This work studies the problem of remote sensorng image compression by joint source-channel coding. The vector quantizer methods evaluated are those designed with the LBG algorithm, the COVQ (channel-optimized vector quantizer) algorithm as well as tree-structured vector quantizer. The noisy channel is modelled as a BSC. In this context, two news methods are proposed: (1) A tree- structures vector quantizer that considers the transmission through noisy channels (denominated CD-TSVQ), and (2) a new class of compressors that uses forward error- correcting codes over the TSVQ structure, as a way to actively protect data during the transmission. The twoproposed methods can be combined on the same compressor architecture, resulting in a vast class of compressors well-adapted to the transmission through noisy channels. Results allowing the comparision of the proposed methods with existing ones are presented. Performance evaluated in a scenery where images are compressed to be transmited at a rate of 1.5bpp. Results yield to the conclusion that the porposed methods are much less complex than the existing methods, yet achieve equivalent or, in some situations, improved performance.