de son projet de Télécartographie prédictive du Programme de géocartographie de l’énergie et des minéraux de Ressources naturelles Canada, la Commission géologique du Canada a le mandat de produire des cartes géoscientifiques à jour du territoire du Canada au nord de la latitude 60°. Au cours des trois dernières décennies, le nombre croissant des détecteurs aérospatiaux aux résolutions spatiales et spectrales de plus en plus élevées a fait passer la télédétection géologique d’une discipline principalement qualitative à une discipline quantitative basée sur l'analyse informatique d’images numériques.     La classification des données de télédétection est une application commune et bien connue de traitement d'image qui est utilisée depuis le début des années 1970, parallèlement au lancement de Landsat (ERST) le premier satellite d'observation de la Terre. Dans le cas présent, nous avons employé une méthode de classification dirigée en ayant recours à un nouvel algorithme appelé Méthode de classification robuste (MRC), ainsi qu’au classificateur Random Forest (RF), appliqués à une variété de données de télédétection dont celles de Landsat-7, Landsat-8, Spot-5, Aster et d’imagerie magnétique aéroportée, pour produire des classifications prédictives de la lithologie du substratum rocheux et de la couverture Quaternaire du centre de l'île Victoria, dans les Territoires du Nord-Ouest. Les différents types de données sont comparés et contrastés pour évaluer dans quelle mesure ils classent les divers lithotypes et matériaux de surface; ces évaluations sont validés par analyse de matrices de confusion et par comparaison des classifications généralisées des nouvelles cartes géologiques de la zone d'étude. En outre, trois nouvelles  méthodes par système de classification multiple (MCS) sont proposées qui permettent d’exploiter les meilleures caractéristiques de toutes les données de télédétection utilisées pour la classification.     Tant la méthode MRC (utilisant l'algorithme de classification de vraisemblance maximale ou MLC que la méthode RF donne de bons résultats de classification; toutefois c’est la méthode RF qui offre la précision de classification la plus élevée car elle utilise toutes les 43 les bandes de données brutes et dérivées de toutes les données de télédétection. Les classifications MCS, basées sur le jeu de données généralisées d’apprentissage, montrent le meilleur accord avec la nouvelle carte géologique de la zone d'étude.


Author(s):  
M. R. Mohd Salleh ◽  
N. H. A. Norhairi ◽  
Z. Ismail ◽  
M. Z. Abd Rahman ◽  
M. F. Abdul Khanan ◽  
...  

Abstract. This paper introduced a novel method of landslide activity mapping using vegetation anomalies indicators (VAIs) obtained from high resolution remotely sensed data. The study area was located in a tectonically active area of Kundasang, Sabah, Malaysia. High resolution remotely sensed data were used to assist manual landslide inventory process and production on VAIs. The inventory process identified 33, 139, and 31 of active, dormant, and relict landslides, respectively. Landslide inventory map were randomly divided into two groups for training (70%) and validation (30%) datasets. Overall, 7 group of VAIs were derived including (i) tree height irregularities; (ii) tree canopy gap; (iii) density of different layer of vegetation; (iv) vegetation type distribution; (v) vegetation indices (VIs); (vi) root strength index (RSI); and (vii) distribution of water-loving trees. The VAIs were used as the feature layer input of the classification process with landslide activity as the target results. The landslide activity of the study area was classified using support vector machine (SVM) approach. SVM parameter optimization was applied by using Grid Search (GS) and Genetic Algorithm (GA) techniques. The results showed that the overall accuracy of the validation dataset is between 61.4–86%, and kappa is between 0.335–0.769 for deep-seated translational landslide. SVM RBF-GS with 0.5m spatial resolution produced highest overall accuracy and kappa values. Also, the overall accuracy of the validation dataset for shallow translational is between 49.8–71.3%, and kappa is between 0.243–0.563 where SVM RBF-GS with 0.5m resolution recorded the best result. In conclusion, this study provides a novel framework in utilizing high resolution remote sensing to support labour intensive process of landslide inventory. The nature-based vegetation anomalies indicators have been proved to be reliable for landslide activity identification in Malaysia.


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