Validation of global forest change detection databases

Validation of global forest change detection databases Abstract The main aim of the thesis is to validate selected databases of changes in forest areas based on the analysis of satellite imagery time series in the Czech Republic. For this purpose we are using databases of M. C. Hansen and P. V. Potapov which are mapping the evolution of forest areas internationally. For the purposes of validation, we have proposed a methodology primarily based on historical ortophotographs from 2000-2012, the same time period which is documented in the validated databases. The results obtained were statistically processed, allowing to assess the accuracy of validated databases. At the end of the thesis, we are discussing the causes of identified inaccuracies and presented with recommendations for future improvements of detection of changes in forest areas. Keywords: validation, forest, land cover, change detection, Hansen, PotapovValidace globálních databází změn lesních ploch Abstrakt Hlavním cílem práce je validace vybraných databází změn lesních ploch založených na analýze časových řad družicových snímků na území České republiky. K tomu byly využity databáze M. C. Hansena a P. V. Potapova, které mapují vývoj lesních ploch v nadnárodním měřítku. Za účelem validace byla navržena metodika, jejíž stěžejní součástí je využití historických ortofotosnímků z období let 2000-2012, které rovněž zachycují ověřované databáze. Získané výsledky byly statisticky zpracovány, což umožnilo zhodnocení přesnosti validovaných databází. V závěru práce jsou diskutovány příčiny zjištěných nepřesností a předkládána doporučení k budoucímu zdokonalení detekce změn lesních ploch. Klíčová slova: validace, les, land cover, change detection, Hansen, PotapovKatedra aplikované geoinformatiky a kartografieDepartment of Applied Geoinformatics and CartographyPřírodovědecká fakultaFaculty of Scienc

Spatiotemporal pattern of global forest change over the past 60 years and the forest transition theory

Forest ecosystems play an indispensable role in addressing various pressing sustainability and social-ecological challenges such as climate change and biodiversity loss. However, global forest loss has been, and still is today, an important issue. Here, based on spatially explicit data, we show that over the past 60 years (1960–2019), the global forest area has declined by 81.7 million ha (i.e. 10% more than the size of the entire Borneo island), with forest loss (437.3 million ha) outweighing forest gain (355.6 million ha). With this forest decline and the population increase (4.68 billion) over the period, the global forest per capita has decreased by over 60%, from 1.4 ha in 1960 to 0.5 ha in 2019. The spatiotemporal pattern of forest change supports the forest transition theory, with forest losses occurring primarily in the lower income countries in the tropics and forest gains in the higher income countries in the extratropics. Furthermore, economic growth has a stronger association with net forest gain than with net forest loss. Our results highlight the need to strengthen the support given to lower income countries, especially in the tropics, to help improve their capacity to minimize or end their forest losses. To help address the displacement of forest losses to the lower income countries in the tropics, higher income nations need to reduce their dependence on imported tropical forest products

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Accuracy and Spatial Pattern Assessment of Forest Cover Change Datasets in Central Kalimantan

The accurate information of forest cover change is important to measure the amount of carbon release and sink. The newly-available remote sensing based products and method such as Daichi Forest/Non-Forest (FNF), Global Forest Change (GFC) datasets and Semi-automatic Claslite systems offers the benefit to derive these information in a quick and simple manner. We measured the accuracy by constructing area-proportion error matrix from 388 random sample points and assessed the consistency analysis by looking at the spatial pattern of deforestation and regrowth from built-up area, roads, and rivers from 2010 – 2015 in Katingan district, Central Kalimantan. Accuracy assessment showed that those 3 datasets indicate low to medium accuracy level in which the highest accuracy was achieved by Claslite who produced 71 % ± 5 % of overall accuracy. The consistency analysis provides a similar spatial pattern of deforestation and regrowth measured from the road, river, and built-up area though their distance sensitivity are different one to another.

Using fractal analysis in modeling the dynamics of forest areas and economic impact assessment:Maramureș County, Romania, as a case study

This study uses fractal analysis to quantify the spatial changes of forest resources caused by an increase of deforested areas. The method introduced contributes to the evaluation of forest resources being under significant pressure from anthropogenic activities. The pressure on the forest resources has been analyzed for Maramureș County, one of the most deforested counties in Romania. In order to evaluate this, the deforested areas were calculated for the period of 2001–2014, by using the Global Forest Change 2000–2014 database. The Fractal Fragmentation Index (FFI) and Fixed Grid 2D Lacunarity (FG2DL) were used to quantify the degree of fragmentation and dispersion of the forested areas, and thereby the extent to which a forest area is affected by deforestation. The process of quantifying the pressure on forested areas included the creation of a database for the period of 2000–2014 containing economic activities (turnover) related to woody recourses, important indicators of forest exploitation. Taken together, the results obtained indicate a dramatic increase in deforested areas (over 19,122 ha in total for the period of analysis), in Maramureș County

Examining the Hansen Global Forest Change (2000 - 2014) dataset within an Australian local government area

Human activities have long changed the state of land cover on the surface of the earth. However, since the Industrial Revolution that rate of change has reached such proportions that the very biogeochemical systems that sustain the biosphere of the planet have been impacted. Forests are an essential component in the biogeochemical processes that maintain a balanced geosphere. This project provides a GIS based spatial analysis of forest cover and forest loss in the region covered by the Sunshine Coast Council. The analysis was performed against various datasets which were relevant to forest cover. The Hansen global forest change dataset was utilized as it provides a time-series analysis of high (30m) spatial resolution Landsat images aimed at capturing the global forest extent and change from the years 2000 to 2014. The aim was to test the hypothesis that 'Forests are lost when land use is converted to another use' and to reveal which land use changes contribute to forest loss. An analysis over remnant vegetation areas was also performed in an attempt to give an indication of the effectiveness of conservation efforts. The ArcGIS tabulate area tool was used to analyse the areas of the Hansen dataset against a set of 'zones' defined by the datasets of interest. The analysis has provided key insights into land use change within the study area. In particular, 95% of forest gain was outside of land use change areas. 21% of land use change areas, with change Nature Conservation removed, experienced forest loss. Only 3% forest loss was experienced in areas with no change, giving strength to the hypothesis outlined above. The study confirmed that the Hansen dataset is successful at performing land use analysis at the local government scale, though it does not discriminate types of forest loss i.e. plantation to virgin forest. Errors were encountered in the data affecting the ability to successfully quantify the effectiveness of vegetation management strategies in the study area

A global view of shifting cultivation: Recent, current, and future extent

Mosaic landscapes under shifting cultivation, with their dynamic mix of managed and natural land covers, often fall through the cracks in remote sensing–based land cover and land use classifications, as these are unable to adequately capture such landscapes’ dynamic nature and complex spectral and spatial signatures. But information about such landscapes is urgently needed to improve the outcomes of global earth system modelling and large-scale carbon and greenhouse gas accounting. This study combines existing global Landsat-based deforestation data covering the years 2000 to 2014 with very high-resolution satellite imagery to visually detect the specific spatio-temporal pattern of shifting cultivation at a one-degree cell resolution worldwide. The accuracy levels of our classification were high with an overall accuracy above 87%. We estimate the current global extent of shifting cultivation and compare it to other current global mapping endeavors as well as results of literature searches. Based on an expert survey, we make a first attempt at estimating past trends as well as possible future trends in the global distribution of shifting cultivation until the end of the 21st century. With 62% of the investigated one-degree cells in the humid and sub-humid tropics currently showing signs of shifting cultivation—the majority in the Americas (41%) and Africa (37%)—this form of cultivation remains widespread, and it would be wrong to speak of its general global demise in the last decades. We estimate that shifting cultivation landscapes currently cover roughly 280 million hectares worldwide, including both cultivated fields and fallows. While only an approximation, this estimate is clearly smaller than the areas mentioned in the literature which range up to 1,000 million hectares. Based on our expert survey and historical trends we estimate a possible strong decrease in shifting cultivation over the next decades, raising issues of livelihood security and resilience among people currently depending on shifting cultivation

Recent Deforestation Pattern Changes (2000-2017) in the Central Carpathians:A Gray-Level Co-Occurrence Matrix and Fractal Analysis Approach

The paper explores the distribution of tree cover and deforested areas in the Central Carpathians in the central-east part of Romania, in the context of the anthropogenic forest disturbances and sustainable forest management. The study aims to evaluate the spatiotemporal changes in deforested areas due to human pressure in the Carpathian Mountains, a sensitive biodiverse European ecosystem. We used an analysis of satellite imagery with Landsat-7 Enhanced Thematic Mapper Plus (Landsat-7 ETM+) from the University of Maryland (UMD) Global Forest Change (GFC) dataset. The workflow started with the determination of tree cover and deforested areas from 2000–2017, with an overall accuracy of 97%. For the monitoring of forest dynamics, a Gray-Level Co-occurrence Matrix analysis (Entropy) and fractal analysis (Fractal Fragmentation-Compaction Index and Tug-of-War Lacunarity) were utilized. The increased fragmentation of tree cover (annually 2000–2017) was demonstrated by the highest values of the Fractal Fragmentation-Compaction Index, a measure of the degree of disorder (Entropy) and heterogeneity (Lacunarity). The principal outcome of the research reveals the dynamics of disturbance of tree cover and deforested areas expressed by the textural and fractal analysis. The results obtained can be used in the future development and adaptation of forestry management policies to ensure sustainable management of exploited forest areas