Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007

Kuemmerle, Tobias; Chaskovskyy, Oleh; Knorn, Jan; Radeloff, Volker C.; Kruhlov, Ivan; Keeton, William S.; Hostert, Patrick

doi:10.1016/j.rse.2009.02.006

Cited by 195 publications

(137 citation statements)

References 55 publications

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“…ETM+ data have outperformed the higher spatial resolution but lower spectral information IKONOS and SPOT-4 High-Resolution Visible and Infrared sensor (HRVIR) in LAI estimation (Soudani et al 2006), with ALS data being reported as alternatives (Zhao and Popescu 2009). Finally, managed forest disturbances, due to logging activities, have been monitored with Landsat time series (Kuemmerle et al 2009), whereas RapidEye VHR imagery has been used in logging trail detection (Franke et al 2012). …”

Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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Section: Forestry Monitoringmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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“…The observed positive trends in forest cover 32% of its area. This is evident in the areas such as mountainous regions in the Carpathian Mountains in the west of Ukraine and in the Crimean Mountains [9].…”

Section: Correlation Analysismentioning

confidence: 93%

“…The third generation GIMMS NDVI is a 15-day Maximum Value Composite that was acquired from seven different NOAA satellites (7,9,11,14,16,17,18), which have been processed using an adaptive Empirical Mode Decomposition. NDVI3g is appropriate for long-term studies of land surface trends in vegetation, seasonality and coupling between climate variability and vegetation over the last three decades [31,32].…”

Section: Normalized Difference Vegetation Indexmentioning

confidence: 99%

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Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

et al. 2016

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Ukraine has experienced immense environmental and institutional changes during the last three decades. We have conducted this study to analyze important land surface dynamics and to assess processes underlying the changes. This research was conducted in two consecutive steps. To analyze monotonic changes we first applied a Mann-Kendall trend analysis of the Normalized Difference Vegetation Index (NDVI3g) time series. Gradual and abrupt changes were studied by fitting a seasonal trend model and detecting the breakpoints. Secondly, essential environmental factors were used to quantify their possible relationships with land surface changes. These factors included soil moisture as well as gridded air temperature and precipitation data. This was done using partial rank correlation analysis based on annually aggregated time-series. Our results demonstrate that positive NDVI trends characterize approximately one-third of Ukraine's land surface, located in the northern and western areas of the country. Negative trends occurred less frequently, covering less than 2% of the area and are distributed irregularly across the country. Monotonic trends were rarely found; shifting trends were identified with a greater frequency. Trend shifts were seen to occur with an increased frequency following the period of the 2000s. We determined that land surface dynamics and climate variability are functionally interdependent; however, the relative influence of the drivers varies in different locations. Among the factors analyzed, the air temperature variable explains the largest portion of NDVI variability. High air temperature/NDVI correlation coefficients (r = 0.36´0.77) are observed over the entire country. The soil moisture content is of significant influence in the eastern portion of Ukraine (r = 0.68); precipitation (r = 0.65) was most influential in the central regions of the country. These results increase our understanding of ecosystem responses to climatic changes and anthropogenic activities.

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“…migration, population growth etc.) (Geri et al, 2010), leading to illegal resource use (Kuemmerle et al, 2009), and expanse bare land (Hostert et al, 2011;Pongratz et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Detection of Land Use Changes in Northeastern Iran by Landsat Satellite Data

Varamesh¹

2017

Appl Ecol Env Res

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Abstract. Land use is a dynamic process referring to human activities and various uses carried out over land. Population growth and elevated socio-economic necessities result in pressure on land use. Land use changes have been investigated in Golestan province located in northeastern Iran during 2000 -2013 at a 30 m spatial resolution using Landsat ETM+ images. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) algorithms were applied to classify land use changes. Land use reference data were used to classify and assess the accuracy of land use classification. Overall accuracy and kappa coefficients were calculated for the maps prepared by SVMs (93.74% and 0.92, respectively) and ANNs approaches (93.08% and 0.91, respectively). In addition, accuracy over 85% is considered satisfactory for land use mapping and planning purposes. The SVM technique also was employed to prepare land use map of year 2000. The areas of each land use type were compared for land use maps of 2000 and 2013. The results obtained from the present study showed that the main land use changes in Golestan province was the conversion of forest and rangeland to agricultural and residential land uses.

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Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007

Cited by 195 publications

References 55 publications

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

Detection of Land Use Changes in Northeastern Iran by Landsat Satellite Data

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