![]() ![]() This was done on a sample area where many of the Caatinga’s vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA’s GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA’s MODIS MCD12Q1. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. Ecological restoration projects such as Grain for Green implemented in 2000 in the upper reaches resulted in the woodland increase. A hypothetical error in 93% of the 2000 data and 58% of the 2010 data can explain deviations from uniform transition given woodland gain during 2000–20–2020. The artificial surfaces gains were active for all three reaches and had strong evidence. ![]() The size of cultivated land decreased during both intervals. The exchange component was larger than the quantity and shift component, and the gross change was 4.1 times larger than the net change. The results showed that at the interval scale, the land transition rate increased from the first to the second period for all of the upper, middle, and lower reaches. The strength of the evidence for the deviation was revealed even though the confusion matrixes of the LULC data at each time point for the YRB were unavailable. ![]() The intensity analysis method with hypothetical errors calculation was used, which could explain the deviations from uniform land changes. In this study, the LULC transitions in the Yellow River Basin (YRB) were analyzed based on the GlobeLand30 land use data in 2000, 2010, and 2020. Land use and land cover (LULC) change influences many issues such as the climate, ecological environment, and economy. Our article applies the principles to characterize both temporal changes and classification errors using land-cover maps from suburban Massachusetts, USA. SHIFT EXCHANGE SOFTWARE HOW TOWe show also how to compute the three components for each category and to reveal the category pairs that account for the largest exchanges. Our article shows how to compute all three components of overall difference: quantity, exchange, and shift. If there are more than two categories, then it is possible to have a component called shift, which is allocation difference that is not exchange. Exchange exists for a pair of pixels when one pixel is classified as category A in the first map and as category B in the second map, while simultaneously the paired pixel is classified as category B in the first map and as category A in the second map. This article shows how to take an additional step to express allocation difference as the sum of two components called exchange and shift. One popular technique is to express the overall difference as the sum of two components called quantity and allocation. A common task is to measure the difference between two maps that show the same spatial extent for the same categorical variable, such as land-cover type. ![]()
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