Applying the change vector analysis technique to assess the desertification risk in the south-west of Romania in the period 1984–2011

Abstract

The desertification risk affects around 40% of the agricultural land in various regions of Romania. The purpose of this study is to analyse the risk of desertification in the south-west of Romania in the period 1984-2011 using the change vector analysis (CVA) technique and Landsat thematic mapper (TM) satellite images. CVA was applied to combinations of normalised difference vegetation index (NDVI)-albedo, NDVI-bare soil index (BI) and tasselled cap greenness (TCG)-tasselled cap brightness (TCB). The combination NDVI-albedo proved to be the best in assessing the desertification risk, with an overall accuracy of 87.67%, identifying a desertification risk on 25.16% of the studied period. The classification of the maps was performed for the following classes: desertification risk, re-growing and persistence. Four degrees of desertification risk and re-growing were used: low, medium, high and extreme. Using the combination NDVI-albedo, 0.53% of the analysed surface was assessed as having an extreme degree of desertification risk, 3.93% a high degree, 8.72% a medium degree and 11.98% a low degree. The driving forces behind the risk of desertification are both anthropogenic and climatic causes. The anthropogenic causes include the destruction of the irrigation system, deforestation, the destruction of the forest shelterbelts, the fragmentation of agricultural land and its inefficient management. Climatic causes refer to increase of temperatures, frequent and prolonged droughts and decline of the amount of precipitation.

Exploring the Impact of Various Spectral Indices on Land Cover Change Detection Using Change Vector Analysis: A Case Study of Crete Island, Greece

“…Thus, NDVI-albedo can be characterized as the most appropriate index combination for detecting the land cover changes in the study area. This finding is in agreement with the results from the research work of Vorovencii [33]. Specifically, by applying CVA for three different index combinations (NDVI-albedo, NDVI-brightness index and TCG-TCB), it was concluded that the most accurate outputs were obtained from the combination of NDVI-albedo.…”

“…The outputs of CVA were evaluated by confusion matrices in terms of accuracy [33,34]. A confusion matrix provides accuracy statistics such as kappa index (with a range from 0 to 1 indicating from very low to excellent accuracy) and overall accuracy, according to the proportion of an area that is correctly matched with reference data [35].…”

RETRACTED ARTICLE: Research on desert water management and desert control

“…Desertification is the focus of environmental issues of concern to the world today, and its hazard ranks first among the top ten disasters in the world (Vorovencii, 2017). The expansion of deserts engulfs productive arable land, resulting in harsh sand and dust climates, leading to poverty and ecological migration (Xue et al, 2017).…”

Development of Mathematical Models for the Estimation of Dendrometric Variables Based on Unmanned Aerial Vehicle Optical Data: A Romanian Case Study

“…Using UASs equipped with small thermal, laser, or optical sensors has emerged as a promising alternative to obtaining 3D models, and is more frequently being used in forestry applications [5]. Data processing techniques makes it possible to use high-resolution images and 3D data in forest monitoring activities [6][7][8][9][10][11], as well as in taking forest inventory and remotely measuring tree variables [1,[12][13][14]. Several studies have focused on estimating the biometric characteristics of either forest stands [15] or individual trees by means of the segmentation of digital models [11,16].…”