No abstract

Abstract:The monitoring of crops is of vital importance for food and environmental security in a global and European context. The main goal of this study was to assess the crop mapping performance provided by the 100 m spatial resolution of PROBA-V compared to coarser resolution data (e.g., PROBA-V at 300 m) for a 2250 km 2 test site in Bulgaria. The focus was on winter and summer crop mapping with three to five classes. For classification, single-and multi-date spectral data were used as well as NDVI time series. Our results demonstrate that crop identification using 100 m PROBA-V data performed significantly better in all experiments compared to the PROBA-V 300 m data. PROBA-V multispectral imagery, acquired in spring (March) was the most appropriate for winter crop identification, while satellite data acquired in summer (July) was superior for summer crop identification. The classification accuracy from PROBA-V 100 m compared to PROBA-V 300 m was improved by 5.8% to 14.8% depending on crop type. Stacked multi-date satellite images with three to four images gave overall classification accuracies of 74%-77% (PROBA-V 100 m data) and 66%-70% (PROBA-V 300 m data) with four OPEN ACCESSRemote Sens. 2015, 7 13844 classes (wheat, rapeseed, maize, and sunflower). This demonstrates that three to four image acquisitions, well distributed over the growing season, capture most of the spectral and temporal variability in our test site. Regarding the PROBA-V NDVI time series, useful results were only obtained if crops were grouped into two broader crop type classes (summer and winter crops). Mapping accuracies decreased significantly when mapping more classes. Again, a positive impact of the increased spatial resolution was noted.Together, the findings demonstrate the positive effect of the 100 m resolution PROBA-V data compared to the 300 m for crop mapping. This has important implications for future data provision and strengthens the arguments for a second generation of this mission originally designed solely as a "gap-filler mission".

Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products.

No abstract

This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 × 10 km 2 , especially when the SVR method was used. For the five dominant classes in the test sites the R 2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used.including the linear mixture model (LMM) [4], artificial neural network (ANN) [9-11], regression tree [5], fuzzy classification [6,12], and support vector machine (SVM) [13].Liu and Wu [11] argued that the non-linear models, especially neural network-based models, outperformed the traditional linear unmixing models. Support for such a conclusion is given in Verbeiren et al.[1] who compared ANN and LMM in an attempt to generate a sub-pixel map from SPOT-VEGETATION 1 km data across Belgium. The authors showed that the ANN approach outperformed LMM and that, for the major classes, the acreage estimates obtained via ANN, when aggregated to the level of the administrative regions, were in good agreement with the true values. Also, a multilayer perceptron (MLP) neural network regression algorithm has been shown to outperform the regression tree algorithm [5]. Atkinson et al. [9] also obtained better results with ANN than with the other tested methods but pointed out that its successful implementation depends on accurate co-registration and the availability of a training data set. Liu et al.[14] compared a linear spectral unmixing model, a supervised fully-fuzzy classification method and a SVM to generate a fraction map and achieved the most accurate fraction result using SVM. Six machine learning methods were compared in a recent study [15] based on multiple criteria, where the authors found that, in general, no method performs best for all evaluation criteria. However, when both time available for preprocessing and the magnitude of the training data set are unconstrained, support vector regression (SVR) and least-squares SVM for regression clearly outperform the other methods.Regarding the satellite imagery widely used for agricultural monitoring, SPOT-VEGETATION (SPOT-VGT) sensors provided one of the longest time series of multispectral reflectance since 1998. The mission was succeeded in 2013 by PROBA-V (PRoject for On-Board Autonomy-Vegetation), a small satellite commissioned by the European Space Agency. The sensor on-board PROBA-V generates products at three different ...

No abstract

Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results.

The paper presents integrated database created using the riverbed of the Kutinska River), and steep unstable as input information ground-based and remote sensing data for technogenic slopes were formed. The third stage begins after studying the changes of the stream network and the development coal mining's termination. During this stage a recultivation of of landslide processes in open coal mining areas. Remote sensing the investigated territory is performed which is still incomplete. methods and GIS technologies make it possible to identify the It is characterized by the development of periodic landslide areas with strongest manifestation of landslide processes and processes generating numerous geoecological problems. determine their spatial parameters. The advantage here is thatThe created database for the investigated processes may be of the analysis is performed on an integrated basis, and not use in landscape planning, for the needs of sustainable regional separately for the particular landscape components. development, or to the local self-governing bodies. The changes of the stream network and landslide processes over a 65-year period (1940-2006) on the territories of the land of Novi Iskur, Sofia Municipality, affected by coal mining were INTRODUCTION investigated. The development of coal mining arouses a number As a result of antropogenization and the opencast of geoecological problems, contributes to the intensification of extraction of coals the nature balance is disturbed in many erosion and landslide processes in the area and has negative b m .impact on the Kutina Pyramids natural landmark located in its regions, affected by minmsg activities. These give rlse to a immediate vicinity.number of ecological problems, which affect human life bothThe thematic database created for the purpose includes directly and indirectly. An integrated GIS is created using as archive panchromatic aerial photographs and high spatial input information ground-based and remote sensing data for resolution satellite images for various years (prior to, during, studying the changes of the stream network and the and after the exploitation period), large-scale topographic maps, development of landslide processes. The created database is data from terrain studies and GPS measurements, photos, aimed at processing and retrieval of information from aerial climatic and geologic data, and other types of information. To photos and satellite images with high spatial resolution. reveal the changes in the stream network and landslide processes Remote sensing methods and GIS technologies make it in the open coal mining areas, remote sensing methods in conjunction with geomorphologic and cartographic methods possibletodentify the areastwith strongest anifestatonrof were used. Computer-aided visual deciphering and landslide processes and determine their spatial parameters. interpretation of the high spatial resolution aerial photographs The advantage here is that the analysis is performed on an and satellite images for the aforementioned ye...