A change vector analysis technique for monitoring land cover changes in Copsa Mica, Romania, in the period 1985–2011

Abstract

During the communist regime, Romania's planned economy focused exclusively on production neglecting the environment protection. The lack of less polluting production technologies and of environmental protection measures led to excessive pollution in certain industrialized areas. This is the case of the town of Copsa Mica in Sibiu County, which in 1987 was considered one of the most polluted towns in Europe. The present study assesses the change vector analysis (CVA) technique using a Landsat Thematic Mapper (TM) image time series to monitor land cover changes caused by carbon black and heavy metal pollution. CVA was applied to the tasseled cap greenness (TCG) and tasseled cap brightness (TCB) indices, as well as to the Normalized Difference Vegetation Index (NDVI) and bare soil index (BI). Various maps were generated for the periods 1985-1994, 1994-2003, 2003-2011, and 1985-2011, and threshold values were determined for the detection of land cover change/no change. The change direction and magnitude values were cross-tabulated and classified. The technique was assessed based on the change versus no-change error matrix. The results show that in the area of Copsa Mica, land cover changes occurred because of a considerable decrease in the area affected by carbon black and heavy metal pollution. The CVA technique proved efficient in monitoring the land cover changes caused by pollution and especially by carbon black pollution. Soil pollution by heavy metals is reflected in the bare soil surfaces present in the imagery.

Forests dynamics in the montane–alpine boundary: a comparative study using satellite imagery and climate data

“…We sampled 200 random training areas and classified those as either forest or non-forest, based on visual interpretation. Areas were considered forested if tree cover exceeded 60% and forest cos cos cos Classification accuracy was evaluated through a confusion matrix based on a minimum of 100 ground truth sites for each image, other than the training sites, established through a random sampling strategy based on field recommendations (Congalton & Green 2009, Vorovencii 2014. In order to emphasise the changes in land cover classes over the 1970−2014 period, the classified images were compared by cross-tabulation, which resulted in the change matrix that estimates quantitative change (Fig.…”

Change Vector Analysis, Tasseled Cap, and NDVI-NDMI for Measuring Land Use/Cover Changes Caused by a Sudden Short-Term Severe Drought: 2011 Texas Event

“…In addition, the bare soils index (BI) is also used to measure the density of vegetation as a comparison against completely bare soil, sparse canopies, and dense canopies. Finally, the normalized difference moisture index (NDMI) is a measure that is highly correlated with canopy water content [11,12]. To these indices, we add tasseled cap transformations (TCT) which facilitate direct comparisons across multiple scenes.…”

Change Vector Analysis to Monitor the Changes in Fuzzy Shorelines

“…CVA identifies changes of features which were acquired at different times. In previous studies, CVA was applied to the brightness and greenness indices [28,29], normalized difference vegetation index [25,30], near infrared band and vegetation index [31], wetness and bare soil index [32], and spectral bands and textural images [33,34]. In this study, CVA was applied to the water membership values of shoreline images.…”