Environmental　Sensor　Networks　(ESNs)　in　forests　facilitate　the　study　of　fundamental　processes,　and　the　development　of　wireless　communication　makes　ESNs　into　＇intelligent＇　sensor　network,　named　as　Wireless　Environmental　Sensor　Networks　(WESNs).　However,　data　loss　is　prevalent　in　wireless　transmission,　which　may　result　in　incompletion　of　sensory　datasets.　Thus,　if　we　want　to　achieve　a　satisfactory　accuracy　in　a　WESNs　system,　the　task　of　recovering　data　from　achieved　sensory　datasets　is　unavoidable.　Previous　works　provide　many　approaches　to　solve　the　data　missing　problem.　Compared　with　other　methods,　Compressing　Sensing　(CS)　is　powerful　technique　for　estimating　data,　which　can　utilize　a　small　fraction　of　data　to　reconstruct　the　entire　dataset.　In　real　forests,　because　of　the　complicated　geographic　conditions　and　deployment　of　sensors,　sensory　data　will　largely　loss　during　the　wireless　transmission.　Despite　CS　technique　is　a　better　choice,　it　cannot　be　directly　applied　for　the　data　missing　problem.　In　this　paper,　we　will　present　a　reliable　WESNs　system　and　a　better　approach　based　on　CS　to　reconstruct　sensory　datasets.